Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

Directory of Open Access Journals (Sweden)

German Ignacio Parisi

2015-06-01

Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

Arm Motion Recognition and Exercise Coaching System for Remote Interaction

Directory of Open Access Journals (Sweden)

Hong Zeng

2016-01-01

Full Text Available Arm motion recognition and its related applications have become a promising human computer interaction modal due to the rapid integration of numerical sensors in modern mobile-phones. We implement a mobile-phone-based arm motion recognition and exercise coaching system that can help people carrying mobile-phones to do body exercising anywhere at any time, especially for the persons that have very limited spare time and are constantly traveling across cities. We first design improved k-means algorithm to cluster the collecting 3-axis acceleration and gyroscope data of person actions into basic motions. A learning method based on Hidden Markov Model is then designed to classify and recognize continuous arm motions of both learners and coaches, which also measures the action similarities between the persons. We implement the system on MIUI 2S mobile-phone and evaluate the system performance and its accuracy of recognition.

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer.

Science.gov (United States)

Liu, Maolin; Li, Huaiyu; Wang, Yuan; Li, Fei; Chen, Xiuwan

2018-04-01

Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer's application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions.

Action Recognition in Semi-synthetic Images using Motion Primitives

DEFF Research Database (Denmark)

Fihl, Preben; Holte, Michael Boelstoft; Moeslund, Thomas B.

This technical report describes an action recognition approach based on motion primitives. A few characteristic time instances are found in a sequence containing an action and the action is classified from these instances. The characteristic instances are defined solely on the human motion, hence...... motion primitives. The motion primitives are extracted by double difference images and represented by four features. In each frame the primitive, if any, that best explains the observed data is identified. This leads to a discrete recognition problem since a video sequence will be converted into a string...... containing a sequence of symbols, each representing a primitive. After pruning the string a probabilistic Edit Distance classifier is applied to identify which action best describes the pruned string. The method is evaluated on five one-arm gestures. A test is performed with semi-synthetic input data...

Motion Primitives for Action Recognition

DEFF Research Database (Denmark)

Fihl, Preben; Holte, Michael Boelstoft; Moeslund, Thomas B.

2007-01-01

the actions as a sequence of temporal isolated instances, denoted primitives. These primitives are each defined by four features extracted from motion images. The primitives are recognized in each frame based on a trained classifier resulting in a sequence of primitives. From this sequence we recognize......The number of potential applications has made automatic recognition of human actions a very active research area. Different approaches have been followed based on trajectories through some state space. In this paper we also model an action as a trajectory through a state space, but we represent...... different temporal actions using a probabilistic Edit Distance method. The method is tested on different actions with and without noise and the results show recognition rates of 88.7% and 85.5%, respectively....

Embodied learning of a generative neural model for biological motion perception and inference.

Science.gov (United States)

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

Embodied Learning of a Generative Neural Model for Biological Motion Perception and Inference

Directory of Open Access Journals (Sweden)

Fabian eSchrodt

2015-07-01

Full Text Available Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

Patient cloth with motion recognition sensors based on flexible piezoelectric materials.

Science.gov (United States)

Youngsu Cha; Kihyuk Nam; Doik Kim

2017-07-01

In this paper, we introduce a patient cloth for position monitoring using motion recognition sensors based on flexible piezoelectric materials. The motion recognition sensors are embedded in three parts, which are the knee, hip and back, in the patient cloth. We use polyvinylidene fluoride (PVDF) as the flexible piezoelectric material for the sensors. By using the piezoelectric effect of the PVDF, we detect electrical signals when the cloth is bent or extended. We analyze the sensing values for our human motions by processing the sensor outputs in a custom-made program. Specifically, we focus on the transitions between standing and sitting, and sitting knee extension and supine position, which are important motions for patient monitoring.

Brain correlates of recognition of communicative interactions from biological motion in schizophrenia.

Science.gov (United States)

Okruszek, Ł; Wordecha, M; Jarkiewicz, M; Kossowski, B; Lee, J; Marchewka, A

2017-11-27

Recognition of communicative interactions is a complex social cognitive ability which is associated with a specific neural activity in healthy individuals. However, neural correlates of communicative interaction processing from whole-body motion have not been known in patients with schizophrenia (SCZ). Therefore, the current study aims to examine the neural activity associated with recognition of communicative interactions in SCZ by using displays of the dyadic interactions downgraded to minimalistic point-light presentations. Twenty-six healthy controls (HC) and 25 SCZ were asked to judge whether two agents presented only by point-light displays were communicating or acting independently. Task-related activity and functional connectivity of brain structures were examined with General Linear Model and Generalized Psychophysiological Interaction approach, respectively. HC were significantly more efficient in recognizing each type of action than SCZ. At the neural level, the activity of the right posterior superior temporal sulcus (pSTS) was observed to be higher in HC compared with SCZ for communicative v. individual action processing. Importantly, increased connectivity of the right pSTS with structures associated with mentalizing (left pSTS) and mirroring networks (left frontal areas) was observed in HC, but not in SCZ, during the presentation of social interactions. Under-recruitment of the right pSTS, a structure known to have a pivotal role in social processing, may also be of importance for higher-order social cognitive deficits in SCZ. Furthermore, decreased task-related connectivity of the right pSTS may result in reduced use of additional sources of information (for instance motor resonance signals) during social cognitive processing in schizophrenia.

A triboelectric motion sensor in wearable body sensor network for human activity recognition.

Science.gov (United States)

Hui Huang; Xian Li; Ye Sun

2016-08-01

The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

On Integral Invariants for Effective 3-D Motion Trajectory Matching and Recognition.

Science.gov (United States)

Shao, Zhanpeng; Li, Youfu

2016-02-01

Motion trajectories tracked from the motions of human, robots, and moving objects can provide an important clue for motion analysis, classification, and recognition. This paper defines some new integral invariants for a 3-D motion trajectory. Based on two typical kernel functions, we design two integral invariants, the distance and area integral invariants. The area integral invariants are estimated based on the blurred segment of noisy discrete curve to avoid the computation of high-order derivatives. Such integral invariants for a motion trajectory enjoy some desirable properties, such as computational locality, uniqueness of representation, and noise insensitivity. Moreover, our formulation allows the analysis of motion trajectories at a range of scales by varying the scale of kernel function. The features of motion trajectories can thus be perceived at multiscale levels in a coarse-to-fine manner. Finally, we define a distance function to measure the trajectory similarity to find similar trajectories. Through the experiments, we examine the robustness and effectiveness of the proposed integral invariants and find that they can capture the motion cues in trajectory matching and sign recognition satisfactorily.

Impaired Perception of Biological Motion in Parkinson’s Disease

Science.gov (United States)

Jaywant, Abhishek; Shiffrar, Maggie; Roy, Serge; Cronin-Golomb, Alice

2016-01-01

Objective We examined biological motion perception in Parkinson’s disease (PD). Biological motion perception is related to one’s own motor function and depends on the integrity of brain areas affected in PD, including posterior superior temporal sulcus. If deficits in biological motion perception exist, they may be specific to perceiving natural/fast walking patterns that individuals with PD can no longer perform, and may correlate with disease-related motor dysfunction. Method 26 non-demented individuals with PD and 24 control participants viewed videos of point-light walkers and scrambled versions that served as foils, and indicated whether each video depicted a human walking. Point-light walkers varied by gait type (natural, parkinsonian) and speed (0.5, 1.0, 1.5 m/s). Participants also completed control tasks (object motion, coherent motion perception), a contrast sensitivity assessment, and a walking assessment. Results The PD group demonstrated significantly less sensitivity to biological motion than the control group (pperception (p=.02, Cohen’s d=.68). There was no group difference in coherent motion perception. Although individuals with PD had slower walking speed and shorter stride length than control participants, gait parameters did not correlate with biological motion perception. Contrast sensitivity and coherent motion perception also did not correlate with biological motion perception. Conclusion PD leads to a deficit in perceiving biological motion, which is independent of gait dysfunction and low-level vision changes, and may therefore arise from difficulty perceptually integrating form and motion cues in posterior superior temporal sulcus. PMID:26949927

Micro-motion Recognition of Spatial Cone Target Based on ISAR Image Sequences

Directory of Open Access Journals (Sweden)

Changyong Shu

2016-04-01

Full Text Available The accurate micro-motions recognition of spatial cone target is the foundation of the characteristic parameter acquisition. For this reason, a micro-motion recognition method based on the distinguishing characteristics extracted from the Inverse Synthetic Aperture Radar (ISAR sequences is proposed in this paper. The projection trajectory formula of cone node strong scattering source and cone bottom slip-type strong scattering sources, which are located on the spatial cone target, are deduced under three micro-motion types including nutation, precession, and spinning, and the correctness is verified by the electromagnetic simulation. By comparison, differences are found among the projection of the scattering sources with different micro-motions, the coordinate information of the scattering sources in the Inverse Synthetic Aperture Radar sequences is extracted by the CLEAN algorithm, and the spinning is recognized by setting the threshold value of Doppler. The double observation points Interacting Multiple Model Kalman Filter is used to separate the scattering sources projection of the nutation target or precession target, and the cross point number of each scattering source’s projection track is used to classify the nutation or precession. Finally, the electromagnetic simulation data are used to verify the effectiveness of the micro-motion recognition method.

Biological Motion Perception in Autism

Directory of Open Access Journals (Sweden)

J Cusack

2011-04-01

Full Text Available Typically developing adults can readily recognize human actions, even when conveyed to them via point-like markers placed on the body of the actor (Johansson, 1973. Previous research has suggested that children affected by autism spectrum disorder (ASD are not equally sensitive to this type of visual information (Blake et al, 2003, but it remains unknown why ASD would impact the ability to perceive biological motion. We present evidence which looks at how adolescents and adults with autism are affected by specific factors which are important in biological motion perception, such as (eg, inter-agent synchronicity, upright/inverted, etc.

Gait Recognition Using Wearable Motion Recording Sensors

Directory of Open Access Journals (Sweden)

Davrondzhon Gafurov

2009-01-01

Full Text Available This paper presents an alternative approach, where gait is collected by the sensors attached to the person's body. Such wearable sensors record motion (e.g. acceleration of the body parts during walking. The recorded motion signals are then investigated for person recognition purposes. We analyzed acceleration signals from the foot, hip, pocket and arm. Applying various methods, the best EER obtained for foot-, pocket-, arm- and hip- based user authentication were 5%, 7%, 10% and 13%, respectively. Furthermore, we present the results of our analysis on security assessment of gait. Studying gait-based user authentication (in case of hip motion under three attack scenarios, we revealed that a minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users can be a threat to gait-based authentication. We also provide some new insights toward the uniqueness of gait in case of foot motion. In particular, we revealed the following: a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward directions; and different segments of the gait cycle provide different level of discrimination.

Impaired global, and compensatory local, biological motion processing in people with high levels of autistic traits.

Science.gov (United States)

van Boxtel, Jeroen J A; Lu, Hongjing

2013-01-01

People with Autism Spectrum Disorder (ASD) are hypothesized to have poor high-level processing but superior low-level processing, causing impaired social recognition, and a focus on non-social stimulus contingencies. Biological motion perception provides an ideal domain to investigate exactly how ASD modulates the interaction between low and high-level processing, because it involves multiple processing stages, and carries many important social cues. We investigated individual differences among typically developing observers in biological motion processing, and whether such individual differences associate with the number of autistic traits. In Experiment 1, we found that individuals with fewer autistic traits were automatically and involuntarily attracted to global biological motion information, whereas individuals with more autistic traits did not show this pre-attentional distraction. We employed an action adaptation paradigm in the second study to show that individuals with more autistic traits were able to compensate for deficits in global processing with an increased involvement in local processing. Our findings can be interpreted within a predictive coding framework, which characterizes the functional relationship between local and global processing stages, and explains how these stages contribute to the perceptual difficulties associated with ASD.

Impaired global, and compensatory local, biological motion processing in people with high levels of autistic traits

Directory of Open Access Journals (Sweden)

Jeroen J A Van Boxtel

2013-04-01

Full Text Available People with Autism Spectrum Disorder (ASD are hypothesized to have poor high-level processing but superior low-level processing, causing impaired social recognition, and a focus on non-social stimulus contingencies. Biological motion perception provides an ideal domain to investigate exactly how ASD modulates the interaction between low and high-level processing, because it involves multiple processing stages, and carries many important social cues. We investigated individual differences among typically developing observers in biological motion processing, and whether such individual differences associate with the number of autistic traits. In Experiment 1, we found that individuals with fewer autistic traits were automatically and involuntarily attracted to global biological motion information, whereas individuals with more autistic traits did not show this pre-attentional distraction. We employed an action adaptation paradigm in the second study to show that individuals with more autistic traits were able to compensate for deficits in global processing with an increased involvement in local processing. Our findings can be interpreted within a predictive coding framework, which characterizes the functional relationship between local and global processing stages, and explains how these stages contribute to the perceptual difficulties associated with ASD.

Unaffected perceptual thresholds for biological and non-biological form-from-motion perception in autism spectrum conditions.

Directory of Open Access Journals (Sweden)

Ayse Pinar Saygin

2010-10-01

Full Text Available Perception of biological motion is linked to the action perception system in the human brain, abnormalities within which have been suggested to underlie impairments in social domains observed in autism spectrum conditions (ASC. However, the literature on biological motion perception in ASC is heterogeneous and it is unclear whether deficits are specific to biological motion, or might generalize to form-from-motion perception.We compared psychophysical thresholds for both biological and non-biological form-from-motion perception in adults with ASC and controls. Participants viewed point-light displays depicting a walking person (Biological Motion, a translating rectangle (Structured Object or a translating unfamiliar shape (Unstructured Object. The figures were embedded in noise dots that moved similarly and the task was to determine direction of movement. The number of noise dots varied on each trial and perceptual thresholds were estimated adaptively. We found no evidence for an impairment in biological or non-biological object motion perception in individuals with ASC. Perceptual thresholds in the three conditions were almost identical between the ASC and control groups.Impairments in biological motion and non-biological form-from-motion perception are not across the board in ASC, and are only found for some stimuli and tasks. We discuss our results in relation to other findings in the literature, the heterogeneity of which likely relates to the different tasks performed. It appears that individuals with ASC are unaffected in perceptual processing of form-from-motion, but may exhibit impairments in higher order judgments such as emotion processing. It is important to identify more specifically which processes of motion perception are impacted in ASC before a link can be made between perceptual deficits and the higher-level features of the disorder.

Social network size relates to developmental neural sensitivity to biological motion

Directory of Open Access Journals (Sweden)

L.A. Kirby

2018-04-01

Full Text Available The ability to perceive others’ actions and goals from human motion (i.e., biological motion perception is a critical component of social perception and may be linked to the development of real-world social relationships. Adult research demonstrates two key nodes of the brain’s biological motion perception system—amygdala and posterior superior temporal sulcus (pSTS—are linked to variability in social network properties. The relation between social perception and social network properties, however, has not yet been investigated in middle childhood—a time when individual differences in social experiences and social perception are growing. The aims of this study were to (1 replicate past work showing amygdala and pSTS sensitivity to biological motion in middle childhood; (2 examine age-related changes in the neural sensitivity for biological motion, and (3 determine whether neural sensitivity for biological motion relates to social network characteristics in children. Consistent with past work, we demonstrate a significant relation between social network size and neural sensitivity for biological motion in left pSTS, but do not find age-related change in biological motion perception. This finding offers evidence for the interplay between real-world social experiences and functional brain development and has important implications for understanding disorders of atypical social experience. Keywords: Biological motion, Social networks, Middle childhood, Neural specialization, Brain-behavior relations, pSTS

IQ Predicts Biological Motion Perception in Autism Spectrum Disorders

Science.gov (United States)

Rutherford, M. D.; Troje, Nikolaus F.

2012-01-01

Biological motion is easily perceived by neurotypical observers when encoded in point-light displays. Some but not all relevant research shows significant deficits in biological motion perception among those with ASD, especially with respect to emotional displays. We tested adults with and without ASD on the perception of masked biological motion…

Action Recognition using Motion Primitives

DEFF Research Database (Denmark)

Moeslund, Thomas B.; Fihl, Preben; Holte, Michael Boelstoft

the actions as a sequence of temporal isolated instances, denoted primitives. These primitives are each defined by four features extracted from motion images. The primitives are recognized in each frame based on a trained classifier resulting in a sequence of primitives. From this sequence we recognize......The number of potential applications has made automatic recognition of human actions a very active research area. Different approaches have been followed based on trajectories through some state space. In this paper we also model an action as a trajectory through a state space, but we represent...... different temporal actions using a probabilistic Edit Distance method. The method is tested on different actions with and without noise and the results show recognizing rates of 88.7% and 85.5%, respectively....

Surface EMG signals based motion intent recognition using multi-layer ELM

Science.gov (United States)

Wang, Jianhui; Qi, Lin; Wang, Xiao

2017-11-01

The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human's intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.

A Self-Powered Insole for Human Motion Recognition

Directory of Open Access Journals (Sweden)

Yingzhou Han

2016-09-01

Full Text Available Biomechanical energy harvesting is a feasible solution for powering wearable sensors by directly driving electronics or acting as wearable self-powered sensors. A wearable insole that not only can harvest energy from foot pressure during walking but also can serve as a self-powered human motion recognition sensor is reported. The insole is designed as a sandwich structure consisting of two wavy silica gel film separated by a flexible piezoelectric foil stave, which has higher performance compared with conventional piezoelectric harvesters with cantilever structure. The energy harvesting insole is capable of driving some common electronics by scavenging energy from human walking. Moreover, it can be used to recognize human motion as the waveforms it generates change when people are in different locomotion modes. It is demonstrated that different types of human motion such as walking and running are clearly classified by the insole without any external power source. This work not only expands the applications of piezoelectric energy harvesters for wearable power supplies and self-powered sensors, but also provides possible approaches for wearable self-powered human motion monitoring that is of great importance in many fields such as rehabilitation and sports science.

Changing predictions, stable recognition: Children’s representations of downward incline motion

OpenAIRE

Hast, Michael; Howe, Christine

2017-01-01

Various studies to-date have demonstrated children hold ill-conceived expressed beliefs about the physical world such as that one ball will fall faster than another because it is heavier. At the same time they also demonstrate accurate recognition of dynamic events. How these representations relate is still unresolved. This study examined 5- to 11-year-olds’ (N = 130) predictions and recognition of motion down inclines. Predictions were typically in error, matching previous work, but children...

Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition

Directory of Open Access Journals (Sweden)

Zheng Chang

2015-01-01

Full Text Available Based on the traditional machine vision recognition technology and traditional artificial neural networks about body movement trajectory, this paper finds out the shortcomings of the traditional recognition technology. By combining the invariant moments of the three-dimensional motion history image (computed as the eigenvector of body movements and the extreme learning machine (constructed as the classification artificial neural network of body movements, the paper applies the method to the machine vision of the body movement trajectory. In detail, the paper gives a detailed introduction about the algorithm and realization scheme of the body movement trajectory recognition based on the three-dimensional motion history image and the extreme learning machine. Finally, by comparing with the results of the recognition experiments, it attempts to verify that the method of body movement trajectory recognition technology based on the three-dimensional motion history image and extreme learning machine has a more accurate recognition rate and better robustness.

Changing predictions, stable recognition: Children's representations of downward incline motion.

Science.gov (United States)

Hast, Michael; Howe, Christine

2017-11-01

Various studies to-date have demonstrated children hold ill-conceived expressed beliefs about the physical world such as that one ball will fall faster than another because it is heavier. At the same time, they also demonstrate accurate recognition of dynamic events. How these representations relate is still unresolved. This study examined 5- to 11-year-olds' (N = 130) predictions and recognition of motion down inclines. Predictions were typically in error, matching previous work, but children largely recognized correct events as correct and rejected incorrect ones. The results also demonstrate while predictions change with increasing age, recognition shows signs of stability. The findings provide further support for a hybrid model of object representations and argue in favour of stable core cognition existing alongside developmental changes. Statement of contribution What is already known on this subject? Children's predictions of physical events show limitations in accuracy Their recognition of such events suggests children may use different knowledge sources in their reasoning What the present study adds? Predictions fluctuate more strongly than recognition, suggesting stable core cognition But recognition also shows some fluctuation, arguing for a hybrid model of knowledge representation. © 2017 The British Psychological Society.

Biologically inspired emotion recognition from speech

Directory of Open Access Journals (Sweden)

Buscicchio Cosimo

2011-01-01

Full Text Available Abstract Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

Directory of Open Access Journals (Sweden)

Gaojing Wang

2018-06-01

Full Text Available Human activity recognition (HAR is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.

Motion Primitives and Probabilistic Edit Distance for Action Recognition

DEFF Research Database (Denmark)

Fihl, Preben; Holte, Michael Boelstoft; Moeslund, Thomas B.

2009-01-01

the actions as a sequence of temporal isolated instances, denoted primitives. These primitives are each defined by four features extracted from motion images. The primitives are recognized in each frame based on a trained classifier resulting in a sequence of primitives. From this sequence we recognize......The number of potential applications has made automatic recognition of human actions a very active research area. Different approaches have been followed based on trajectories through some state space. In this paper we also model an action as a trajectory through a state space, but we represent...... different temporal actions using a probabilistic Edit Distance method. The method is tested on different actions with and without noise and the results show recognition rates of 88.7% and 85.5%, respectively....

An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

Directory of Open Access Journals (Sweden)

Chengyu Guo

2014-01-01

Full Text Available This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM and autoregressive process, a predictive fusion model (PFM is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.

Disappearance of the inversion effect during memory-guided tracking of scrambled biological motion.

Science.gov (United States)

Jiang, Changhao; Yue, Guang H; Chen, Tingting; Ding, Jinhong

2016-08-01

The human visual system is highly sensitive to biological motion. Even when a point-light walker is temporarily occluded from view by other objects, our eyes are still able to maintain tracking continuity. To investigate how the visual system establishes a correspondence between the biological-motion stimuli visible before and after the disruption, we used the occlusion paradigm with biological-motion stimuli that were intact or scrambled. The results showed that during visually guided tracking, both the observers' predicted times and predictive smooth pursuit were more accurate for upright biological motion (intact and scrambled) than for inverted biological motion. During memory-guided tracking, however, the processing advantage for upright as compared with inverted biological motion was not found in the scrambled condition, but in the intact condition only. This suggests that spatial location information alone is not sufficient to build and maintain the representational continuity of the biological motion across the occlusion, and that the object identity may act as an important information source in visual tracking. The inversion effect disappeared when the scrambled biological motion was occluded, which indicates that when biological motion is temporarily occluded and there is a complete absence of visual feedback signals, an oculomotor prediction is executed to maintain the tracking continuity, which is established not only by updating the target's spatial location, but also by the retrieval of identity information stored in long-term memory.

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

NARCIS (Netherlands)

Shoaib, M.; Bosch, S.; Durmaz, O.; Scholten, Johan; Havinga, Paul J.M.

2016-01-01

The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such

Visual event-related potentials to biological motion stimuli in autism spectrum disorders

Science.gov (United States)

Bletsch, Anke; Krick, Christoph; Siniatchkin, Michael; Jarczok, Tomasz A.; Freitag, Christine M.; Bender, Stephan

2014-01-01

Atypical visual processing of biological motion contributes to social impairments in autism spectrum disorders (ASD). However, the exact temporal sequence of deficits of cortical biological motion processing in ASD has not been studied to date. We used 64-channel electroencephalography to study event-related potentials associated with human motion perception in 17 children and adolescents with ASD and 21 typical controls. A spatio-temporal source analysis was performed to assess the brain structures involved in these processes. We expected altered activity already during early stimulus processing and reduced activity during subsequent biological motion specific processes in ASD. In response to both, random and biological motion, the P100 amplitude was decreased suggesting unspecific deficits in visual processing, and the occipito-temporal N200 showed atypical lateralization in ASD suggesting altered hemispheric specialization. A slow positive deflection after 400 ms, reflecting top-down processes, and human motion-specific dipole activation differed slightly between groups, with reduced and more diffuse activation in the ASD-group. The latter could be an indicator of a disrupted neuronal network for biological motion processing in ADS. Furthermore, early visual processing (P100) seems to be correlated to biological motion-specific activation. This emphasizes the relevance of early sensory processing for higher order processing deficits in ASD. PMID:23887808

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors.

Science.gov (United States)

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

2016-03-24

The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2-30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.

Emotion Recognition in Face and Body Motion in Bulimia Nervosa.

Science.gov (United States)

Dapelo, Marcela Marin; Surguladze, Simon; Morris, Robin; Tchanturia, Kate

2017-11-01

Social cognition has been studied extensively in anorexia nervosa (AN), but there are few studies in bulimia nervosa (BN). This study investigated the ability of people with BN to recognise emotions in ambiguous facial expressions and in body movement. Participants were 26 women with BN, who were compared with 35 with AN, and 42 healthy controls. Participants completed an emotion recognition task by using faces portraying blended emotions, along with a body emotion recognition task by using videos of point-light walkers. The results indicated that BN participants exhibited difficulties recognising disgust in less-ambiguous facial expressions, and a tendency to interpret non-angry faces as anger, compared with healthy controls. These difficulties were similar to those found in AN. There were no significant differences amongst the groups in body motion emotion recognition. The findings suggest that difficulties with disgust and anger recognition in facial expressions may be shared transdiagnostically in people with eating disorders. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association.

Biological Motion Cues Trigger Reflexive Attentional Orienting

Science.gov (United States)

Shi, Jinfu; Weng, Xuchu; He, Sheng; Jiang, Yi

2010-01-01

The human visual system is extremely sensitive to biological signals around us. In the current study, we demonstrate that biological motion walking direction can induce robust reflexive attentional orienting. Following a brief presentation of a central point-light walker walking towards either the left or right direction, observers' performance…

Facilitating Effects of Emotion on the Perception of Biological Motion: Evidence for a Happiness Superiority Effect.

Science.gov (United States)

Lee, Hannah; Kim, Jejoong

2017-06-01

It has been reported that visual perception can be influenced not only by the physical features of a stimulus but also by the emotional valence of the stimulus, even without explicit emotion recognition. Some previous studies reported an anger superiority effect while others found a happiness superiority effect during visual perception. It thus remains unclear as to which emotion is more influential. In the present study, we conducted two experiments using biological motion (BM) stimuli to examine whether emotional valence of the stimuli would affect BM perception; and if so, whether a specific type of emotion is associated with a superiority effect. Point-light walkers with three emotion types (anger, happiness, and neutral) were used, and the threshold to detect BM within noise was measured in Experiment 1. Participants showed higher performance in detecting happy walkers compared with the angry and neutral walkers. Follow-up motion velocity analysis revealed that physical difference among the stimuli was not the main factor causing the effect. The results of the emotion recognition task in Experiment 2 also showed a happiness superiority effect, as in Experiment 1. These results show that emotional valence (happiness) of the stimuli can facilitate the processing of BM.

A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

Directory of Open Access Journals (Sweden)

Minglin Wu

2016-10-01

Full Text Available In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.

Attentional Networks and Biological Motion

Directory of Open Access Journals (Sweden)

Chandramouli Chandrasekaran

2010-03-01

Full Text Available Our ability to see meaningful actions when presented with pointlight traces of human movement is commonly referred to as the perception of biological motion. While traditionalexplanations have emphasized the spontaneous and automatic nature of this ability, morerecent findings suggest that attention may play a larger role than is typically assumed. Intwo studies we show that the speed and accuracy of responding to point-light stimuli is highly correlated with the ability to control selective attention. In our first experiment we measured thresholds for determining the walking direction of a masked point-light figure, and performance on a range of attention-related tasks in the same set of observers. Mask-density thresholds for the direction discrimination task varied quite considerably from observer to observer and this variation was highly correlated with performance on both Stroop and flanker interference tasks. Other components of attention, such as orienting, alerting and visual search efficiency, showed no such relationship. In a second experiment, we examined the relationship between the ability to determine the orientation of unmasked point-light actions and Stroop interference, again finding a strong correlation. Our results are consistent with previous research suggesting that biological motion processing may requite attention, and specifically implicate networks of attention related to executive control and selection.

S1-3: Perception of Biological Motion in Schizophrenia and Obsessive-Compulsive Disorder

Directory of Open Access Journals (Sweden)

Jejoong Kim

2012-10-01

Full Text Available Major mental disorders including schizophrenia, autism, and obsessive-compulsive disorder (OCD are characterized by impaired social functioning regardless of wide range of clinical symptoms. Past studies also revealed that people with these mental illness exhibit perceptual problems with altered neural activation. For example, schizophrenia patients are deficient in processing rapid and dynamic visual stimuli. As well documented, people are very sensitive to motion signals generated by others (i.e., biological motion even when those motions are portrayed by point-light display. Therefore, ability to perceive biological motion is important for both visual perception and social functioning. Nevertheless, there have been no systematic attempts to investigate biological motion perception in people with mental illness associated with impaired social functioning until a decade ago. Recently, a series of studies newly revealed abnormal patterns of biological motion perception and associated neural activations in schizophrenia and OCD. These new achievements will be reviewed focusing on perceptual and neural difference between patients with schizophrenia/OCD and healthy individuals. Then implications and possible future research will be discussed in this talk.

Biological inspiration used for robots motion synthesis.

Science.gov (United States)

Zielińska, Teresa

2009-01-01

This work presents a biologically inspired method of gait generation. Bipedal gait pattern (for hip and knee joints) was taken into account giving the reference trajectories in a learning task. The four coupled oscillators were taught to generate the outputs similar to those in a human gait. After applying the correction functions the obtained generation method was validated using ZMP criterion. The formula suitable for real-time motion generation taking into account the positioning errors was also formulated. The small real robot prototype was tested to be able walk successfully following the elaborated motion pattern.

Backward-walking biological motion orients attention to moving away instead of moving toward.

Science.gov (United States)

Ding, Xiaowei; Yin, Jun; Shui, Rende; Zhou, Jifan; Shen, Mowei

2017-04-01

Walking direction is an important attribute of biological motion because it carries key information, such as the specific intention of the walker. Although it is known that spatial attention is guided by walking direction, it remains unclear whether this attentional shift is reflexive (i.e., constantly shifts to the walking direction) or not. A richer interpretation of this effect is that attention is guided to seek the information that is necessary to understand the motion. To investigate this issue, we examined how backward-walking biological motion orients attention because the intention of walking backward is usually to avoid something that walking forward would encounter. The results showed that attention was oriented to the walking-away direction of biological motion instead of the walking-toward direction (Experiment 1), and this effect was not due to the gaze direction of biological motion (Experiment 2). Our findings suggest that the attentional shift triggered by walking direction is not reflexive, thus providing support for the rich interpretation of these attentional effects.

Structural insight into RNA recognition motifs: versatile molecular Lego building blocks for biological systems.

Science.gov (United States)

Muto, Yutaka; Yokoyama, Shigeyuki

2012-01-01

'RNA recognition motifs (RRMs)' are common domain-folds composed of 80-90 amino-acid residues in eukaryotes, and have been identified in many cellular proteins. At first they were known as RNA binding domains. Through discoveries over the past 20 years, however, the RRMs have been shown to exhibit versatile molecular recognition activities and to behave as molecular Lego building blocks to construct biological systems. Novel RNA/protein recognition modes by RRMs are being identified, and more information about the molecular recognition by RRMs is becoming available. These RNA/protein recognition modes are strongly correlated with their biological significance. In this review, we would like to survey the recent progress on these versatile molecular recognition modules. Copyright © 2012 John Wiley & Sons, Ltd.

Two-year-olds with autism orient to nonsocial contingencies rather than biological motion

Science.gov (United States)

Klin, Ami; Lin, David J.; Gorrindo, Phillip; Ramsay, Gordon; Jones, Warren

2009-01-01

Typically-developing human infants preferentially attend to biological motion within the first days of life1. This ability is highly conserved across species2,3 and is believed to be critical for filial attachment and for detection of predators4. The neural underpinnings of biological motion perception are overlapping with brain regions involved in perception of basic social signals such as facial expression and gaze direction5, and preferential attention to biological motion is seen as a precursor to the capacity for attributing intentions to others6. However, in a serendipitous observation7, we recently found that an infant with autism failed to recognize point-light displays of biological motion but was instead highly sensitive to the presence of a non-social, physical contingency that occurred within the stimuli by chance. This observation raised the hypothesis that perception of biological motion may be altered in children with autism from a very early age, with cascading consequences for both social development and for the lifelong impairments in social interaction that are a hallmark of autism spectrum disorders8. Here we show that two-year-olds with autism fail to orient towards point-light displays of biological motion, and that their viewing behavior when watching these point-light displays can be explained instead as a response to non-social, physical contingencies physical contingencies that are disregarded by control children. This observation has far-reaching implications for understanding the altered neurodevelopmental trajectory of brain specialization in autism9. PMID:19329996

Identification of strong earthquake ground motion by using pattern recognition

International Nuclear Information System (INIS)

Suzuki, Kohei; Tozawa, Shoji; Temmyo, Yoshiharu.

1983-01-01

The method of grasping adequately the technological features of complex waveform of earthquake ground motion and utilizing them as the input to structural systems has been proposed by many researchers, and the method of making artificial earthquake waves to be used for the aseismatic design of nuclear facilities has not been established in the unified form. In this research, earthquake ground motion was treated as an irregular process with unsteady amplitude and frequency, and the running power spectral density was expressed as a dark and light image on a plane of the orthogonal coordinate system with both time and frequency axes. The method of classifying this image into a number of technologically important categories by pattern recognition was proposed. This method is based on the concept called compound similarity method in the image technology, entirely different from voice diagnosis, and it has the feature that the result of identification can be quantitatively evaluated by the analysis of correlation of spatial images. Next, the standard pattern model of the simulated running power spectral density corresponding to the representative classification categories was proposed. Finally, the method of making unsteady simulated earthquake motion was shown. (Kako, I.)

Human Action Recognition Using Ordinal Measure of Accumulated Motion

Directory of Open Access Journals (Sweden)

Kim Wonjun

2010-01-01

Full Text Available This paper presents a method for recognizing human actions from a single query action video. We propose an action recognition scheme based on the ordinal measure of accumulated motion, which is robust to variations of appearances. To this end, we first define the accumulated motion image (AMI using image differences. Then the AMI of the query action video is resized to a subimage by intensity averaging and a rank matrix is generated by ordering the sample values in the sub-image. By computing the distances from the rank matrix of the query action video to the rank matrices of all local windows in the target video, local windows close to the query action are detected as candidates. To find the best match among the candidates, their energy histograms, which are obtained by projecting AMI values in horizontal and vertical directions, respectively, are compared with those of the query action video. The proposed method does not require any preprocessing task such as learning and segmentation. To justify the efficiency and robustness of our approach, the experiments are conducted on various datasets.

A novel rotational invariants target recognition method for rotating motion blurred images

Science.gov (United States)

Lan, Jinhui; Gong, Meiling; Dong, Mingwei; Zeng, Yiliang; Zhang, Yuzhen

2017-11-01

The imaging of the image sensor is blurred due to the rotational motion of the carrier and reducing the target recognition rate greatly. Although the traditional mode that restores the image first and then identifies the target can improve the recognition rate, it takes a long time to recognize. In order to solve this problem, a rotating fuzzy invariants extracted model was constructed that recognizes target directly. The model includes three metric layers. The object description capability of metric algorithms that contain gray value statistical algorithm, improved round projection transformation algorithm and rotation-convolution moment invariants in the three metric layers ranges from low to high, and the metric layer with the lowest description ability among them is as the input which can eliminate non pixel points of target region from degenerate image gradually. Experimental results show that the proposed model can improve the correct target recognition rate of blurred image and optimum allocation between the computational complexity and function of region.

Improved Hip-Based Individual Recognition Using Wearable Motion Recording Sensor

Science.gov (United States)

Gafurov, Davrondzhon; Bours, Patrick

In todays society the demand for reliable verification of a user identity is increasing. Although biometric technologies based on fingerprint or iris can provide accurate and reliable recognition performance, they are inconvenient for periodic or frequent re-verification. In this paper we propose a hip-based user recognition method which can be suitable for implicit and periodic re-verification of the identity. In our approach we use a wearable accelerometer sensor attached to the hip of the person, and then the measured hip motion signal is analysed for identity verification purposes. The main analyses steps consists of detecting gait cycles in the signal and matching two sets of detected gait cycles. Evaluating the approach on a hip data set consisting of 400 gait sequences (samples) from 100 subjects, we obtained equal error rate (EER) of 7.5% and identification rate at rank 1 was 81.4%. These numbers are improvements by 37.5% and 11.2% respectively of the previous study using the same data set.

Ventral aspect of the visual form pathway is not critical for the perception of biological motion

Science.gov (United States)

Gilaie-Dotan, Sharon; Saygin, Ayse Pinar; Lorenzi, Lauren J.; Rees, Geraint; Behrmann, Marlene

2015-01-01

Identifying the movements of those around us is fundamental for many daily activities, such as recognizing actions, detecting predators, and interacting with others socially. A key question concerns the neurobiological substrates underlying biological motion perception. Although the ventral “form” visual cortex is standardly activated by biologically moving stimuli, whether these activations are functionally critical for biological motion perception or are epiphenomenal remains unknown. To address this question, we examined whether focal damage to regions of the ventral visual cortex, resulting in significant deficits in form perception, adversely affects biological motion perception. Six patients with damage to the ventral cortex were tested with sensitive point-light display paradigms. All patients were able to recognize unmasked point-light displays and their perceptual thresholds were not significantly different from those of three different control groups, one of which comprised brain-damaged patients with spared ventral cortex (n > 50). Importantly, these six patients performed significantly better than patients with damage to regions critical for biological motion perception. To assess the necessary contribution of different regions in the ventral pathway to biological motion perception, we complement the behavioral findings with a fine-grained comparison between the lesion location and extent, and the cortical regions standardly implicated in biological motion processing. This analysis revealed that the ventral aspects of the form pathway (e.g., fusiform regions, ventral extrastriate body area) are not critical for biological motion perception. We hypothesize that the role of these ventral regions is to provide enhanced multiview/posture representations of the moving person rather than to represent biological motion perception per se. PMID:25583504

Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

CERN Document Server

Pisharady, Pramod Kumar; Poh, Loh Ai

2014-01-01

This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

Biological Motion Preference in Humans at Birth: Role of Dynamic and Configural Properties

Science.gov (United States)

Bardi, Lara; Regolin, Lucia; Simion, Francesca

2011-01-01

The present study addresses the hypothesis that detection of biological motion is an intrinsic capacity of the visual system guided by a non-species-specific predisposition for the pattern of vertebrate movement and investigates the role of global vs. local information in biological motion detection. Two-day-old babies exposed to a biological…

Image processing and recognition for biological images.

Science.gov (United States)

Uchida, Seiichi

2013-05-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Can molecular cell biology explain chromosome motions?

Directory of Open Access Journals (Sweden)

Gagliardi L

2011-05-01

Full Text Available Abstract Background Mitotic chromosome motions have recently been correlated with electrostatic forces, but a lingering "molecular cell biology" paradigm persists, proposing binding and release proteins or molecular geometries for force generation. Results Pole-facing kinetochore plates manifest positive charges and interact with negatively charged microtubule ends providing the motive force for poleward chromosome motions by classical electrostatics. This conceptual scheme explains dynamic tracking/coupling of kinetochores to microtubules and the simultaneous depolymerization of kinetochore microtubules as poleward force is generated. Conclusion We question here why cells would prefer complex molecular mechanisms to move chromosomes when direct electrostatic interactions between known bound charge distributions can accomplish the same task much more simply.

Sensitivity to synchronicity of biological motion in normal and amblyopic vision

Science.gov (United States)

Luu, Jennifer Y.; Levi, Dennis M.

2017-01-01

Amblyopia is a developmental disorder of spatial vision that results from abnormal early visual experience usually due to the presence of strabismus, anisometropia, or both strabismus and anisometropia. Amblyopia results in a range of visual deficits that cannot be corrected by optics because the deficits reflect neural abnormalities. Biological motion refers to the motion patterns of living organisms, and is normally displayed as points of lights positioned at the major joints of the body. In this experiment, our goal was twofold. We wished to examine whether the human visual system in people with amblyopia retained the higher-level processing capabilities to extract visual information from the synchronized actions of others, therefore retaining the ability to detect biological motion. Specifically, we wanted to determine if the synchronized interaction of two agents performing a dancing routine allowed the amblyopic observer to use the actions of one agent to predict the expected actions of a second agent. We also wished to establish whether synchronicity sensitivity (detection of synchronized versus desynchronized interactions) is impaired in amblyopic observers relative to normal observers. The two aims are differentiated in that the first aim looks at whether synchronized actions result in improved expected action predictions while the second aim quantitatively compares synchronicity sensitivity, or the ratio of desynchronized to synchronized detection sensitivities, to determine if there is a difference between normal and amblyopic observers. Our results show that the ability to detect biological motion requires more samples in both eyes of amblyopes than in normal control observers. The increased sample threshold is not the result of low-level losses but may reflect losses in feature integration due to undersampling in the amblyopic visual system. However, like normal observers, amblyopes are more sensitive to synchronized versus desynchronized interactions

Decreased reward value of biological motion among individuals with autistic traits.

Science.gov (United States)

Williams, Elin H; Cross, Emily S

2018-02-01

The Social Motivation Theory posits that a reduced sensitivity to the value of social stimuli, specifically faces, can account for social impairments in Autism Spectrum Disorders (ASD). Research has demonstrated that typically developing (TD) individuals preferentially orient towards another type of salient social stimulus, namely biological motion. Individuals with ASD, however, do not show this preference. While the reward value of faces to both TD and ASD individuals has been well-established, the extent to which individuals from these populations also find human motion to be rewarding remains poorly understood. The present study investigated the value assigned to biological motion by TD participants in an effort task, and further examined whether these values differed among individuals with more autistic traits. The results suggest that TD participants value natural human motion more than rigid, machine-like motion or non-human control motion, but this preference is attenuated among individuals reporting more autistic traits. This study provides the first evidence to suggest that individuals with more autistic traits find a broader conceptualisation of social stimuli less rewarding compared to individuals with fewer autistic traits. By quantifying the social reward value of human motion, the present findings contribute an important piece to our understanding of social motivation in individuals with and without social impairments. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Pattern recognition neural-net by spatial mapping of biology visual field

Science.gov (United States)

Lin, Xin; Mori, Masahiko

2000-05-01

The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

Visual form Cues, Biological Motions, Auditory Cues, and Even Olfactory Cues Interact to Affect Visual Sex Discriminations

OpenAIRE

Rick Van Der Zwan; Anna Brooks; Duncan Blair; Coralia Machatch; Graeme Hacker

2011-01-01

Johnson and Tassinary (2005) proposed that visually perceived sex is signalled by structural or form cues. They suggested also that biological motion cues signal sex, but do so indirectly. We previously have shown that auditory cues can mediate visual sex perceptions (van der Zwan et al., 2009). Here we demonstrate that structural cues to body shape are alone sufficient for visual sex discriminations but that biological motion cues alone are not. Interestingly, biological motions can resolve ...

Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos

Directory of Open Access Journals (Sweden)

Li Yao

2016-01-01

Full Text Available Both static features and motion features have shown promising performance in human activities recognition task. However, the information included in these features is insufficient for complex human activities. In this paper, we propose extracting relational information of static features and motion features for human activities recognition. The videos are represented by a classical Bag-of-Word (BoW model which is useful in many works. To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook. After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set. Then we use a k-way partition to create a new codebook in which similar words are getting together. With this new codebook, videos can be represented by a new BoW vector with strong relational information. Moreover, we propose a method to compute new clusters from the divisive algorithm’s projective function. We test our work on the several datasets and obtain very promising results.

BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.

Science.gov (United States)

Zhou, Jie; Lamichhane, Santosh; Sterne, Gabriella; Ye, Bing; Peng, Hanchuan

2013-10-04

Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological

Atypical biological motion kinematics are represented by complementary lower-level and top-down processes during imitation learning.

Science.gov (United States)

Hayes, Spencer J; Dutoy, Chris A; Elliott, Digby; Gowen, Emma; Bennett, Simon J

2016-01-01

Learning a novel movement requires a new set of kinematics to be represented by the sensorimotor system. This is often accomplished through imitation learning where lower-level sensorimotor processes are suggested to represent the biological motion kinematics associated with an observed movement. Top-down factors have the potential to influence this process based on the social context, attention and salience, and the goal of the movement. In order to further examine the potential interaction between lower-level and top-down processes in imitation learning, the aim of this study was to systematically control the mediating effects during an imitation of biological motion protocol. In this protocol, we used non-human agent models that displayed different novel atypical biological motion kinematics, as well as a control model that displayed constant velocity. Importantly the three models had the same movement amplitude and movement time. Also, the motion kinematics were displayed in the presence, or absence, of end-state-targets. Kinematic analyses showed atypical biological motion kinematics were imitated, and that this performance was different from the constant velocity control condition. Although the imitation of atypical biological motion kinematics was not modulated by the end-state-targets, movement time was more accurate in the absence, compared to the presence, of an end-state-target. The fact that end-state targets modulated movement time accuracy, but not biological motion kinematics, indicates imitation learning involves top-down attentional, and lower-level sensorimotor systems, which operate as complementary processes mediated by the environmental context. Copyright © 2015 Elsevier B.V. All rights reserved.

Brain responses to biological motion predict treatment outcome in young adults with autism receiving Virtual Reality Social Cognition Training: Preliminary findings.

Science.gov (United States)

Yang, Y J Daniel; Allen, Tandra; Abdullahi, Sebiha M; Pelphrey, Kevin A; Volkmar, Fred R; Chapman, Sandra B

2017-06-01

Autism Spectrum Disorder (ASD) is characterized by remarkable heterogeneity in social, communication, and behavioral deficits, creating a major barrier in identifying effective treatments for a given individual with ASD. To facilitate precision medicine in ASD, we utilized a well-validated biological motion neuroimaging task to identify pretreatment biomarkers that can accurately forecast the response to an evidence-based behavioral treatment, Virtual Reality-Social Cognition Training (VR-SCT). In a preliminary sample of 17 young adults with high-functioning ASD, we identified neural predictors of change in emotion recognition after VR-SCT. The predictors were characterized by the pretreatment brain activations to biological vs. scrambled motion in the neural circuits that support (a) language comprehension and interpretation of incongruent auditory emotions and prosody, and (b) processing socio-emotional experience and interpersonal affective information, as well as emotional regulation. The predictive value of the findings for individual adults with ASD was supported by regression-based multivariate pattern analyses with cross validation. To our knowledge, this is the first pilot study that shows neuroimaging-based predictive biomarkers for treatment effectiveness in adults with ASD. The findings have potentially far-reaching implications for developing more precise and effective treatments for ASD. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Detecting Biological Motion for Human–Robot Interaction: A Link between Perception and Action

Directory of Open Access Journals (Sweden)

Alessia Vignolo

2017-06-01

Full Text Available One of the fundamental skills supporting safe and comfortable interaction between humans is their capability to understand intuitively each other’s actions and intentions. At the basis of this ability is a special-purpose visual processing that human brain has developed to comprehend human motion. Among the first “building blocks” enabling the bootstrapping of such visual processing is the ability to detect movements performed by biological agents in the scene, a skill mastered by human babies in the first days of their life. In this paper, we present a computational model based on the assumption that such visual ability must be based on local low-level visual motion features, which are independent of shape, such as the configuration of the body and perspective. Moreover, we implement it on the humanoid robot iCub, embedding it into a software architecture that leverages the regularities of biological motion also to control robot attention and oculomotor behaviors. In essence, we put forth a model in which the regularities of biological motion link perception and action enabling a robotic agent to follow a human-inspired sensory-motor behavior. We posit that this choice facilitates mutual understanding and goal prediction during collaboration, increasing the pleasantness and safety of the interaction.

Diffusion-advection within dynamic biological gaps driven by structural motion

Science.gov (United States)

Asaro, Robert J.; Zhu, Qiang; Lin, Kuanpo

2018-04-01

To study the significance of advection in the transport of solutes, or particles, within thin biological gaps (channels), we examine theoretically the process driven by stochastic fluid flow caused by random thermal structural motion, and we compare it with transport via diffusion. The model geometry chosen resembles the synaptic cleft; this choice is motivated by the cleft's readily modeled structure, which allows for well-defined mechanical and physical features that control the advection process. Our analysis defines a Péclet-like number, AD, that quantifies the ratio of time scales of advection versus diffusion. Another parameter, AM, is also defined by the analysis that quantifies the full potential extent of advection in the absence of diffusion. These parameters provide a clear and compact description of the interplay among the well-defined structural, geometric, and physical properties vis-a ̀-vis the advection versus diffusion process. For example, it is found that AD˜1 /R2 , where R is the cleft diameter and hence diffusion distance. This curious, and perhaps unexpected, result follows from the dependence of structural motion that drives fluid flow on R . AM, on the other hand, is directly related (essentially proportional to) the energetic input into structural motion, and thereby to fluid flow, as well as to the mechanical stiffness of the cleftlike structure. Our model analysis thus provides unambiguous insight into the prospect of competition of advection versus diffusion within biological gaplike structures. The importance of the random, versus a regular, nature of structural motion and of the resulting transient nature of advection under random motion is made clear in our analysis. Further, by quantifying the effects of geometric and physical properties on the competition between advection and diffusion, our results clearly demonstrate the important role that metabolic energy (ATP) plays in this competitive process.

A biologically inspired neural network model to transformation invariant object recognition

Science.gov (United States)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

Effect of buffer at nanoscale molecular recognition interfaces - electrostatic binding of biological polyanions.

Science.gov (United States)

Rodrigo, Ana C; Laurini, Erik; Vieira, Vânia M P; Pricl, Sabrina; Smith, David K

2017-10-19

We investigate the impact of an over-looked component on molecular recognition in water-buffer. The binding of a cationic dye to biological polyanion heparin is shown by isothermal calorimetry to depend on buffer (Tris-HCl > HEPES > PBS). The heparin binding of self-assembled multivalent (SAMul) cationic micelles is even more buffer dependent. Multivalent electrostatic molecular recognition is buffer dependent as a result of competitive interactions between the cationic binding interface and anions present in the buffer.

Flexible Piezoelectric Sensor-Based Gait Recognition

Directory of Open Access Journals (Sweden)

Youngsu Cha

2018-02-01

Full Text Available Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

S1-1: Individual Differences in the Perception of Biological Motion

Directory of Open Access Journals (Sweden)

Ian Thornton

2012-10-01

Full Text Available Our ability to accurately perceive the actions of others based on reduced visual cues has been well documented. Previous work has suggested that this ability is probably made possible by separable mechanisms that can operate in either a passive, bottom-up fashion or an active, top-down fashion (Thornton, Rensink, & Shiffrar, 2002 Perception 31 837–853. One line of evidence for exploring the contribution of top-down mechanisms is to consider the extent to which individual differences in more general cognitive abilities, such as attention and working memory, predict performance on biological motion tasks. In this talk, I will begin by reviewing previous work that has looked at biological motion processing in clinical settings and as a function of domain-specific expertise. I will then introduce a new task that we are using in my lab to explore individual variation in action matching as a function of independently assessed attentional control and working memory capacity.

Motion-sensor fusion-based gesture recognition and its VLSI architecture design for mobile devices

Science.gov (United States)

Zhu, Wenping; Liu, Leibo; Yin, Shouyi; Hu, Siqi; Tang, Eugene Y.; Wei, Shaojun

2014-05-01

With the rapid proliferation of smartphones and tablets, various embedded sensors are incorporated into these platforms to enable multimodal human-computer interfaces. Gesture recognition, as an intuitive interaction approach, has been extensively explored in the mobile computing community. However, most gesture recognition implementations by now are all user-dependent and only rely on accelerometer. In order to achieve competitive accuracy, users are required to hold the devices in predefined manner during the operation. In this paper, a high-accuracy human gesture recognition system is proposed based on multiple motion sensor fusion. Furthermore, to reduce the energy overhead resulted from frequent sensor sampling and data processing, a high energy-efficient VLSI architecture implemented on a Xilinx Virtex-5 FPGA board is also proposed. Compared with the pure software implementation, approximately 45 times speed-up is achieved while operating at 20 MHz. The experiments show that the average accuracy for 10 gestures achieves 93.98% for user-independent case and 96.14% for user-dependent case when subjects hold the device randomly during completing the specified gestures. Although a few percent lower than the conventional best result, it still provides competitive accuracy acceptable for practical usage. Most importantly, the proposed system allows users to hold the device randomly during operating the predefined gestures, which substantially enhances the user experience.

Neuromorphic Configurable Architecture for Robust Motion Estimation

Directory of Open Access Journals (Sweden)

Guillermo Botella

2008-01-01

Full Text Available The robustness of the human visual system recovering motion estimation in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, efficiently, and effortlessly. There is obviously a great deal we can learn from our own visual system. Currently, there are several optical flow algorithms, although none of them deals efficiently with noise, illumination changes, second-order motion, occlusions, and so on. The main contribution of this work is the efficient implementation of a biologically inspired motion algorithm that borrows nature templates as inspiration in the design of architectures and makes use of a specific model of human visual motion perception: Multichannel Gradient Model (McGM. This novel customizable architecture of a neuromorphic robust optical flow can be constructed with FPGA or ASIC device using properties of the cortical motion pathway, constituting a useful framework for building future complex bioinspired systems running in real time with high computational complexity. This work includes the resource usage and performance data, and the comparison with actual systems. This hardware has many application fields like object recognition, navigation, or tracking in difficult environments due to its bioinspired and robustness properties.

Peptide and Peptide-Dependent Motions in MHC Proteins: Immunological Implications and Biophysical Underpinnings

Directory of Open Access Journals (Sweden)

Cory M. Ayres

2017-08-01

Full Text Available Structural biology of peptides presented by class I and class II MHC proteins has transformed immunology, impacting our understanding of fundamental immune mechanisms and allowing researchers to rationalize immunogenicity and design novel vaccines. However, proteins are not static structures as often inferred from crystallographic structures. Their components move and breathe individually and collectively over a range of timescales. Peptides bound within MHC peptide-binding grooves are no exception and their motions have been shown to impact recognition by T cell and other receptors in ways that influence function. Furthermore, peptides tune the motions of MHC proteins themselves, which impacts recognition of peptide/MHC complexes by other proteins. Here, we review the motional properties of peptides in MHC binding grooves and discuss how peptide properties can influence MHC motions. We briefly review theoretical concepts about protein motion and highlight key data that illustrate immunological consequences. We focus primarily on class I systems due to greater availability of data, but segue into class II systems as the concepts and consequences overlap. We suggest that characterization of the dynamic “energy landscapes” of peptide/MHC complexes and the resulting functional consequences is one of the next frontiers in structural immunology.

The effect of oxytocin on biological motion perception in dogs (Canis familiaris).

Science.gov (United States)

Kovács, Krisztina; Kis, Anna; Kanizsár, Orsolya; Hernádi, Anna; Gácsi, Márta; Topál, József

2016-05-01

Recent studies have shown that the neuropeptide oxytocin is involved in the regulation of several complex human social behaviours. There is, however, little research on the effect of oxytocin on basic mechanisms underlying human sociality, such as the perception of biological motion. In the present study, we investigated the effect of oxytocin on biological motion perception in dogs (Canis familiaris), a species adapted to the human social environment and thus widely used to model many aspects of human social behaviour. In a within-subjects design, dogs (N = 39), after having received either oxytocin or placebo treatment, were presented with 2D projection of a moving point-light human figure and the inverted and scrambled version of the same movie. Heart rate (HR) and heart rate variability (HRV) were measured as physiological responses, and behavioural response was evaluated by observing dogs' looking time. Subjects were also rated on the personality traits of Neuroticism and Agreeableness by their owners. As expected, placebo-pretreated (control) dogs showed a spontaneous preference for the biological motion pattern; however, there was no such preference after oxytocin pretreatment. Furthermore, following the oxytocin pretreatment female subjects looked more at the moving point-light figure than males. The individual variations along the dimensions of Agreeableness and Neuroticism also modulated dogs' behaviour. Furthermore, HR and HRV measures were affected by oxytocin treatment and in turn played a role in subjects' looking behaviour. We discuss how these findings contribute to our understanding of the neurohormonal regulatory mechanisms of human (and non-human) social skills.

Object instance recognition using motion cues and instance specific appearance models

Science.gov (United States)

Schumann, Arne

2014-03-01

In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.

Neural Response to Biological Motion in Healthy Adults Varies as a Function of Autistic-Like Traits

Directory of Open Access Journals (Sweden)

Meghan H. Puglia

2017-07-01

Full Text Available Perception of biological motion is an important social cognitive ability that has been mapped to specialized brain regions. Perceptual deficits and neural differences during biological motion perception have previously been associated with autism, a disorder classified by social and communication difficulties and repetitive and restricted interests and behaviors. However, the traits associated with autism are not limited to diagnostic categories, but are normally distributed within the general population and show the same patterns of heritability across the continuum. In the current study, we investigate whether self-reported autistic-like traits in healthy adults are associated with variable neural response during passive viewing of biological motion displays. Results show that more autistic-like traits, particularly those associated with the communication domain, are associated with increased neural response in key regions involved in social cognitive processes, including prefrontal and left temporal cortices. This distinct pattern of activation might reflect differential neurodevelopmental processes for individuals with varying autistic-like traits, and highlights the importance of considering the full trait continuum in future work.

The First Time Ever I Saw Your Feet: Inversion Effect in Newborns' Sensitivity to Biological Motion

Science.gov (United States)

Bardi, Lara; Regolin, Lucia; Simion, Francesca

2014-01-01

Inversion effect in biological motion perception has been recently attributed to an innate sensitivity of the visual system to the gravity-dependent dynamic of the motion. However, the specific cues that determine the inversion effect in naïve subjects were never investigated. In the present study, we have assessed the contribution of the local…

Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition.

Science.gov (United States)

Scherf, K Suzanne; Elbich, Daniel B; Motta-Mena, Natalie V

2017-01-01

There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women.

Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition

Science.gov (United States)

2017-01-01

Abstract There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women. PMID:28497111

Why the long face? The importance of vertical image structure for biological "barcodes" underlying face recognition.

Science.gov (United States)

Spence, Morgan L; Storrs, Katherine R; Arnold, Derek H

2014-07-29

Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition is supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis-a biological facial "barcode" (Dakin & Watt, 2009). A critical prediction of the facial barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distributions along the horizontal axis. Using a novel paradigm involving dynamic image distortions, a series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axes. Results show that disrupting the image distribution along the vertical image axis is more disruptive for recognition than matched distortions along the horizontal axis. Consistent with the facial barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. © 2014 ARVO.

Hybrid gesture recognition system for short-range use

Science.gov (United States)

Minagawa, Akihiro; Fan, Wei; Katsuyama, Yutaka; Takebe, Hiroaki; Ozawa, Noriaki; Hotta, Yoshinobu; Sun, Jun

2012-03-01

In recent years, various gesture recognition systems have been studied for use in television and video games[1]. In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning popularity of small mobile displays, gesture recognition systems capable of operating at much shorter ranges have become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is unknown to the user during operation, which imposes several restrictions on his/her actions. To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and motion analysis using image frame differences (motion-based approach)(for example, see[4]). We evaluated this system using sample users and classified the resulting errors into three categories: errors that depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture recognition systems.

Structural Motion Grammar for Universal Use of Leap Motion: Amusement and Functional Contents Focused

Directory of Open Access Journals (Sweden)

Byungseok Lee

2018-01-01

Full Text Available Motions using Leap Motion controller are not standardized while the use of it is spreading in media contents. Each content defines its own motions, thereby creating confusion for users. Therefore, to alleviate user inconvenience, this study categorized the commonly used motion by Amusement and Functional Contents and defined the Structural Motion Grammar that can be universally used based on the classification. To this end, the Motion Lexicon was defined, which is a fundamental motion vocabulary, and an algorithm that enables real-time recognition of Structural Motion Grammar was developed. Moreover, the proposed method was verified by user evaluation and quantitative comparison tests.

Effects of walker gender and observer gender on biological motion walking direction discrimination.

Science.gov (United States)

Yang, Xiaoying; Cai, Peng; Jiang, Yi

2014-09-01

The ability to recognize the movements of other biological entities, such as whether a person is walking toward you, is essential for survival and social interaction. Previous studies have shown that the visual system is particularly sensitive to approaching biological motion. In this study, we examined whether the gender of walkers and observers influenced the walking direction discrimination of approaching point-light walkers in fine granularity. The observers were presented a walker who walked in different directions and were asked to quickly judge the walking direction (left or right). The results showed that the observers demonstrated worse direction discrimination when the walker was depicted as male than when the walker was depicted as female, probably because the observers tended to perceive the male walkers as walking straight ahead. Intriguingly, male observers performed better than female observers at judging the walking directions of female walkers but not those of male walkers, a result indicating perceptual advantage with evolutionary significance. These findings provide strong evidence that the gender of walkers and observers modulates biological motion perception and that an adaptive perceptual mechanism exists in the visual system to facilitate the survival of social organisms. © 2014 The Institute of Psychology, Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd.

Action Recognition Using Motion Primitives and Probabilistic Edit Distance

DEFF Research Database (Denmark)

Fihl, Preben; Holte, Michael Boelstoft; Moeslund, Thomas B.

2006-01-01

In this paper we describe a recognition approach based on the notion of primitives. As opposed to recognizing actions based on temporal trajectories or temporal volumes, primitive-based recognition is based on representing a temporal sequence containing an action by only a few characteristic time...... into a string containing a sequence of symbols, each representing a primitives. After pruning the string a probabilistic Edit Distance classifier is applied to identify which action best describes the pruned string. The approach is evaluated on five one-arm gestures and the recognition rate is 91...

Lack of visual orienting to biological motion and audiovisual synchrony in 3-year-olds with autism.

Directory of Open Access Journals (Sweden)

Terje Falck-Ytter

Full Text Available It has been suggested that children with autism orient towards audiovisual synchrony (AVS rather than biological motion and that the opposite pattern is to be expected in typical development. Here, we challenge this notion by showing that 3-year-old neurotypical children orient to AVS and to biological motion in point-light displays but that 3-year-old children with autism orient to neither of these types of information. Thus, our data suggest that two fundamental mechanisms are disrupted in young children with autism: one that supports orienting towards others' movements and one that supports orienting towards multimodally specified events. These impairments may have consequences for socio-cognitive development and brain organization.

Entropy in molecular recognition by proteins.

Science.gov (United States)

Caro, José A; Harpole, Kyle W; Kasinath, Vignesh; Lim, Jackwee; Granja, Jeffrey; Valentine, Kathleen G; Sharp, Kim A; Wand, A Joshua

2017-06-20

Molecular recognition by proteins is fundamental to molecular biology. Dissection of the thermodynamic energy terms governing protein-ligand interactions has proven difficult, with determination of entropic contributions being particularly elusive. NMR relaxation measurements have suggested that changes in protein conformational entropy can be quantitatively obtained through a dynamical proxy, but the generality of this relationship has not been shown. Twenty-eight protein-ligand complexes are used to show a quantitative relationship between measures of fast side-chain motion and the underlying conformational entropy. We find that the contribution of conformational entropy can range from favorable to unfavorable, which demonstrates the potential of this thermodynamic variable to modulate protein-ligand interactions. For about one-quarter of these complexes, the absence of conformational entropy would render the resulting affinity biologically meaningless. The dynamical proxy for conformational entropy or "entropy meter" also allows for refinement of the contributions of solvent entropy and the loss in rotational-translational entropy accompanying formation of high-affinity complexes. Furthermore, structure-based application of the approach can also provide insight into long-lived specific water-protein interactions that escape the generic treatments of solvent entropy based simply on changes in accessible surface area. These results provide a comprehensive and unified view of the general role of entropy in high-affinity molecular recognition by proteins.

Undergraduate Labs for Biological Physics: Brownian Motion and Optical Trapping

Science.gov (United States)

Chu, Kelvin; Laughney, A.; Williams, J.

2006-12-01

We describe a set of case-study driven labs for an upper-division biological physics course. These labs are motivated by case-studies and consist of inquiry-driven investigations of Brownian motion and optical-trapping experiments. Each lab incorporates two innovative educational techniques to drive the process and application aspects of scientific learning. Case studies are used to encourage students to think independently and apply the scientific method to a novel lab situation. Student input from this case study is then used to decide how to best do the measurement, guide the project and ultimately evaluate the success of the program. Where appropriate, visualization and simulation using VPython is used. Direct visualization of Brownian motion allows students to directly calculate Avogadro's number or the Boltzmann constant. Following case-study driven discussion, students use video microscopy to measure the motion of latex spheres in different viscosity fluids arrive at a good approximation of NA or kB. Optical trapping (laser tweezer) experiments allow students to investigate the consequences of 100-pN forces on small particles. The case study consists of a discussion of the Boltzmann distribution and equipartition theorem followed by a consideration of the shape of the potential. Students can then use video capture to measure the distribution of bead positions to determine the shape and depth of the trap. This work supported by NSF DUE-0536773.

Non Audio-Video gesture recognition system

DEFF Research Database (Denmark)

Craciunescu, Razvan; Mihovska, Albena Dimitrova; Kyriazakos, Sofoklis

2016-01-01

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current research focus includes on the emotion...... recognition from the face and hand gesture recognition. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors in order to design a low-cost gesture recognition device...

Integrated structural biology to unravel molecular mechanisms of protein-RNA recognition.

Science.gov (United States)

Schlundt, Andreas; Tants, Jan-Niklas; Sattler, Michael

2017-04-15

Recent advances in RNA sequencing technologies have greatly expanded our knowledge of the RNA landscape in cells, often with spatiotemporal resolution. These techniques identified many new (often non-coding) RNA molecules. Large-scale studies have also discovered novel RNA binding proteins (RBPs), which exhibit single or multiple RNA binding domains (RBDs) for recognition of specific sequence or structured motifs in RNA. Starting from these large-scale approaches it is crucial to unravel the molecular principles of protein-RNA recognition in ribonucleoprotein complexes (RNPs) to understand the underlying mechanisms of gene regulation. Structural biology and biophysical studies at highest possible resolution are key to elucidate molecular mechanisms of RNA recognition by RBPs and how conformational dynamics, weak interactions and cooperative binding contribute to the formation of specific, context-dependent RNPs. While large compact RNPs can be well studied by X-ray crystallography and cryo-EM, analysis of dynamics and weak interaction necessitates the use of solution methods to capture these properties. Here, we illustrate methods to study the structure and conformational dynamics of protein-RNA complexes in solution starting from the identification of interaction partners in a given RNP. Biophysical and biochemical techniques support the characterization of a protein-RNA complex and identify regions relevant in structural analysis. Nuclear magnetic resonance (NMR) is a powerful tool to gain information on folding, stability and dynamics of RNAs and characterize RNPs in solution. It provides crucial information that is complementary to the static pictures derived from other techniques. NMR can be readily combined with other solution techniques, such as small angle X-ray and/or neutron scattering (SAXS/SANS), electron paramagnetic resonance (EPR), and Förster resonance energy transfer (FRET), which provide information about overall shapes, internal domain

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

Science.gov (United States)

Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

2017-01-01

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm

Directory of Open Access Journals (Sweden)

Simon Fong

2015-01-01

Full Text Available Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.

Object feature extraction and recognition model

International Nuclear Information System (INIS)

Wan Min; Xiang Rujian; Wan Yongxing

2001-01-01

The characteristics of objects, especially flying objects, are analyzed, which include characteristics of spectrum, image and motion. Feature extraction is also achieved. To improve the speed of object recognition, a feature database is used to simplify the data in the source database. The feature vs. object relationship maps are stored in the feature database. An object recognition model based on the feature database is presented, and the way to achieve object recognition is also explained

Reverse control for humanoid robot task recognition.

Science.gov (United States)

Hak, Sovannara; Mansard, Nicolas; Stasse, Olivier; Laumond, Jean Paul

2012-12-01

Efficient methods to perform motion recognition have been developed using statistical tools. Those methods rely on primitive learning in a suitable space, for example, the latent space of the joint angle and/or adequate task spaces. Learned primitives are often sequential: A motion is segmented according to the time axis. When working with a humanoid robot, a motion can be decomposed into parallel subtasks. For example, in a waiter scenario, the robot has to keep some plates horizontal with one of its arms while placing a plate on the table with its free hand. Recognition can thus not be limited to one task per consecutive segment of time. The method presented in this paper takes advantage of the knowledge of what tasks the robot is able to do and how the motion is generated from this set of known controllers, to perform a reverse engineering of an observed motion. This analysis is intended to recognize parallel tasks that have been used to generate a motion. The method relies on the task-function formalism and the projection operation into the null space of a task to decouple the controllers. The approach is successfully applied on a real robot to disambiguate motion in different scenarios where two motions look similar but have different purposes.

Breaking cover: neural responses to slow and fast camouflage-breaking motion.

Science.gov (United States)

Yin, Jiapeng; Gong, Hongliang; An, Xu; Chen, Zheyuan; Lu, Yiliang; Andolina, Ian M; McLoughlin, Niall; Wang, Wei

2015-08-22

Primates need to detect and recognize camouflaged animals in natural environments. Camouflage-breaking movements are often the only visual cue available to accomplish this. Specifically, sudden movements are often detected before full recognition of the camouflaged animal is made, suggesting that initial processing of motion precedes the recognition of motion-defined contours or shapes. What are the neuronal mechanisms underlying this initial processing of camouflaged motion in the primate visual brain? We investigated this question using intrinsic-signal optical imaging of macaque V1, V2 and V4, along with computer simulations of the neural population responses. We found that camouflaged motion at low speed was processed as a direction signal by both direction- and orientation-selective neurons, whereas at high-speed camouflaged motion was encoded as a motion-streak signal primarily by orientation-selective neurons. No population responses were found to be invariant to the camouflage contours. These results suggest that the initial processing of camouflaged motion at low and high speeds is encoded as direction and motion-streak signals in primate early visual cortices. These processes are consistent with a spatio-temporal filter mechanism that provides for fast processing of motion signals, prior to full recognition of camouflage-breaking animals. © 2015 The Authors.

Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control.

Science.gov (United States)

Scheme, Erik; Lock, Blair; Hargrove, Levi; Hill, Wendy; Kuruganti, Usha; Englehart, Kevin

2014-01-01

This paper describes two novel proportional control algorithms for use with pattern recognition-based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the user's usable dynamic range. Class-specific normalization parameters were calculated using data collected during classifier training and require no additional user action or configuration. The new control schemes were compared to the standard method of deriving proportional control using a one degree of freedom Fitts' law test for each of the wrist flexion/extension, wrist pronation/supination and hand close/open degrees of freedom. Performance was evaluated using the Fitts' law throughput value as well as more descriptive metrics including path efficiency, overshoot, stopping distance and completion rate. The proposed normalization methods significantly outperformed the incumbent method in every performance category for able bodied subjects (p < 0.001) and nearly every category for amputee subjects. Furthermore, one proposed method significantly outperformed both other methods in throughput (p < 0.0001), yielding 21% and 40% improvement over the incumbent method for amputee and able bodied subjects, respectively. The proposed control schemes represent a computationally simple method of fundamentally improving myoelectric control users' ability to elicit robust, and controlled, proportional velocity commands.

Dance-the-Music: an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

Science.gov (United States)

Maes, Pieter-Jan; Amelynck, Denis; Leman, Marc

2012-12-01

In this article, a computational platform is presented, entitled "Dance-the-Music", that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers' models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method-can determine the quality of a student's performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures.

A Biological Micro Actuator: Graded and Closed-Loop Control of Insect Leg Motion by Electrical Stimulation of Muscles

Science.gov (United States)

Cao, Feng; Zhang, Chao; Vo Doan, Tat Thang; Li, Yao; Sangi, Daniyal Haider; Koh, Jie Sheng; Huynh, Ngoc Anh; Aziz, Mohamed Fareez Bin; Choo, Hao Yu; Ikeda, Kazuo; Abbeel, Pieter; Maharbiz, Michel M.; Sato, Hirotaka

2014-01-01

In this study, a biological microactuator was demonstrated by closed-loop motion control of the front leg of an insect (Mecynorrhina torquata, beetle) via electrical stimulation of the leg muscles. The three antagonistic pairs of muscle groups in the front leg enabled the actuator to have three degrees of freedom: protraction/retraction, levation/depression, and extension/flexion. We observed that the threshold amplitude (voltage) required to elicit leg motions was approximately 1.0 V; thus, we fixed the stimulation amplitude at 1.5 V to ensure a muscle response. The leg motions were finely graded by alternation of the stimulation frequencies: higher stimulation frequencies elicited larger leg angular displacement. A closed-loop control system was then developed, where the stimulation frequency was the manipulated variable for leg-muscle stimulation (output from the final control element to the leg muscle) and the angular displacement of the leg motion was the system response. This closed-loop control system, with an optimized proportional gain and update time, regulated the leg to set at predetermined angular positions. The average electrical stimulation power consumption per muscle group was 148 µW. These findings related to and demonstrations of the leg motion control offer promise for the future development of a reliable, low-power, biological legged machine (i.e., an insect–machine hybrid legged robot). PMID:25140875

A biological micro actuator: graded and closed-loop control of insect leg motion by electrical stimulation of muscles.

Directory of Open Access Journals (Sweden)

Feng Cao

Full Text Available In this study, a biological microactuator was demonstrated by closed-loop motion control of the front leg of an insect (Mecynorrhina torquata, beetle via electrical stimulation of the leg muscles. The three antagonistic pairs of muscle groups in the front leg enabled the actuator to have three degrees of freedom: protraction/retraction, levation/depression, and extension/flexion. We observed that the threshold amplitude (voltage required to elicit leg motions was approximately 1.0 V; thus, we fixed the stimulation amplitude at 1.5 V to ensure a muscle response. The leg motions were finely graded by alternation of the stimulation frequencies: higher stimulation frequencies elicited larger leg angular displacement. A closed-loop control system was then developed, where the stimulation frequency was the manipulated variable for leg-muscle stimulation (output from the final control element to the leg muscle and the angular displacement of the leg motion was the system response. This closed-loop control system, with an optimized proportional gain and update time, regulated the leg to set at predetermined angular positions. The average electrical stimulation power consumption per muscle group was 148 µW. These findings related to and demonstrations of the leg motion control offer promise for the future development of a reliable, low-power, biological legged machine (i.e., an insect-machine hybrid legged robot.

Discriminative Vision-Based Recovery and Recognition of Human Motion

NARCIS (Netherlands)

Poppe, Ronald Walter

2009-01-01

The automatic analysis of human motion from images opens up the way for applications in the domains of security and surveillance, human-computer interaction, animation, retrieval and sports motion analysis. In this dissertation, the focus is on robust and fast human pose recovery and action

Weak Ergodicity Breaking of Receptor Motion in Living Cells Stemming from Random Diffusivity

Science.gov (United States)

Manzo, Carlo; Torreno-Pina, Juan A.; Massignan, Pietro; Lapeyre, Gerald J.; Lewenstein, Maciej; Garcia Parajo, Maria F.

2015-01-01

Molecular transport in living systems regulates numerous processes underlying biological function. Although many cellular components exhibit anomalous diffusion, only recently has the subdiffusive motion been associated with nonergodic behavior. These findings have stimulated new questions for their implications in statistical mechanics and cell biology. Is nonergodicity a common strategy shared by living systems? Which physical mechanisms generate it? What are its implications for biological function? Here, we use single-particle tracking to demonstrate that the motion of dendritic cell-specific intercellular adhesion molecule 3-grabbing nonintegrin (DC-SIGN), a receptor with unique pathogen-recognition capabilities, reveals nonergodic subdiffusion on living-cell membranes In contrast to previous studies, this behavior is incompatible with transient immobilization, and, therefore, it cannot be interpreted according to continuous-time random-walk theory. We show that the receptor undergoes changes of diffusivity, consistent with the current view of the cell membrane as a highly dynamic and diverse environment. Simulations based on a model of an ordinary random walk in complex media quantitatively reproduce all our observations, pointing toward diffusion heterogeneity as the cause of DC-SIGN behavior. By studying different receptor mutants, we further correlate receptor motion to its molecular structure, thus establishing a strong link between nonergodicity and biological function. These results underscore the role of disorder in cell membranes and its connection with function regulation. Because of its generality, our approach offers a framework to interpret anomalous transport in other complex media where dynamic heterogeneity might play a major role, such as those found, e.g., in soft condensed matter, geology, and ecology.

ALPHABET SIGN LANGUAGE RECOGNITION USING LEAP MOTION TECHNOLOGY AND RULE BASED BACKPROPAGATION-GENETIC ALGORITHM NEURAL NETWORK (RBBPGANN

Directory of Open Access Journals (Sweden)

Wijayanti Nurul Khotimah

2017-01-01

Full Text Available Sign Language recognition was used to help people with normal hearing communicate effectively with the deaf and hearing-impaired. Based on survey that conducted by Multi-Center Study in Southeast Asia, Indonesia was on the top four position in number of patients with hearing disability (4.6%. Therefore, the existence of Sign Language recognition is important. Some research has been conducted on this field. Many neural network types had been used for recognizing many kinds of sign languages. However, their performance are need to be improved. This work focuses on the ASL (Alphabet Sign Language in SIBI (Sign System of Indonesian Language which uses one hand and 26 gestures. Here, thirty four features were extracted by using Leap Motion. Further, a new method, Rule Based-Backpropagation Genetic Al-gorithm Neural Network (RB-BPGANN, was used to recognize these Sign Languages. This method is combination of Rule and Back Propagation Neural Network (BPGANN. Based on experiment this pro-posed application can recognize Sign Language up to 93.8% accuracy. It was very good to recognize large multiclass instance and can be solution of overfitting problem in Neural Network algorithm.

Improved motion description for action classification

Directory of Open Access Journals (Sweden)

Mihir eJain

2016-01-01

Full Text Available Even though the importance of explicitly integrating motion characteristics in video descriptions has been demonstrated by several recent papers on action classification, our current work concludes that adequately decomposing visual motion into dominant and residual motions, i.e.: camera and scene motion, significantly improves action recognition algorithms. This holds true both for the extraction of the space-time trajectories and for computation of descriptors.We designed a new motion descriptor – the DCS descriptor – that captures additional information on local motion patterns enhancing results based on differential motion scalar quantities, divergence, curl and shear features. Finally, applying the recent VLAD coding technique proposed in image retrieval provides a substantial improvement for action recognition. These findings are complementary to each other and they outperformed all previously reported results by a significant margin on three challenging datasets: Hollywood 2, HMDB51 and Olympic Sports as reported in (Jain et al. (2013. These results were further improved by (Oneata et al. (2013; Wang and Schmid (2013; Zhu et al. (2013 through the use of the Fisher vector encoding. We therefore also employ Fisher vector in this paper and we further enhance our approach by combining trajectories from both optical flow and compensated flow. We as well provide additional details of DCS descriptors, including visualization. For extending the evaluation, a novel dataset with 101 action classes, UCF101, was added.

Perceived health from biological motion predicts voting behaviour.

Science.gov (United States)

Kramer, Robin S S; Arend, Isabel; Ward, Robert

2010-04-01

Body motion signals socially relevant traits like the sex, age, and even the genetic quality of actors and may therefore facilitate various social judgements. By examining ratings and voting decisions based solely on body motion of political candidates, we considered how the candidates' motion affected people's judgements and voting behaviour. In two experiments, participants viewed stick figure motion displays made from videos of politicians in public debate. Participants rated the motion displays for a variety of social traits and then indicated their vote preference. In both experiments, perceived physical health was the single best predictor of vote choice, and no two-factor model produced significant improvement. Notably, although attractiveness and leadership correlated with voting behaviour, neither provided additional explanatory power to a single-factor model of health alone. Our results demonstrate for the first time that motion can produce systematic vote preferences.

Simultaneous topography and recognition imaging: physical aspects and optimal imaging conditions

International Nuclear Information System (INIS)

Preiner, Johannes; Ebner, Andreas; Zhu Rong; Hinterdorfer, Peter; Chtcheglova, Lilia

2009-01-01

Simultaneous topography and recognition imaging (TREC) allows for the investigation of receptor distributions on natural biological surfaces under physiological conditions. Based on atomic force microscopy (AFM) in combination with a cantilever tip carrying a ligand molecule, it enables us to sense topography and recognition of receptor molecules simultaneously with nanometre accuracy. In this study we introduce optimized handling conditions and investigate the physical properties of the cantilever-tip-sample ensemble, which is essential for the interpretation of the experimental data gained from this technique. In contrast to conventional AFM methods, TREC is based on a more sophisticated feedback loop, which enables us to discriminate topographical contributions from recognition events in the AFM cantilever motion. The features of this feedback loop were investigated through a detailed analysis of the topography and recognition data obtained on a model protein system. Single avidin molecules immobilized on a mica substrate were imaged with an AFM tip functionalized with a biotinylated IgG. A simple procedure for adjusting the optimal amplitude for TREC imaging is described by exploiting the sharp localization of the TREC signal within a small range of oscillation amplitudes. This procedure can also be used for proving the specificity of the detected receptor-ligand interactions. For understanding and eliminating topographical crosstalk in the recognition images we developed a simple theoretical model, which nicely explains its origin and its dependence on the excitation frequency.

Dynamic simulation and modeling of the motion modes produced during the 3D controlled manipulation of biological micro/nanoparticles based on the AFM.

Science.gov (United States)

Saraee, Mahdieh B; Korayem, Moharam H

2015-08-07

Determining the motion modes and the exact position of a particle displaced during the manipulation process is of special importance. This issue becomes even more important when the studied particles are biological micro/nanoparticles and the goals of manipulation are the transfer of these particles within body cells, repair of cancerous cells and the delivery of medication to damaged cells. However, due to the delicate nature of biological nanoparticles and their higher vulnerability, by obtaining the necessary force of manipulation for the considered motion mode, we can prevent the sample from interlocking with or sticking to the substrate because of applying a weak force or avoid damaging the sample due to the exertion of excessive force. In this paper, the dynamic behaviors and the motion modes of biological micro/nanoparticles such as DNA, yeast, platelet and bacteria due to the 3D manipulation effect have been investigated. Since the above nanoparticles generally have a cylindrical shape, the cylindrical contact models have been employed in an attempt to more precisely model the forces exerted on the nanoparticle during the manipulation process. Also, this investigation has performed a comprehensive modeling and simulation of all the possible motion modes in 3D manipulation by taking into account the eccentricity of the applied load on the biological nanoparticle. The obtained results indicate that unlike the macroscopic scale, the sliding of nanoparticle on substrate in nano-scale takes place sooner than the other motion modes and that the spinning about the vertical and transverse axes and the rolling of nanoparticle occur later than the other motion modes. The simulation results also indicate that the applied force necessary for the onset of nanoparticle movement and the resulting motion mode depend on the size and aspect ratio of the nanoparticle. Copyright © 2015 Elsevier Ltd. All rights reserved.

Probabilistic recognition of human faces from video

DEFF Research Database (Denmark)

Zhou, Saohua; Krüger, Volker; Chellappa, Rama

2003-01-01

Recognition of human faces using a gallery of still or video images and a probe set of videos is systematically investigated using a probabilistic framework. In still-to-video recognition, where the gallery consists of still images, a time series state space model is proposed to fuse temporal...... of the identity variable produces the recognition result. The model formulation is very general and it allows a variety of image representations and transformations. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach for both still-to-video and video-to-video...... information in a probe video, which simultaneously characterizes the kinematics and identity using a motion vector and an identity variable, respectively. The joint posterior distribution of the motion vector and the identity variable is estimated at each time instant and then propagated to the next time...

Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

Science.gov (United States)

Yurtman, Aras; Barshan, Billur

2017-08-09

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage.

A Biological Micro Actuator: Graded and Closed-Loop Control of Insect Leg Motion by Electrical Stimulation of Muscles

OpenAIRE

Cao, Feng; Zhang, Chao; Vo Doan, Tat Thang; Li, Yao; Sangi, Daniyal Haider; Koh, Jie Sheng; Huynh, Ngoc Anh; Aziz, Mohamed Fareez Bin; Choo, Hao Yu; Ikeda, Kazuo; Abbeel, Pieter; Maharbiz, Michel M.; Sato, Hirotaka

2014-01-01

In this study, a biological microactuator was demonstrated by closed-loop motion control of the front leg of an insect (Mecynorrhina torquata, beetle) via electrical stimulation of the leg muscles. The three antagonistic pairs of muscle groups in the front leg enabled the actuator to have three degrees of freedom: protraction/retraction, levation/depression, and extension/flexion. We observed that the threshold amplitude (voltage) required to elicit leg motions was approximately 1.0 V; thus, ...

A Kinect based sign language recognition system using spatio-temporal features

Science.gov (United States)

Memiş, Abbas; Albayrak, Songül

2013-12-01

This paper presents a sign language recognition system that uses spatio-temporal features on RGB video images and depth maps for dynamic gestures of Turkish Sign Language. Proposed system uses motion differences and accumulation approach for temporal gesture analysis. Motion accumulation method, which is an effective method for temporal domain analysis of gestures, produces an accumulated motion image by combining differences of successive video frames. Then, 2D Discrete Cosine Transform (DCT) is applied to accumulated motion images and temporal domain features transformed into spatial domain. These processes are performed on both RGB images and depth maps separately. DCT coefficients that represent sign gestures are picked up via zigzag scanning and feature vectors are generated. In order to recognize sign gestures, K-Nearest Neighbor classifier with Manhattan distance is performed. Performance of the proposed sign language recognition system is evaluated on a sign database that contains 1002 isolated dynamic signs belongs to 111 words of Turkish Sign Language (TSL) in three different categories. Proposed sign language recognition system has promising success rates.

Viewpoint Manifolds for Action Recognition

Directory of Open Access Journals (Sweden)

Souvenir Richard

2009-01-01

Full Text Available Abstract Action recognition from video is a problem that has many important applications to human motion analysis. In real-world settings, the viewpoint of the camera cannot always be fixed relative to the subject, so view-invariant action recognition methods are needed. Previous view-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available dataset previously only achieved using multiple simultaneous views.

Usage of stereoscopic visualization in the learning contents of rotational motion.

Science.gov (United States)

Matsuura, Shu

2013-01-01

Rotational motion plays an essential role in physics even at an introductory level. In addition, the stereoscopic display of three-dimensional graphics includes is advantageous for the presentation of rotational motions, particularly for depth recognition. However, the immersive visualization of rotational motion has been known to lead to dizziness and even nausea for some viewers. Therefore, the purpose of this study is to examine the onset of nausea and visual fatigue when learning rotational motion through the use of a stereoscopic display. The findings show that an instruction method with intermittent exposure of the stereoscopic display and a simplification of its visual components reduced the onset of nausea and visual fatigue for the viewers, which maintained the overall effect of instantaneous spatial recognition.

Recognition of dance-like actions: memory for static posture or dynamic movement?

Science.gov (United States)

Vicary, Staci A; Robbins, Rachel A; Calvo-Merino, Beatriz; Stevens, Catherine J

2014-07-01

Dance-like actions are complex visual stimuli involving multiple changes in body posture across time and space. Visual perception research has demonstrated a difference between the processing of dynamic body movement and the processing of static body posture. Yet, it is unclear whether this processing dissociation continues during the retention of body movement and body form in visual working memory (VWM). When observing a dance-like action, it is likely that static snapshot images of body posture will be retained alongside dynamic images of the complete motion. Therefore, we hypothesized that, as in perception, posture and movement would differ in VWM. Additionally, if body posture and body movement are separable in VWM, as form- and motion-based items, respectively, then differential interference from intervening form and motion tasks should occur during recognition. In two experiments, we examined these hypotheses. In Experiment 1, the recognition of postures and movements was tested in conditions in which the formats of the study and test stimuli matched (movement-study to movement-test, posture-study to posture-test) or mismatched (movement-study to posture-test, posture-study to movement-test). In Experiment 2, the recognition of postures and movements was compared after intervening form and motion tasks. These results indicated that (1) the recognition of body movement based only on posture is possible, but it is significantly poorer than recognition based on the entire movement stimulus, and (2) form-based interference does not impair memory for movements, although motion-based interference does. We concluded that, whereas static posture information is encoded during the observation of dance-like actions, body movement and body posture differ in VWM.

Load-sensitive impairment of working memory for biological motion in schizophrenia.

Science.gov (United States)

Lee, Hannah; Kim, Jejoong

2017-01-01

Impaired working memory (WM) is a core cognitive deficit in schizophrenia. Nevertheless, past studies have reported that patients may also benefit from increasing salience of memory stimuli. Such efficient encoding largely depends upon precise perception. Thus an investigation on the relationship between perceptual processing and WM would be worthwhile. Here, we used biological motion (BM), a socially relevant stimulus that schizophrenics have difficulty discriminating from similar meaningless motions, in a delayed-response task. Non-BM stimuli and static polygons were also used for comparison. In each trial, one of the three types of stimuli was presented followed by two probes, with a short delay in between. Participants were asked to indicate whether one of them was identical to the memory item or both were novel. The number of memory items was one or two. Healthy controls were more accurate in recognizing BM than non-BM regardless of memory loads. Patients with schizophrenia exhibited similar accuracy patterns to those of controls in the Load 1 condition only. These results suggest that information contained in BM could facilitate WM encoding in general, but the effect is vulnerable to the increase of cognitive load in schizophrenia, implying inefficient encoding driven by imprecise perception.

Efficient Human Action and Gait Analysis Using Multiresolution Motion Energy Histogram

Directory of Open Access Journals (Sweden)

Kuo-Chin Fan

2010-01-01

Full Text Available Average Motion Energy (AME image is a good way to describe human motions. However, it has to face the computation efficiency problem with the increasing number of database templates. In this paper, we propose a histogram-based approach to improve the computation efficiency. We convert the human action/gait recognition problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multiresolution structure on the Motion Energy Histogram (MEH. To utilize the multiresolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins, which is much less than the AME method. Two applications, action recognition and gait classification, are conducted in the experiments to demonstrate the feasibility and validity of the proposed approach.

He throws like a girl (but only when he's sad): emotion affects sex-decoding of biological motion displays.

Science.gov (United States)

Johnson, Kerri L; McKay, Lawrie S; Pollick, Frank E

2011-05-01

Gender stereotypes have been implicated in sex-typed perceptions of facial emotion. Such interpretations were recently called into question because facial cues of emotion are confounded with sexually dimorphic facial cues. Here we examine the role of visual cues and gender stereotypes in perceptions of biological motion displays, thus overcoming the morphological confounding inherent in facial displays. In four studies, participants' judgments revealed gender stereotyping. Observers accurately perceived emotion from biological motion displays (Study 1), and this affected sex categorizations. Angry displays were overwhelmingly judged to be men; sad displays were judged to be women (Studies 2-4). Moreover, this pattern remained strong when stimuli were equated for velocity (Study 3). We argue that these results were obtained because perceivers applied gender stereotypes of emotion to infer sex category (Study 4). Implications for both vision sciences and social psychology are discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Human NKG2D-ligands: cell biology strategies to ensure immune recognition

Directory of Open Access Journals (Sweden)

Lola eFernández-Messina

2012-09-01

Full Text Available Immune recognition mediated by the activating receptor NKG2D plays an important role for the elimination of stressed cells, including tumours and virus-infected cells. On the other hand, the ligands for NKG2D can also be shed into the sera of cancer patients where they weaken the immune response by downmodulating the receptor on effector cells, mainly NK and T cells. Although both families of NKG2D-ligands, MICA/B and ULBPs, are related to MHC molecules and their expression is increased after stress, many differences are observed in terms of their biochemical properties and cell trafficking. In this paper, we summarise the variety of NKG2D-ligands and propose that selection pressure has driven evolution of diversity in their trafficking and shedding, but not receptor binding affinity. However, it is also possible to identify functional properties common to individual ULBP molecules and MICA/B alleles, but not generally conserved within the MIC or ULBP families. These characteristics likely represent examples of convergent evolution for efficient immune recognition, but are also attractive targets for pathogen immune evasion strategies. Categorization of NKG2D-ligands according to their biological features, rather than their genetic family, may help to achieve a better understanding of NKG2D-ligand association with disease.

Visual recognition and tracking of objects for robot sensing

International Nuclear Information System (INIS)

Lowe, D.G.

1994-01-01

An overview is presented of a number of techniques used for recognition and motion tracking of articulated 3-D objects. With recent advances in robust methods for model-based vision and improved performance of computer systems, it will soon be possible to build low-cost, high-reliability systems for model-based motion tracking. Such systems can be expected to open up a wide range of applications in robotics by providing machines with real-time information about their environment. This paper describes a number of techniques for efficiently matching parameterized 3-D models to image features. The matching methods are robust with respect to missing and ambiguous features as well as measurement errors. Unlike most previous work on model-based motion tracking, this system provides for the integrated treatment of matching and measurement errors during motion tracking. The initial application is in a system for real-time motion tracking of articulated 3-D objects. With the future addition of an indexing component, these same techniques can also be used for general model-based recognition. The current real-time implementation is based on matching straight line segments, but some preliminary experiments on matching arbitrary curves are also described. (author)

Further explorations of the facing bias in biological motion perception: perspective cues, observer sex, and response times.

Directory of Open Access Journals (Sweden)

Ben Schouten

Full Text Available The human visual system has evolved to be highly sensitive to visual information about other persons and their movements as is illustrated by the effortless perception of point-light figures or 'biological motion'. When presented orthographically, a point-light walker is interpreted in two anatomically plausible ways: As 'facing the viewer' or as 'facing away' from the viewer. However, human observers show a 'facing bias': They perceive such a point-light walker as facing towards them in about 70-80% of the cases. In studies exploring the role of social and biological relevance as a possible account for the facing bias, we found a 'figure gender effect': Male point-light figures elicit a stronger facing bias than female point-light figures. Moreover, we also found an 'observer gender effect': The 'figure gender effect' was stronger for male than for female observers. In the present study we presented to 11 males and 11 females point-light walkers of which, very subtly, the perspective information was manipulated by modifying the earlier reported 'perspective technique'. Proportions of 'facing the viewer' responses and reaction times were recorded. Results show that human observers, even in the absence of local shape or size cues, easily pick up on perspective cues, confirming recent demonstrations of high visual sensitivity to cues on whether another person is potentially approaching. We also found a consistent difference in how male and female observers respond to stimulus variations (figure gender or perspective cues that cause variations in the perceived in-depth orientation of a point-light walker. Thus, the 'figure gender effect' is possibly caused by changes in the relative locations and motions of the dots that the perceptual system tends to interpret as perspective cues. Third, reaction time measures confirmed the existence of the facing bias and recent research showing faster detection of approaching than receding biological motion.

Motion Imitation and Recognition using Parametric Hidden Markov Models

DEFF Research Database (Denmark)

Herzog, Dennis; Ude, Ales; Krüger, Volker

2008-01-01

) are important. Only together they convey the whole meaning of an action. Similarly, to imitate a movement, the robot needs to select the proper action and parameterize it, e.g., by the relative position of the object that needs to be grasped. We propose to utilize parametric hidden Markov models (PHMMs), which...... extend the classical HMMs by introducing a joint parameterization of the observation densities, to simultaneously solve the problems of action recognition, parameterization of the observed actions, and action synthesis. The proposed approach was fully implemented on a humanoid robot HOAP-3. To evaluate...... the approach, we focused on reaching and pointing actions. Even though the movements are very similar in appearance, our approach is able to distinguish the two movement types and discover the parameterization, and is thus enabling both, action recognition and action synthesis. Through parameterization we...

Predictive Coding Strategies for Invariant Object Recognition and Volitional Motion Control in Neuromorphic Agents

Science.gov (United States)

2015-09-02

model for scene understanding was proposed based on deep convolutional neural networks to improve recognition accuracy. Facial expression recognition ...A deep-learning-based model for facial expression recognition was formulated. It could recognize emotional status of people regardless of...CVPRW), 2014 IEEE Conference on. IEEE, 2014. DISTRIBUTION A: Distribution approved for public release. 4 Facial Expression Recognition

Decoding facial expressions based on face-selective and motion-sensitive areas.

Science.gov (United States)

Liang, Yin; Liu, Baolin; Xu, Junhai; Zhang, Gaoyan; Li, Xianglin; Wang, Peiyuan; Wang, Bin

2017-06-01

Humans can easily recognize others' facial expressions. Among the brain substrates that enable this ability, considerable attention has been paid to face-selective areas; in contrast, whether motion-sensitive areas, which clearly exhibit sensitivity to facial movements, are involved in facial expression recognition remained unclear. The present functional magnetic resonance imaging (fMRI) study used multi-voxel pattern analysis (MVPA) to explore facial expression decoding in both face-selective and motion-sensitive areas. In a block design experiment, participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) in images, videos, and eyes-obscured videos. Due to the use of multiple stimulus types, the impacts of facial motion and eye-related information on facial expression decoding were also examined. It was found that motion-sensitive areas showed significant responses to emotional expressions and that dynamic expressions could be successfully decoded in both face-selective and motion-sensitive areas. Compared with static stimuli, dynamic expressions elicited consistently higher neural responses and decoding performance in all regions. A significant decrease in both activation and decoding accuracy due to the absence of eye-related information was also observed. Overall, the findings showed that emotional expressions are represented in motion-sensitive areas in addition to conventional face-selective areas, suggesting that motion-sensitive regions may also effectively contribute to facial expression recognition. The results also suggested that facial motion and eye-related information played important roles by carrying considerable expression information that could facilitate facial expression recognition. Hum Brain Mapp 38:3113-3125, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Sensing Movement: Microsensors for Body Motion Measurement

Directory of Open Access Journals (Sweden)

Hansong Zeng

2011-01-01

Full Text Available Recognition of body posture and motion is an important physiological function that can keep the body in balance. Man-made motion sensors have also been widely applied for a broad array of biomedical applications including diagnosis of balance disorders and evaluation of energy expenditure. This paper reviews the state-of-the-art sensing components utilized for body motion measurement. The anatomy and working principles of a natural body motion sensor, the human vestibular system, are first described. Various man-made inertial sensors are then elaborated based on their distinctive sensing mechanisms. In particular, both the conventional solid-state motion sensors and the emerging non solid-state motion sensors are depicted. With their lower cost and increased intelligence, man-made motion sensors are expected to play an increasingly important role in biomedical systems for basic research as well as clinical diagnostics.

Vision-Based Recognition of Activities by a Humanoid Robot

Directory of Open Access Journals (Sweden)

Mounîm A. El-Yacoubi

2015-12-01

Full Text Available We present an autonomous assistive robotic system for human activity recognition from video sequences. Due to the large variability inherent to video capture from a non-fixed robot (as opposed to a fixed camera, as well as the robot's limited computing resources, implementation has been guided by robustness to this variability and by memory and computing speed efficiency. To accommodate motion speed variability across users, we encode motion using dense interest point trajectories. Our recognition model harnesses the dense interest point bag-of-words representation through an intersection kernel-based SVM that better accommodates the large intra-class variability stemming from a robot operating in different locations and conditions. To contextually assess the engine as implemented in the robot, we compare it with the most recent approaches of human action recognition performed on public datasets (non-robot-based, including a novel approach of our own that is based on a two-layer SVM-hidden conditional random field sequential recognition model. The latter's performance is among the best within the recent state of the art. We show that our robot-based recognition engine, while less accurate than the sequential model, nonetheless shows good performances, especially given the adverse test conditions of the robot, relative to those of a fixed camera.

He Throws like a Girl (but Only when He's Sad): Emotion Affects Sex-Decoding of Biological Motion Displays

Science.gov (United States)

Johnson, Kerri L.; McKay, Lawrie S.; Pollick, Frank E.

2011-01-01

Gender stereotypes have been implicated in sex-typed perceptions of facial emotion. Such interpretations were recently called into question because facial cues of emotion are confounded with sexually dimorphic facial cues. Here we examine the role of visual cues and gender stereotypes in perceptions of biological motion displays, thus overcoming…

Gender differences in the relationship between social communication and emotion recognition.

Science.gov (United States)

Kothari, Radha; Skuse, David; Wakefield, Justin; Micali, Nadia

2013-11-01

To investigate the association between autistic traits and emotion recognition in a large community sample of children using facial and social motion cues, additionally stratifying by gender. A general population sample of 3,666 children from the Avon Longitudinal Study of Parents and Children (ALSPAC) were assessed on their ability to correctly recognize emotions using the faces subtest of the Diagnostic Analysis of Non-Verbal Accuracy, and the Emotional Triangles Task, a novel test assessing recognition of emotion from social motion cues. Children with autistic-like social communication difficulties, as assessed by the Social Communication Disorders Checklist, were compared with children without such difficulties. Autistic-like social communication difficulties were associated with poorer recognition of emotion from social motion cues in both genders, but were associated with poorer facial emotion recognition in boys only (odds ratio = 1.9, 95% CI = 1.4, 2.6, p = .0001). This finding must be considered in light of lower power to detect differences in girls. In this community sample of children, greater deficits in social communication skills are associated with poorer discrimination of emotions, implying there may be an underlying continuum of liability to the association between these characteristics. As a similar degree of association was observed in both genders on a novel test of social motion cues, the relatively good performance of girls on the more familiar task of facial emotion discrimination may be due to compensatory mechanisms. Our study might indicate the existence of a cognitive process by which girls with underlying autistic traits can compensate for their covert deficits in emotion recognition, although this would require further investigation. Copyright © 2013 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Galileo and the Problems of Motion

Science.gov (United States)

Hooper, Wallace Edd

Galileo's science of motion changed natural philosophy. His results initiated a broad human awakening to the intricate new world of physical order found in the midst of familiar operations of nature. His thinking was always based squarely on the academic traditions of the spiritual old world. He advanced physics by new standards of judgment drawn from mechanics and geometry, and disciplined observation of the world. My study first determines the order of composition of the earliest essays on motion and physics, ca. 1588 -1592, from internal evidence, and bibliographic evidence. There are clear signs of a Platonist critique of Aristotle, supported by Archimedes, in the Ten Section Version of On Motion, written ca. 1588, and probably the earliest of his treatises on motion or physics. He expanded upon his opening Platonic -Archimedean position by investigating the ideas of scholastic critics of Aristotle, including the Doctores Parisienses, found in his readings of the Jesuit Professors at the Collegio Romano. Their influences surfaced clearly in Galileo's Memoranda on Motion and the Dialogue on Motion, and in On Motion, which followed, ca. 1590-1592. At the end of his sojourn in Pisa, Galileo opened the road to the new physics by solving an important problem in the mechanics of Pappus, concerning motion along inclined planes. My study investigates why Galileo gave up attempts to establish a ratio between speed and weight, and why he began to seek the ratios of time and distance and speed, by 1602. It also reconstructs Galileo's development of the 1604 principle, seeking to outline its invention, elaboration, and abandonment. Then, I try to show that we have a record of Galileo's moment of recognition of the direct relation between the time of fall and the accumulated speed of motion--that great affinity between time and motion and the key to the new science of motion established before 1610. Evidence also ties the discovery of the time affinity directly to Galileo

Micro Expression Recognition Using the Eulerian Video Magnification Method

Directory of Open Access Journals (Sweden)

Elham Zarezadeh

2016-08-01

Full Text Available In this paper we propose a new approach for facial micro expressions recognition. For this purpose the Eulerian Video Magnification (EVM method is used to retrieve the subtle motions of the face. The results of this method are obtained as in the magnified images sequence. In this study the numerical tests are performed on two databases: Spontaneous Micro expression (SMIC and Category and Sourcing Managers Executive (CASME. We evaluate our proposed method in two phases using the eigenface method. In phase 1 we recognize the type of a micro expression, for example emotional versus unemotional in SMIC database. Phase 2 classifies the recognized micro expression as negative versus positive in SMIC database and happiness versus disgust in CASME database. The results show that the eigenface method by the EVM method for the retrieval of subtle motions of the face increases the performance of micro expression recognition. Moreover, the proposed approach is more accurate and promising than the previous works in micro expressions recognition.

The Relative Importance of Spatial Versus Temporal Structure in the Perception of Biological Motion: An Event-Related Potential Study

Science.gov (United States)

Hirai, Masahiro; Hiraki, Kazuo

2006-01-01

We investigated how the spatiotemporal structure of animations of biological motion (BM) affects brain activity. We measured event-related potentials (ERPs) during the perception of BM under four conditions: normal spatial and temporal structure; scrambled spatial and normal temporal structure; normal spatial and scrambled temporal structure; and…

Micro-Doppler Feature Extraction and Recognition Based on Netted Radar for Ballistic Targets

Directory of Open Access Journals (Sweden)

Feng Cun-qian

2015-12-01

Full Text Available This study examines the complexities of using netted radar to recognize and resolve ballistic midcourse targets. The application of micro-motion feature extraction to ballistic mid-course targets is analyzed, and the current status of application and research on micro-motion feature recognition is concluded for singlefunction radar networks such as low- and high-resolution imaging radar networks. Advantages and disadvantages of these networks are discussed with respect to target recognition. Hybrid-mode radar networks combine low- and high-resolution imaging radar and provide a specific reference frequency that is the basis for ballistic target recognition. Main research trends are discussed for hybrid-mode networks that apply micromotion feature extraction to ballistic mid-course targets.

Automatic Video-based Analysis of Human Motion

DEFF Research Database (Denmark)

Fihl, Preben

The human motion contains valuable information in many situations and people frequently perform an unconscious analysis of the motion of other people to understand their actions, intentions, and state of mind. An automatic analysis of human motion will facilitate many applications and thus has...... received great interest from both industry and research communities. The focus of this thesis is on video-based analysis of human motion and the thesis presents work within three overall topics, namely foreground segmentation, action recognition, and human pose estimation. Foreground segmentation is often...... the first important step in the analysis of human motion. By separating foreground from background the subsequent analysis can be focused and efficient. This thesis presents a robust background subtraction method that can be initialized with foreground objects in the scene and is capable of handling...

Semantic Models of Sentences with Verbs of Motion in Standard Language and in Scientific Language Used in Biology

Directory of Open Access Journals (Sweden)

Vita Banionytė

2016-06-01

Full Text Available The semantic models of sentences with verbs of motion in German standard language and in scientific language used in biology are analyzed in the article. In its theoretic part it is affirmed that the article is based on the semantic theory of the sentence. This theory, in its turn, is grounded on the correlation of semantic predicative classes and semantic roles. The combination of semantic predicative classes and semantic roles is expressed by the main semantic formula – proposition. In its practical part the differences between the semantic models of standard and scientific language used in biology are explained. While modelling sentences with verbs of motion, two groups of semantic models of sentences are singled out: that of action (Handlung and process (Vorgang. The analysis shows that the semantic models of sentences with semantic action predicatives dominate in the text of standard language while the semantic models of sentences with semantic process predicatives dominate in the texts of scientific language used in biology. The differences how the doer and direction are expressed in standard and in scientific language are clearly seen and the semantic cases (Agens, Patiens, Direktiv1 help to determine that. It is observed that in scientific texts of high level of specialization (biology science in contrast to popular scientific literature models of sentences with moving verbs are usually seldom found. They are substituted by denominative constructions. In conclusions it is shown that this analysis can be important in methodics, especially planning material for teaching professional-scientific language.

Influence of oxytocin on emotion recognition from body language: A randomized placebo-controlled trial.

Science.gov (United States)

Bernaerts, Sylvie; Berra, Emmely; Wenderoth, Nicole; Alaerts, Kaat

2016-10-01

The neuropeptide 'oxytocin' (OT) is known to play a pivotal role in a variety of complex social behaviors by promoting a prosocial attitude and interpersonal bonding. One mechanism by which OT is hypothesized to promote prosocial behavior is by enhancing the processing of socially relevant information from the environment. With the present study, we explored to what extent OT can alter the 'reading' of emotional body language as presented by impoverished biological motion point light displays (PLDs). To do so, a double-blind between-subjects randomized placebo-controlled trial was conducted, assessing performance on a bodily emotion recognition task in healthy adult males before and after a single-dose of intranasal OT (24 IU). Overall, a single-dose of OT administration had a significant effect of medium size on emotion recognition from body language. OT-induced improvements in emotion recognition were not differentially modulated by the emotional valence of the presented stimuli (positive versus negative) and also, the overall tendency to label an observed emotional state as 'happy' (positive) or 'angry' (negative) was not modified by the administration of OT. Albeit moderate, the present findings of OT-induced improvements in bodily emotion recognition from whole-body PLD provide further support for a link between OT and the processing of socio-communicative cues originating from the body of others. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Both physical exercise and progressive muscle relaxation reduce the facing-the-viewer bias in biological motion perception.

Directory of Open Access Journals (Sweden)

Adam Heenan

Full Text Available Biological motion stimuli, such as orthographically projected stick figure walkers, are ambiguous about their orientation in depth. The projection of a stick figure walker oriented towards the viewer, therefore, is the same as its projection when oriented away. Even though such figures are depth-ambiguous, however, observers tend to interpret them as facing towards them more often than facing away. Some have speculated that this facing-the-viewer bias may exist for sociobiological reasons: Mistaking another human as retreating when they are actually approaching could have more severe consequences than the opposite error. Implied in this hypothesis is that the facing-towards percept of biological motion stimuli is potentially more threatening. Measures of anxiety and the facing-the-viewer bias should therefore be related, as researchers have consistently found that anxious individuals display an attentional bias towards more threatening stimuli. The goal of this study was to assess whether physical exercise (Experiment 1 or an anxiety induction/reduction task (Experiment 2 would significantly affect facing-the-viewer biases. We hypothesized that both physical exercise and progressive muscle relaxation would decrease facing-the-viewer biases for full stick figure walkers, but not for bottom- or top-half-only human stimuli, as these carry less sociobiological relevance. On the other hand, we expected that the anxiety induction task (Experiment 2 would increase facing-the-viewer biases for full stick figure walkers only. In both experiments, participants completed anxiety questionnaires, exercised on a treadmill (Experiment 1 or performed an anxiety induction/reduction task (Experiment 2, and then immediately completed a perceptual task that allowed us to assess their facing-the-viewer bias. As hypothesized, we found that physical exercise and progressive muscle relaxation reduced facing-the-viewer biases for full stick figure walkers only. Our

Evaluating the influence of organ motion during photon vs. proton therapy for locally advanced prostate cancer using biological models

DEFF Research Database (Denmark)

Busch, Kia; G Andersen, Andreas; Casares-Magaz, Oscar

2017-01-01

beam angles for pelvic irradiation, we aimed to evaluate the influence of organ motion for PT using biological models, and to compare this with contemporary photon-based RT. MATERIAL AND METHODS: Eight locally advanced prostate cancer patients with a planning CT (pCT) and 8-9 repeated CT scans (r...

Camera Motion and Surrounding Scene Appearance as Context for Action Recognition

KAUST Repository

Heilbron, Fabian Caba; Thabet, Ali Kassem; Niebles, Juan Carlos; Ghanem, Bernard

2015-01-01

This paper describes a framework for recognizing human actions in videos by incorporating a new set of visual cues that represent the context of the action. We develop a weak foreground-background segmentation approach in order to robustly extract not only foreground features that are focused on the actors, but also global camera motion and contextual scene information. Using dense point trajectories, our approach separates and describes the foreground motion from the background, represents the appearance of the extracted static background, and encodes the global camera motion that interestingly is shown to be discriminative for certain action classes. Our experiments on four challenging benchmarks (HMDB51, Hollywood2, Olympic Sports, and UCF50) show that our contextual features enable a significant performance improvement over state-of-the-art algorithms.

Camera Motion and Surrounding Scene Appearance as Context for Action Recognition

KAUST Repository

Heilbron, Fabian Caba

2015-04-17

This paper describes a framework for recognizing human actions in videos by incorporating a new set of visual cues that represent the context of the action. We develop a weak foreground-background segmentation approach in order to robustly extract not only foreground features that are focused on the actors, but also global camera motion and contextual scene information. Using dense point trajectories, our approach separates and describes the foreground motion from the background, represents the appearance of the extracted static background, and encodes the global camera motion that interestingly is shown to be discriminative for certain action classes. Our experiments on four challenging benchmarks (HMDB51, Hollywood2, Olympic Sports, and UCF50) show that our contextual features enable a significant performance improvement over state-of-the-art algorithms.

Impact of respiratory motion on variable relative biological effectiveness in 4D-dose distributions of proton therapy.

Science.gov (United States)

Ulrich, Silke; Wieser, Hans-Peter; Cao, Wenhua; Mohan, Radhe; Bangert, Mark

2017-11-01

Organ motion during radiation therapy with scanned protons leads to deviations between the planned and the delivered physical dose. Using a constant relative biological effectiveness (RBE) of 1.1 linearly maps these deviations into RBE-weighted dose. However, a constant value cannot account for potential nonlinear variations in RBE suggested by variable RBE models. Here, we study the impact of motion on recalculations of RBE-weighted dose distributions using a phenomenological variable RBE model. 4D-dose calculation including variable RBE was implemented in the open source treatment planning toolkit matRad. Four scenarios were compared for one field and two field proton treatments for a liver cancer patient assuming (α∕β) x  = 2 Gy and (α∕β) x  = 10 Gy: (A) the optimized static dose distribution with constant RBE, (B) a static recalculation with variable RBE, (C) a 4D-dose recalculation with constant RBE and (D) a 4D-dose recalculation with variable RBE. For (B) and (D), the variable RBE was calculated by the model proposed by McNamara. For (C), the physical dose was accumulated with direct dose mapping; for (D), dose-weighted radio-sensitivity parameters of the linear quadratic model were accumulated to model synergistic irradiation effects on RBE. Dose recalculation with variable RBE led to an elevated biological dose at the end of the proton field, while 4D-dose recalculation exhibited random deviations everywhere in the radiation field depending on the interplay of beam delivery and organ motion. For a single beam treatment assuming (α∕β) x  = 2 Gy, D 95 % was 1.98 Gy (RBE) (A), 2.15 Gy (RBE) (B), 1.81 Gy (RBE) (C) and 1.98 Gy (RBE) (D). The homogeneity index was 1.04 (A), 1.08 (B), 1.23 (C) and 1.25 (D). For the studied liver case, intrafractional motion did not reduce the modulation of the RBE-weighted dose postulated by variable RBE models for proton treatments.

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

Science.gov (United States)

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

Effectiveness of the Gaze Direction Recognition Task for Chronic Neck Pain and Cervical Range of Motion: A Randomized Controlled Pilot Study

Directory of Open Access Journals (Sweden)

Satoshi Nobusako

2012-01-01

Full Text Available We developed a mental task with gaze direction recognition (GDR by which subjects observed neck rotation of another individual from behind and attempted to recognize the direction of gaze. A randomized controlled trial was performed in test (=9 and control (=8 groups of subjects with chronic neck pain undergoing physical therapy either with or without the GDR task carried out over 12 sessions during a three-week period. Primary outcome measures were defined as the active range of motion and pain on rotation of the neck. Secondary outcome measures were reaction time (RT and response accuracy in the GDR task group. ANOVA indicated a main effect for task session and group, and interaction of session. Post hoc testing showed that the GDR task group exhibited a significant simple main effect upon session, and significant sequential improvement of neck motion and relief of neck pain. Rapid effectiveness was significant in both groups. The GDR task group had a significant session-to-session reduction of RTs in correct responses. In conclusion, the GDR task we developed provides a promising rehabilitation measure for chronic neck pain.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

Science.gov (United States)

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database.

Science.gov (United States)

Kataoka, Hirokatsu; Satoh, Yutaka; Aoki, Yoshimitsu; Oikawa, Shoko; Matsui, Yasuhiro

2018-02-20

The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing), which are difficult to distinguish from each other. It is believed that the fine-grained action recognition induces a pedestrian intention estimation for a helpful advanced driver-assistance systems (ADAS). The following difficulties have been studied to achieve a fine-grained and accurate pedestrian action recognition: (i) In order to analyze the fine-grained motion of a pedestrian appearance in the vehicle-mounted drive recorder, a method to describe subtle change of motion characteristics occurring in a short time is necessary; (ii) even when the background moves greatly due to the driving of the vehicle, it is necessary to detect changes in subtle motion of the pedestrian; (iii) the collection of large-scale fine-grained actions is very difficult, and therefore a relatively small database should be focused. We find out how to learn an effective recognition model with only a small-scale database. Here, we have thoroughly evaluated several types of configurations to explore an effective approach in fine-grained pedestrian action recognition without a large-scale database. Moreover, two different datasets have been collected in order to raise the issue. Finally, our proposal attained 91.01% on National Traffic Science and Environment Laboratory database (NTSEL) and 53.23% on the near-miss driving recorder database (NDRDB). The paper has improved +8.28% and +6.53% from baseline two-stream fusion convnets.

Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database

Directory of Open Access Journals (Sweden)

Hirokatsu Kataoka

2018-02-01

Full Text Available The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing, which are difficult to distinguish from each other. It is believed that the fine-grained action recognition induces a pedestrian intention estimation for a helpful advanced driver-assistance systems (ADAS. The following difficulties have been studied to achieve a fine-grained and accurate pedestrian action recognition: (i In order to analyze the fine-grained motion of a pedestrian appearance in the vehicle-mounted drive recorder, a method to describe subtle change of motion characteristics occurring in a short time is necessary; (ii even when the background moves greatly due to the driving of the vehicle, it is necessary to detect changes in subtle motion of the pedestrian; (iii the collection of large-scale fine-grained actions is very difficult, and therefore a relatively small database should be focused. We find out how to learn an effective recognition model with only a small-scale database. Here, we have thoroughly evaluated several types of configurations to explore an effective approach in fine-grained pedestrian action recognition without a large-scale database. Moreover, two different datasets have been collected in order to raise the issue. Finally, our proposal attained 91.01% on National Traffic Science and Environment Laboratory database (NTSEL and 53.23% on the near-miss driving recorder database (NDRDB. The paper has improved +8.28% and +6.53% from baseline two-stream fusion convnets.

COMPARISON OF BACKGROUND SUBTRACTION, SOBEL, ADAPTIVE MOTION DETECTION, FRAME DIFFERENCES, AND ACCUMULATIVE DIFFERENCES IMAGES ON MOTION DETECTION

Directory of Open Access Journals (Sweden)

Dara Incam Ramadhan

2018-02-01

Full Text Available Nowadays, digital image processing is not only used to recognize motionless objects, but also used to recognize motions objects on video. One use of moving object recognition on video is to detect motion, which implementation can be used on security cameras. Various methods used to detect motion have been developed so that in this research compared some motion detection methods, namely Background Substraction, Adaptive Motion Detection, Sobel, Frame Differences and Accumulative Differences Images (ADI. Each method has a different level of accuracy. In the background substraction method, the result obtained 86.1% accuracy in the room and 88.3% outdoors. In the sobel method the result of motion detection depends on the lighting conditions of the room being supervised. When the room is in bright condition, the accuracy of the system decreases and when the room is dark, the accuracy of the system increases with an accuracy of 80%. In the adaptive motion detection method, motion can be detected with a condition in camera visibility there is no object that is easy to move. In the frame difference method, testing on RBG image using average computation with threshold of 35 gives the best value. In the ADI method, the result of accuracy in motion detection reached 95.12%.

Hand based visual intent recognition algorithm for wheelchair motion

CSIR Research Space (South Africa)

Luhandjula, T

2010-05-01

Full Text Available This paper describes an algorithm for a visual human-machine interface that infers a person’s intention from the motion of the hand. Work in progress shows a proof of concept tested on static images. The context for which this solution is intended...

Human body contour data based activity recognition.

Science.gov (United States)

Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

2013-01-01

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

Bio-recognitive photonics of a DNA-guided organic semiconductor

Science.gov (United States)

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-01

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an `inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Bio-recognitive photonics of a DNA-guided organic semiconductor.

Science.gov (United States)

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-04

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an 'inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

The Application of Leap Motion in Astronaut Virtual Training

Science.gov (United States)

Qingchao, Xie; Jiangang, Chao

2017-03-01

With the development of computer vision, virtual reality has been applied in astronaut virtual training. As an advanced optic equipment to track hand, Leap Motion can provide precise and fluid tracking of hands. Leap Motion is suitable to be used as gesture input device in astronaut virtual training. This paper built an astronaut virtual training based Leap Motion, and established the mathematics model of hands occlusion. At last the ability of Leap Motion to handle occlusion was analysed. A virtual assembly simulation platform was developed for astronaut training, and occlusion gesture would influence the recognition process. The experimental result can guide astronaut virtual training.

A survey on vision-based human action recognition

NARCIS (Netherlands)

Poppe, Ronald Walter

Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human–computer interaction. The task is challenging due to variations in motion

A neuromorphic architecture for object recognition and motion anticipation using burst-STDP.

Directory of Open Access Journals (Sweden)

Andrew Nere

Full Text Available In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP. STDP is responsible for the strengthening (or weakening of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips.

Motion camouflage in three dimensions

OpenAIRE

Reddy, P. V.; Justh, E. W.; Krishnaprasad, P. S.

2006-01-01

We formulate and analyze a three-dimensional model of motion camouflage, a stealth strategy observed in nature. A high-gain feedback law for motion camouflage is formulated in which the pursuer and evader trajectories are described using natural Frenet frames (or relatively parallel adapted frames), and the corresponding natural curvatures serve as controls. The biological plausibility of the feedback law is discussed, as is its connection to missile guidance. Simulations illustrating motion ...

A Survey of Face Recognition Technique | Omidiora | Journal of ...

African Journals Online (AJOL)

A review of face recognition techniques has been carried out. Face recognition has been an attractive field in the society of both biological and computer vision of research. It exhibits the characteristics of being natural and low-intrusive. In this paper, an updated survey of techniques for face recognition is made. Methods of ...

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

Science.gov (United States)

Adewuyi, Adenike A; Hargrove, Levi J; Kuiken, Todd A

2016-04-01

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

Recognition and Synthesis of Human Movements by Parametric HMMs

DEFF Research Database (Denmark)

Herzog, Dennis; Krüger, Volker

2009-01-01

The representation of human movements for recognition and synthesis is important in many application fields such as: surveillance, human-computer interaction, motion capture, and humanoid robots. Hidden Markov models (HMMs) are a common statistical framework in this context, since...... on the recognition and synthesis of human arm movements. Furthermore, we will show in various experiments the use of PHMMs for the control of a humanoid robot by synthesizing movements for relocating objects at arbitrary positions. In vision-based interaction experiments, PHMM are used for the recognition...... of pointing movements, where the recognized parameterization conveys to a robot the important information which object to relocate and where to put it. Finally, we evaluate the accuracy of recognition and synthesis for pointing and grasping arm movements and discuss that the precision of the synthesis...

Hand motion modeling for psychology analysis in job interview using optical flow-history motion image: OF-HMI

Science.gov (United States)

Khalifa, Intissar; Ejbali, Ridha; Zaied, Mourad

2018-04-01

To survive the competition, companies always think about having the best employees. The selection is depended on the answers to the questions of the interviewer and the behavior of the candidate during the interview session. The study of this behavior is always based on a psychological analysis of the movements accompanying the answers and discussions. Few techniques are proposed until today to analyze automatically candidate's non verbal behavior. This paper is a part of a work psychology recognition system; it concentrates in spontaneous hand gesture which is very significant in interviews according to psychologists. We propose motion history representation of hand based on an hybrid approach that merges optical flow and history motion images. The optical flow technique is used firstly to detect hand motions in each frame of a video sequence. Secondly, we use the history motion images (HMI) to accumulate the output of the optical flow in order to have finally a good representation of the hand`s local movement in a global temporal template.

Dorsal stream involvement in recognition of objects with transient onset but not with ramped onset

Directory of Open Access Journals (Sweden)

Lourenco Tomas

2011-08-01

Full Text Available Abstract Background Although the ventral visual stream is understood to be responsible for object recognition, it has been proposed that the dorsal stream may contribute to object recognition by rapidly activating parietal attention mechanisms, prior to ventral stream object processing. Methods To investigate the relative contribution of the dorsal visual stream to object recognition a group of tertiary students were divided into good and poor motion coherence groups and assessed on tasks classically assumed to rely on ventral stream processing. Participants were required to identify simple line drawings in two tasks, one where objects were presented abruptly for 50 ms followed by a white-noise mask, the other where contrast was linearly ramped on and off over 325 ms and replaced with a mask. Results Although both groups only differed in motion coherence performance (a dorsal stream measure, the good motion coherence group showed superior contrast sensitivity for object recognition on the abrupt, but not the ramped presentation tasks. Conclusions We propose that abrupt presentation of objects activated attention mechanisms fed by the dorsal stream, whereas the ramped presentation had reduced transience and thus did not activate dorsal attention mechanisms as well. The results suggest that rapid dorsal stream activation may be required to assist with ventral stream object processing.

Pattern recognition of neurotransmitters using multimode sensing.

Science.gov (United States)

Stefan-van Staden, Raluca-Ioana; Moldoveanu, Iuliana; van Staden, Jacobus Frederick

2014-05-30

Pattern recognition is essential in chemical analysis of biological fluids. Reliable and sensitive methods for neurotransmitters analysis are needed. Therefore, we developed for pattern recognition of neurotransmitters: dopamine, epinephrine, norepinephrine a method based on multimode sensing. Multimode sensing was performed using microsensors based on diamond paste modified with 5,10,15,20-tetraphenyl-21H,23H-porphyrine, hemin and protoporphyrin IX in stochastic and differential pulse voltammetry modes. Optimized working conditions: phosphate buffer solution of pH 3.01 and KCl 0.1mol/L (as electrolyte support), were determined using cyclic voltammetry and used in all measurements. The lowest limits of quantification were: 10(-10)mol/L for dopamine and epinephrine, and 10(-11)mol/L for norepinephrine. The multimode microsensors were selective over ascorbic and uric acids and the method facilitated reliable assay of neurotransmitters in urine samples, and therefore, the pattern recognition showed high reliability (RSDneurotransmitters on biological fluids at a lower determination level than chromatographic methods. The sampling of the biological fluids referees only to the buffering (1:1, v/v) with a phosphate buffer pH 3.01, while for chromatographic methods the sampling is laborious. Accordingly with the statistic evaluation of the results at 99.00% confidence level, both modes can be used for pattern recognition and quantification of neurotransmitters with high reliability. The best multimode microsensor was the one based on diamond paste modified with protoporphyrin IX. Copyright © 2014 Elsevier B.V. All rights reserved.

Localization and Recognition of Dynamic Hand Gestures Based on Hierarchy of Manifold Classifiers

Science.gov (United States)

Favorskaya, M.; Nosov, A.; Popov, A.

2015-05-01

Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset "Multi-modal Gesture Recognition Challenge 2013: Dataset and Results" including 393 dynamic hand-gestures was chosen. The proposed method yielded 84-91% recognition accuracy, in average, for restricted set of dynamic gestures.

LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

Directory of Open Access Journals (Sweden)

M. Favorskaya

2015-05-01

Full Text Available Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case. Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset “Multi-modal Gesture Recognition Challenge 2013: Dataset and Results” including 393 dynamic hand-gestures was chosen. The proposed method yielded 84–91% recognition accuracy, in average, for restricted set of dynamic gestures.

Two-dimensional laser servoing for precision motion control of an ODV robotic license plate recognition system

Science.gov (United States)

Song, Zhen; Moore, Kevin L.; Chen, YangQuan; Bahl, Vikas

2003-09-01

As an outgrowth of series of projects focused on mobility of unmanned ground vehicles (UGV), an omni-directional (ODV), multi-robot, autonomous mobile parking security system has been developed. The system has two types of robots: the low-profile Omni-Directional Inspection System (ODIS), which can be used for under-vehicle inspections, and the mid-sized T4 robot, which serves as a ``marsupial mothership'' for the ODIS vehicles and performs coarse resolution inspection. A key task for the T4 robot is license plate recognition (LPR). For a successful LPR task without compromising the recognition rate, the robot must be able to identify the bumper locations of vehicles in the parking area and then precisely position the LPR camera relative to the bumper. This paper describes a 2D-laser scanner based approach to bumper identification and laser servoing for the T4 robot. The system uses a gimbal-mounted scanning laser. As the T4 robot travels down a row of parking stalls, data is collected from the laser every 100ms. For each parking stall in the range of the laser during the scan, the data is matched to a ``bumper box'' corresponding to where a car bumper is expected, resulting in a point cloud of data corresponding to a vehicle bumper for each stall. Next, recursive line-fitting algorithms are used to determine a line for the data in each stall's ``bumper box.'' The fitting technique uses Hough based transforms, which are robust against segmentation problems and fast enough for real-time line fitting. Once a bumper line is fitted with an acceptable confidence, the bumper location is passed to the T4 motion controller, which moves to position the LPR camera properly relative to the bumper. The paper includes examples and results that show the effectiveness of the technique, including its ability to work in real-time.

Quadcopter Control Using Speech Recognition

Science.gov (United States)

Malik, H.; Darma, S.; Soekirno, S.

2018-04-01

This research reported a comparison from a success rate of speech recognition systems that used two types of databases they were existing databases and new databases, that were implemented into quadcopter as motion control. Speech recognition system was using Mel frequency cepstral coefficient method (MFCC) as feature extraction that was trained using recursive neural network method (RNN). MFCC method was one of the feature extraction methods that most used for speech recognition. This method has a success rate of 80% - 95%. Existing database was used to measure the success rate of RNN method. The new database was created using Indonesian language and then the success rate was compared with results from an existing database. Sound input from the microphone was processed on a DSP module with MFCC method to get the characteristic values. Then, the characteristic values were trained using the RNN which result was a command. The command became a control input to the single board computer (SBC) which result was the movement of the quadcopter. On SBC, we used robot operating system (ROS) as the kernel (Operating System).

Time-Motion and Biological Responses in Simulated Mixed Martial Arts Sparring Matches.

Science.gov (United States)

Coswig, Victor S; Ramos, Solange de P; Del Vecchio, Fabrício B

2016-08-01

Coswig, VS, Ramos, SdP, and Del Vecchio, FB. Time-motion and biological responses in simulated mixed martial arts sparring matches. J Strength Cond Res 30(8): 2156-2163, 2016-Simulated matches are a relevant component of training for mixed martial arts (MMA) athletes. This study aimed to characterize time-motion responses and investigate physiological stress and neuromuscular changes related to MMA sparring matches. Thirteen athletes with an average age of 25 ± 5 years, body mass of 81.3 ± 9.5 kg, height of 176.2 ± 5.5 cm, and time of practice in MMA of 39 ± 25 months participated in the study. The fighters executed three 5-minute rounds with 1-minute intervals. Blood and salivary samples were collected and physical tests and psychometric questionnaires administered at 3 time points: before (PRE), immediately after (POST), and 48 hours after the combat (48 h). Statistical analysis applied analysis of variance for repeated measurements. In biochemical analysis, significant changes (p ≤ 0.05) were identified between PRE and POST (glucose: 80.3 ± 12.7 to 156.5 ± 19.1 mg·ml; lactate: 4 ± 1.7 to 15.6 ± 4.8 mmol·dl), POST and 48 hours (glucose: 156.5 ± 19.1 to 87.6 ± 15.5 mg·ml; lactate: 15.6 ± 4.8 to 2.9 ± 3.5 mmol·dl; urea: 44.1 ± 8.9 to 36.3 ± 7.8 mg·ml), and PRE and 48 hours (creatine kinase [CK]: 255.8 ± 137.4 to 395.9 ± 188.7 U/L). In addition, time-motion analyses showed a total high:low intensity of 1:2 and an effort:pause ratio of 1:3. In conclusion, simulated MMA sparring matches feature moderate to high intensity and a low degree of musculoskeletal damage, which can be seen by absence of physical performance and decrease in CK. Results of the study indicate that sparring training could be introduced into competitive microcycles to improve technical and tactical aspects of MMA matches, due to the high motor specificity and low muscle damage.

Locust Collective Motion and Its Modeling.

Directory of Open Access Journals (Sweden)

Gil Ariel

2015-12-01

Full Text Available Over the past decade, technological advances in experimental and animal tracking techniques have motivated a renewed theoretical interest in animal collective motion and, in particular, locust swarming. This review offers a comprehensive biological background followed by comparative analysis of recent models of locust collective motion, in particular locust marching, their settings, and underlying assumptions. We describe a wide range of recent modeling and simulation approaches, from discrete agent-based models of self-propelled particles to continuous models of integro-differential equations, aimed at describing and analyzing the fascinating phenomenon of locust collective motion. These modeling efforts have a dual role: The first views locusts as a quintessential example of animal collective motion. As such, they aim at abstraction and coarse-graining, often utilizing the tools of statistical physics. The second, which originates from a more biological perspective, views locust swarming as a scientific problem of its own exceptional merit. The main goal should, thus, be the analysis and prediction of natural swarm dynamics. We discuss the properties of swarm dynamics using the tools of statistical physics, as well as the implications for laboratory experiments and natural swarms. Finally, we stress the importance of a combined-interdisciplinary, biological-theoretical effort in successfully confronting the challenges that locusts pose at both the theoretical and practical levels.

The Coding of Biological Information: From Nucleotide Sequence to Protein Recognition

Science.gov (United States)

Štambuk, Nikola

The paper reviews the classic results of Swanson, Dayhoff, Grantham, Blalock and Root-Bernstein, which link genetic code nucleotide patterns to the protein structure, evolution and molecular recognition. Symbolic representation of the binary addresses defining particular nucleotide and amino acid properties is discussed, with consideration of: structure and metric of the code, direct correspondence between amino acid and nucleotide information, and molecular recognition of the interacting protein motifs coded by the complementary DNA and RNA strands.

Lip motion recognition of speaker based on SIFT%基于SIFT的说话人唇动识别

Institute of Scientific and Technical Information of China (English)

马新军; 吴晨晨; 仲乾元; 李园园

2017-01-01

Aiming at the problem that the lip feature dimension is too high and sensitive to the scale space,a technique based on the Scale-Invariant Feature Transform (SIFT) algorithm was proposed to carry out the speaker authentication.Firstly,a simple video frame image neat algorithm was proposed to adjust the length of the lip video to the same length,and the representative lip motion pictures were extracted.Then,a new algorithm based on key points of SIFT was proposed to extract the texture and motion features.After the integration of Principal Component Analysis (PCA) algorithm,the typical lip motion features were obtained for authentication.Finally,a simple classification algorithm was presented according to the obtained features.The experimental results show that compared to the common Local Binary Pattern (LBP) feature and the Histogram of Oriental Gradient (HOG) feature,the False Acceptance Rate (FAR) and False Rejection Rate (FRR) of the proposed feature extraction algorithm are better,which proves that the whole speaker lip motion recognition algorithm is effective and can get the ideal results.%针对唇部特征提取维度过高以及对尺度空间敏感的问题,提出了一种基于尺度不变特征变换(SIFT)算法作特征提取来进行说话人身份认证的技术.首先,提出了一种简单的视频帧图片规整算法,将不同长度的唇动视频规整到同一的长度,提取出具有代表性的唇动图片;然后,提出一种在SIFT关键点的基础上,进行纹理和运动特征的提取算法,并经过主成分分析(PCA)算法的整合,最终得到具有代表性的唇动特征进行认证;最后,根据所得到的特征,提出了一种简单的分类算法.实验结果显示,和常见的局部二元模式(LBP)特征和方向梯度直方图(HOG)特征相比较,该特征提取算法的错误接受率(FAR)和错误拒绝率(FRR)表现更佳.说明整个说话人唇动特征识别算法是有效的,能够得到较为理想的结果.

Dynamic Time Warping Distance Method for Similarity Test of Multipoint Ground Motion Field

Directory of Open Access Journals (Sweden)

Yingmin Li

2010-01-01

Full Text Available The reasonability of artificial multi-point ground motions and the identification of abnormal records in seismic array observations, are two important issues in application and analysis of multi-point ground motion fields. Based on the dynamic time warping (DTW distance method, this paper discusses the application of similarity measurement in the similarity analysis of simulated multi-point ground motions and the actual seismic array records. Analysis results show that the DTW distance method not only can quantitatively reflect the similarity of simulated ground motion field, but also offers advantages in clustering analysis and singularity recognition of actual multi-point ground motion field.

Wheelchair control by head motion

Directory of Open Access Journals (Sweden)

Pajkanović Aleksandar

2013-01-01

Full Text Available Electric wheelchairs are designed to aid paraplegics. Unfortunately, these can not be used by persons with higher degree of impairment, such as quadriplegics, i.e. persons that, due to age or illness, can not move any of the body parts, except of the head. Medical devices designed to help them are very complicated, rare and expensive. In this paper a microcontroller system that enables standard electric wheelchair control by head motion is presented. The system comprises electronic and mechanic components. A novel head motion recognition technique based on accelerometer data processing is designed. The wheelchair joystick is controlled by the system’s mechanical actuator. The system can be used with several different types of standard electric wheelchairs. It is tested and verified through an experiment performed within this paper.

Fusion of optical flow based motion pattern analysis and silhouette classification for person tracking and detection

NARCIS (Netherlands)

Tangelder, J.W.H.; Lebert, E.; Burghouts, G.J.; Zon, K. van; Den Uyl, M.J.

2014-01-01

This paper presents a novel approach to detect persons in video by combining optical flow based motion analysis and silhouette based recognition. A new fast optical flow computation method is described, and its application in a motion based analysis framework unifying human tracking and detection is

Is synthetic biology mechanical biology?

Science.gov (United States)

Holm, Sune

2015-12-01

A widespread and influential characterization of synthetic biology emphasizes that synthetic biology is the application of engineering principles to living systems. Furthermore, there is a strong tendency to express the engineering approach to organisms in terms of what seems to be an ontological claim: organisms are machines. In the paper I investigate the ontological and heuristic significance of the machine analogy in synthetic biology. I argue that the use of the machine analogy and the aim of producing rationally designed organisms does not necessarily imply a commitment to mechanical biology. The ideal of applying engineering principles to biology is best understood as expressing recognition of the machine-unlikeness of natural organisms and the limits of human cognition. The paper suggests an interpretation of the identification of organisms with machines in synthetic biology according to which it expresses a strategy for representing, understanding, and constructing living systems that are more machine-like than natural organisms.

Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

Directory of Open Access Journals (Sweden)

Shuxiang Guo

2015-04-01

Full Text Available The surface electromyography (sEMG technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS, Detrended Fluctuation Analysis (DFA, Weight Peaks (WP, and Muscular Model (MM and two classifiers (Neural Networks (NN and Support Vector Machine (SVM, for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7% during the training process while SVM performed better in real-time experiments (85.9%. For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7% while MM performed the best during real-time tests (94.3%. The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement.

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

Directory of Open Access Journals (Sweden)

Jingbin Liu

2015-06-01

Full Text Available The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed.

sEMG-Based Gesture Recognition with Convolution Neural Networks

Directory of Open Access Journals (Sweden)

Zhen Ding

2018-06-01

Full Text Available The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

Real-Time Multiview Recognition of Human Gestures by Distributed Image Processing

Directory of Open Access Journals (Sweden)

Sato Kosuke

2010-01-01

Full Text Available Since a gesture involves a dynamic and complex motion, multiview observation and recognition are desirable. For the better representation of gestures, one needs to know, in the first place, from which views a gesture should be observed. Furthermore, it becomes increasingly important how the recognition results are integrated when larger numbers of camera views are considered. To investigate these problems, we propose a framework under which multiview recognition is carried out, and an integration scheme by which the recognition results are integrated online and in realtime. For performance evaluation, we use the ViHASi (Virtual Human Action Silhouette public image database as a benchmark and our Japanese sign language (JSL image database that contains 18 kinds of hand signs. By examining the recognition rates of each gesture for each view, we found gestures that exhibit view dependency and the gestures that do not. Also, we found that the view dependency itself could vary depending on the target gesture sets. By integrating the recognition results of different views, our swarm-based integration provides more robust and better recognition performance than individual fixed-view recognition agents.

Holding Biological Motion in Working Memory: An fMRI Study

Directory of Open Access Journals (Sweden)

Xiqian eLu

2016-06-01

Full Text Available Holding biological motion (BM, the movements of animate entities, in working memory (WM is important to our daily life activities. However, the neural substrates underlying the WM processing of BM remain largely unknown. Employing the functional magnetic resonance imaging (fMRI technique, the current study directly investigated this issue. We used point-light BM animations as the tested stimuli, and explored the neural substrates involved in encoding and retaining BM information in WM. Participants were required to remember two or four BM stimuli in a change-detection task. We first defined a set of potential brain regions devoted to the BM processing in WM in one experiment. We then conducted the second fMRI experiment, and performed time-course analysis over the pre-defined regions, which allowed us to differentiate the encoding and maintenance phases of WM. The results showed that a set of brain regions were involved in encoding BM into WM, including the middle frontal gyrus, inferior frontal gyrus, superior parietal lobule, inferior parietal lobule, superior temporal sulcus, fusiform gyrus, and middle occipital gyrus. However, only the middle frontal gyrus, inferior frontal gyrus, superior parietal lobule, and inferior parietal lobule were involved in retaining BM into WM. These results suggest that an overlapped network exists between the WM encoding and maintenance for BM; however, retaining BM in WM predominately relies on the mirror neuron system.

Recognition Memory for Movement in Photographs: A Developmental Study.

Science.gov (United States)

Futterweit, Lorelle R.; Beilin, Harry

1994-01-01

Investigated whether children's recognition memory for movement in photographs is distorted forward in the direction of implied motion. When asked whether the second photograph was the same as or different from the first, subjects made more errors for test photographs showing the action slightly forward in time, compared with slightly backward in…

Validation of Energy Expenditure Prediction Models Using Real-Time Shoe-Based Motion Detectors.

Science.gov (United States)

Lin, Shih-Yun; Lai, Ying-Chih; Hsia, Chi-Chun; Su, Pei-Fang; Chang, Chih-Han

2017-09-01

This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers. Three physical activity (PA) datasets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The "unclassified" dataset was defined as the data without PA recognition, the "recognition" as the data classified with PA recognition, and the "intensity segmentation" as the data with intensity segmentation. The three datasets contained accelerometer signals (quantified as signal magnitude area (SMA)) and net heart rate (HR net ). The accuracy of these models was assessed according to the deviation between physically measured EE and model-estimated EE. The variance between physically measured EE and model-estimated EE expressed by simple linear regressions was increased by 63% and 13% using SMA and HR net , respectively. The accuracy of the EE predicted from accelerometer signals is influenced by the different activities that exhibit different count-EE relationships within the same prediction model. The recognition model provides a better estimation and lower variability of EE compared with the unclassified and intensity segmentation models. The proposed shoe-based motion detectors can improve the accuracy of EE estimation and has great potential to be used to manage everyday exercise in real time.

Exploiting core knowledge for visual object recognition.

Science.gov (United States)

Schurgin, Mark W; Flombaum, Jonathan I

2017-03-01

Humans recognize thousands of objects, and with relative tolerance to variable retinal inputs. The acquisition of this ability is not fully understood, and it remains an area in which artificial systems have yet to surpass people. We sought to investigate the memory process that supports object recognition. Specifically, we investigated the association of inputs that co-occur over short periods of time. We tested the hypothesis that human perception exploits expectations about object kinematics to limit the scope of association to inputs that are likely to have the same token as a source. In several experiments we exposed participants to images of objects, and we then tested recognition sensitivity. Using motion, we manipulated whether successive encounters with an image took place through kinematics that implied the same or a different token as the source of those encounters. Images were injected with noise, or shown at varying orientations, and we included 2 manipulations of motion kinematics. Across all experiments, memory performance was better for images that had been previously encountered with kinematics that implied a single token. A model-based analysis similarly showed greater memory strength when images were shown via kinematics that implied a single token. These results suggest that constraints from physics are built into the mechanisms that support memory about objects. Such constraints-often characterized as 'Core Knowledge'-are known to support perception and cognition broadly, even in young infants. But they have never been considered as a mechanism for memory with respect to recognition. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Modeling Recognition Memory Using the Similarity Structure of Natural Input

Science.gov (United States)

Lacroix, Joyca P. W.; Murre, Jaap M. J.; Postma, Eric O.; van den Herik, H. Jaap

2006-01-01

The natural input memory (NAM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed…

Milk protein tailoring to improve functional and biological properties

Directory of Open Access Journals (Sweden)

JEAN-MARC CHOBERT

2012-01-01

Full Text Available Proteins are involved in every aspects of life: structure, motion, catalysis, recognition and regulation. Today's highly sophisticated science of the modifications of proteins has ancient roots. The tailoring of proteins for food and medical uses precedes the beginning of what is called biochemistry. Chemical modification of proteins was pursued early in the twentieth century as an analytical procedure for side-chain amino acids. Later, methods were developed for specific inactivation of biologically active proteins and titration of their essential groups. Enzymatic modifications were mainly developed in the seventies when many more enzymes became economically available. Protein engineering has become a valuable tool for creating or improving proteins for practical use and has provided new insights into protein structure and function. The actual and potential use of milk proteins as food ingredients has been a popular topic for research over the past 40 years. With today's sophisticated analytical, biochemical and biological research tools, the presence of compounds with biological activity has been demonstrated. Improvements in separation techniques and enzyme technology have enabled efficient and economic isolation and modification of milk proteins, which has made possible their use as functional foods, dietary supplements, nutraceuticals and medical foods. In this review, some chemical and enzymatic modifications of milk proteins are described, with particular focus on their functional and biological properties.

SU-E-I-75: Development of New Biological Fingerprints for Patient Recognition to Identify Misfiled Images in a PACS Server

Energy Technology Data Exchange (ETDEWEB)

Shimizu, Y; Yoon, Y; Iwase, K; Yasumatsu, S; Matsunobu, Y [Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, JP (Japan); Morishita, J [Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, JP (Japan)

2015-06-15

Purpose: We are trying to develop an image-searching technique to identify misfiled images in a picture archiving and communication system (PACS) server by using five biological fingerprints: the whole lung field, cardiac shadow, superior mediastinum, lung apex, and right lower lung. Each biological fingerprint in a chest radiograph includes distinctive anatomical structures to identify misfiled images. The whole lung field was less effective for evaluating the similarity between two images than the other biological fingerprints. This was mainly due to the variation in the positioning for chest radiographs. The purpose of this study is to develop new biological fingerprints that could reduce influence of differences in the positioning for chest radiography. Methods: Two hundred patients were selected randomly from our database (36,212 patients). These patients had two images each (current and previous images). Current images were used as the misfiled images in this study. A circumscribed rectangular area of the lung and the upper half of the rectangle were selected automatically as new biological fingerprints. These biological fingerprints were matched to all previous images in the database. The degrees of similarity between the two images were calculated for the same and different patients. The usefulness of new the biological fingerprints for automated patient recognition was examined in terms of receiver operating characteristic (ROC) analysis. Results: Area under the ROC curves (AUCs) for the circumscribed rectangle of the lung, upper half of the rectangle, and whole lung field were 0.980, 0.994, and 0.950, respectively. The new biological fingerprints showed better performance in identifying the patients correctly than the whole lung field. Conclusion: We have developed new biological fingerprints: circumscribed rectangle of the lung and upper half of the rectangle. These new biological fingerprints would be useful for automated patient identification system

SU-E-I-75: Development of New Biological Fingerprints for Patient Recognition to Identify Misfiled Images in a PACS Server

International Nuclear Information System (INIS)

Shimizu, Y; Yoon, Y; Iwase, K; Yasumatsu, S; Matsunobu, Y; Morishita, J

2015-01-01

Purpose: We are trying to develop an image-searching technique to identify misfiled images in a picture archiving and communication system (PACS) server by using five biological fingerprints: the whole lung field, cardiac shadow, superior mediastinum, lung apex, and right lower lung. Each biological fingerprint in a chest radiograph includes distinctive anatomical structures to identify misfiled images. The whole lung field was less effective for evaluating the similarity between two images than the other biological fingerprints. This was mainly due to the variation in the positioning for chest radiographs. The purpose of this study is to develop new biological fingerprints that could reduce influence of differences in the positioning for chest radiography. Methods: Two hundred patients were selected randomly from our database (36,212 patients). These patients had two images each (current and previous images). Current images were used as the misfiled images in this study. A circumscribed rectangular area of the lung and the upper half of the rectangle were selected automatically as new biological fingerprints. These biological fingerprints were matched to all previous images in the database. The degrees of similarity between the two images were calculated for the same and different patients. The usefulness of new the biological fingerprints for automated patient recognition was examined in terms of receiver operating characteristic (ROC) analysis. Results: Area under the ROC curves (AUCs) for the circumscribed rectangle of the lung, upper half of the rectangle, and whole lung field were 0.980, 0.994, and 0.950, respectively. The new biological fingerprints showed better performance in identifying the patients correctly than the whole lung field. Conclusion: We have developed new biological fingerprints: circumscribed rectangle of the lung and upper half of the rectangle. These new biological fingerprints would be useful for automated patient identification system

REAL-TIME FACE RECOGNITION BASED ON OPTICAL FLOW AND HISTOGRAM EQUALIZATION

Directory of Open Access Journals (Sweden)

D. Sathish Kumar

2013-05-01

Full Text Available Face recognition is one of the intensive areas of research in computer vision and pattern recognition but many of which are focused on recognition of faces under varying facial expressions and pose variation. A constrained optical flow algorithm discussed in this paper, recognizes facial images involving various expressions based on motion vector computation. In this paper, an optical flow computation algorithm which computes the frames of varying facial gestures, and integrating with synthesized image in a probabilistic environment has been proposed. Also Histogram Equalization technique has been used to overcome the effect of illuminations while capturing the input data using camera devices. It also enhances the contrast of the image for better processing. The experimental results confirm that the proposed face recognition system is more robust and recognizes the facial images under varying expressions and pose variations more accurately.

Model for the computation of self-motion in biological systems

Science.gov (United States)

Perrone, John A.

1992-01-01

A technique is presented by which direction- and speed-tuned cells, such as those commonly found in the middle temporal region of the primate brain, can be utilized to analyze the patterns of retinal image motion that are generated during observer movement through the environment. The developed model determines heading by finding the peak response in a population of detectors or neurons each tuned to a particular heading direction. It is suggested that a complex interaction of multiple cell networks is required for the solution of the self-motion problem in the primate brain.

Modulation of pathogen recognition by autophagy

Directory of Open Access Journals (Sweden)

Ji Eun eOh

2012-03-01

Full Text Available Autophagy is an ancient biological process for maintaining cellular homeostasis by degradation of long-lived cytosolic proteins and organelles. Recent studies demonstrated that autophagy is availed by immune cells to regulate innate immunity. On the one hand, cells exert direct effector function by degrading intracellular pathogens; on the other hand, autophagy modulates pathogen recognition and downstream signaling for innate immune responses. Pathogen recognition via pattern recognition receptors induces autophagy. The function of phagocytic cells is enhanced by recruitment of autophagy-related proteins. Moreover, autophagy acts as a delivery system for viral replication complexes to migrate to the endosomal compartments where virus sensing occurs. In another case, key molecules of the autophagic pathway have been found to negatively regulate immune signaling, thus preventing aberrant activation of cytokine production and consequent immune responses. In this review, we focus on the recent advances in the role of autophagy in pathogen recognition and modulation of innate immune responses.

Sign Language Recognition with the Kinect Sensor Based on Conditional Random Fields

Directory of Open Access Journals (Sweden)

Hee-Deok Yang

2014-12-01

Full Text Available Sign language is a visual language used by deaf people. One difficulty of sign language recognition is that sign instances of vary in both motion and shape in three-dimensional (3D space. In this research, we use 3D depth information from hand motions, generated from Microsoft’s Kinect sensor and apply a hierarchical conditional random field (CRF that recognizes hand signs from the hand motions. The proposed method uses a hierarchical CRF to detect candidate segments of signs using hand motions, and then a BoostMap embedding method to verify the hand shapes of the segmented signs. Experiments demonstrated that the proposed method could recognize signs from signed sentence data at a rate of 90.4%.

Biological impact of geometric uncertainties: what margin is needed for intra-hepatic tumors?

International Nuclear Information System (INIS)

Kuo, Hsiang-Chi; Liu, Wen-Shan; Wu, Andrew; Mah, Dennis; Chuang, Keh-Shih; Hong, Linda; Yaparpalvi, Ravi; Guha, Chandan; Kalnicki, Shalom

2010-01-01

To evaluate and compare the biological impact on different proposed margin recipes for the same geometric uncertainties for intra-hepatic tumors with different tumor cell types or clinical stages. Three different margin recipes based on tumor motion were applied to sixteen IMRT plans with a total of twenty two intra-hepatic tumors. One recipe used the full amplitude of motion measured from patients to generate margins. A second used 70% of the full amplitude of motion, while the third had no margin for motion. The biological effects of geometric uncertainty in these three situations were evaluated with Equivalent Uniform Doses (EUD) for various survival fractions at 2 Gy (SF 2 ). There was no significant difference in the biological impact between the full motion margin and the 70% motion margin. Also, there was no significant difference between different tumor cell types. When the margin for motion was eliminated, the difference of the biological impact was significant among different cell types due to geometric uncertainties. Elimination of the motion margin requires dose escalation to compensate for the biological dose reduction due to the geometric misses during treatment. Both patient-based margins of full motion and of 70% motion are sufficient to prevent serious dosimetric error. Clinical implementation of margin reduction should consider the tumor sensitivity to radiation

Editorial overview : Systems biology for biotechnology

NARCIS (Netherlands)

Heinemann, Matthias; Pilpel, Yitzhak

About 15 years ago, systems biology was introduced as a novel approach to biological research. On the one side, its introduction was a result of the recognition that through solely the reductionist approach, we would ulti- mately not be able to understand how biological systems function as a whole.

A dynamic texture-based approach to recognition of facial actions and their temporal models.

Science.gov (United States)

Koelstra, Sander; Pantic, Maja; Patras, Ioannis

2010-11-01

In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set.

The what, when, where, and how of visual word recognition.

Science.gov (United States)

Carreiras, Manuel; Armstrong, Blair C; Perea, Manuel; Frost, Ram

2014-02-01

A long-standing debate in reading research is whether printed words are perceived in a feedforward manner on the basis of orthographic information, with other representations such as semantics and phonology activated subsequently, or whether the system is fully interactive and feedback from these representations shapes early visual word recognition. We review recent evidence from behavioral, functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and biologically plausible connectionist modeling approaches, focusing on how each approach provides insight into the temporal flow of information in the lexical system. We conclude that, consistent with interactive accounts, higher-order linguistic representations modulate early orthographic processing. We also discuss how biologically plausible interactive frameworks and coordinated empirical and computational work can advance theories of visual word recognition and other domains (e.g., object recognition). Copyright © 2013 Elsevier Ltd. All rights reserved.

Human activity recognition based on feature selection in smart home using back-propagation algorithm.

Science.gov (United States)

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

2014-09-01

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Capturing Motion and Depth Before Cinematography.

Science.gov (United States)

Wade, Nicholas J

2016-01-01

Visual representations of biological states have traditionally faced two problems: they lacked motion and depth. Attempts were made to supply these wants over many centuries, but the major advances were made in the early-nineteenth century. Motion was synthesized by sequences of slightly different images presented in rapid succession and depth was added by presenting slightly different images to each eye. Apparent motion and depth were combined some years later, but they tended to be applied separately. The major figures in this early period were Wheatstone, Plateau, Horner, Duboscq, Claudet, and Purkinje. Others later in the century, like Marey and Muybridge, were stimulated to extend the uses to which apparent motion and photography could be applied to examining body movements. These developments occurred before the birth of cinematography, and significant insights were derived from attempts to combine motion and depth.

Object recognition with hierarchical discriminant saliency networks.

Science.gov (United States)

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

Design of a compact low-power human-computer interaction equipment for hand motion

Science.gov (United States)

Wu, Xianwei; Jin, Wenguang

2017-01-01

Human-Computer Interaction (HCI) raises demand of convenience, endurance, responsiveness and naturalness. This paper describes a design of a compact wearable low-power HCI equipment applied to gesture recognition. System combines multi-mode sense signals: the vision sense signal and the motion sense signal, and the equipment is equipped with the depth camera and the motion sensor. The dimension (40 mm × 30 mm) and structure is compact and portable after tight integration. System is built on a module layered framework, which contributes to real-time collection (60 fps), process and transmission via synchronous confusion with asynchronous concurrent collection and wireless Blue 4.0 transmission. To minimize equipment's energy consumption, system makes use of low-power components, managing peripheral state dynamically, switching into idle mode intelligently, pulse-width modulation (PWM) of the NIR LEDs of the depth camera and algorithm optimization by the motion sensor. To test this equipment's function and performance, a gesture recognition algorithm is applied to system. As the result presents, general energy consumption could be as low as 0.5 W.

Optimizing pattern recognition-based control for partial-hand prosthesis application.

Science.gov (United States)

Earley, Eric J; Adewuyi, Adenike A; Hargrove, Levi J

2014-01-01

Partial-hand amputees often retain good residual wrist motion, which is essential for functional activities involving use of the hand. Thus, a crucial design criterion for a myoelectric, partial-hand prosthesis control scheme is that it allows the user to retain residual wrist motion. Pattern recognition (PR) of electromyographic (EMG) signals is a well-studied method of controlling myoelectric prostheses. However, wrist motion degrades a PR system's ability to correctly predict hand-grasp patterns. We studied the effects of (1) window length and number of hand-grasps, (2) static and dynamic wrist motion, and (3) EMG muscle source on the ability of a PR-based control scheme to classify functional hand-grasp patterns. Our results show that training PR classifiers with both extrinsic and intrinsic muscle EMG yields a lower error rate than training with either group by itself (pgrasps available to the classifier significantly decrease classification error (pgrasp.

Motion sickness: a negative reinforcement model.

Science.gov (United States)

Bowins, Brad

2010-01-15

Theories pertaining to the "why" of motion sickness are in short supply relative to those detailing the "how." Considering the profoundly disturbing and dysfunctional symptoms of motion sickness, it is difficult to conceive of why this condition is so strongly biologically based in humans and most other mammalian and primate species. It is posited that motion sickness evolved as a potent negative reinforcement system designed to terminate motion involving sensory conflict or postural instability. During our evolution and that of many other species, motion of this type would have impaired evolutionary fitness via injury and/or signaling weakness and vulnerability to predators. The symptoms of motion sickness strongly motivate the individual to terminate the offending motion by early avoidance, cessation of movement, or removal of oneself from the source. The motion sickness negative reinforcement mechanism functions much like pain to strongly motivate evolutionary fitness preserving behavior. Alternative why theories focusing on the elimination of neurotoxins and the discouragement of motion programs yielding vestibular conflict suffer from several problems, foremost that neither can account for the rarity of motion sickness in infants and toddlers. The negative reinforcement model proposed here readily accounts for the absence of motion sickness in infants and toddlers, in that providing strong motivation to terminate aberrant motion does not make sense until a child is old enough to act on this motivation.

Bayesian approach to MSD-based analysis of particle motion in live cells.

Science.gov (United States)

Monnier, Nilah; Guo, Syuan-Ming; Mori, Masashi; He, Jun; Lénárt, Péter; Bathe, Mark

2012-08-08

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Gait Recognition and Walking Exercise Intensity Estimation

Directory of Open Access Journals (Sweden)

Bor-Shing Lin

2014-04-01

Full Text Available Cardiovascular patients consult doctors for advice regarding regular exercise, whereas obese patients must self-manage their weight. Because a system for permanently monitoring and tracking patients’ exercise intensities and workouts is necessary, a system for recognizing gait and estimating walking exercise intensity was proposed. For gait recognition analysis, αβ filters were used to improve the recognition of athletic attitude. Furthermore, empirical mode decomposition (EMD was used to filter the noise of patients’ attitude to acquire the Fourier transform energy spectrum. Linear discriminant analysis was then applied to this energy spectrum for training and recognition. When the gait or motion was recognized, the walking exercise intensity was estimated. In addition, this study addressed the correlation between inertia and exercise intensity by using the residual function of the EMD and quadratic approximation to filter the effect of the baseline drift integral of the acceleration sensor. The increase in the determination coefficient of the regression equation from 0.55 to 0.81 proved that the accuracy of the method for estimating walking exercise intensity proposed by Kurihara was improved in this study.

DNA recognition by synthetic constructs.

Science.gov (United States)

Pazos, Elena; Mosquera, Jesús; Vázquez, M Eugenio; Mascareñas, José L

2011-09-05

The interaction of transcription factors with specific DNA sites is key for the regulation of gene expression. Despite the availability of a large body of structural data on protein-DNA complexes, we are still far from fully understanding the molecular and biophysical bases underlying such interactions. Therefore, the development of non-natural agents that can reproduce the DNA-recognition properties of natural transcription factors remains a major and challenging goal in chemical biology. In this review we summarize the basics of double-stranded DNA recognition by transcription factors, and describe recent developments in the design and preparation of synthetic DNA binders. We mainly focus on synthetic peptides that have been designed by following the DNA interaction of natural proteins, and we discuss how the tools of organic synthesis can be used to make artificial constructs equipped with functionalities that introduce additional properties to the recognition process, such as sensing and controllability. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Application of biomolecular recognition via magnetic nanoparticle in nanobiotechnology

Science.gov (United States)

Shen, Wei-Zheng; Cetinel, Sibel; Montemagno, Carlo

2018-05-01

The marriage of biomolecular recognition and magnetic nanoparticle creates tremendous opportunities in the development of advanced technology both in academic research and in industrial sectors. In this paper, we review current progress on the magnetic nanoparticle-biomolecule hybrid systems, particularly employing the recognition pairs of DNA-DNA, DNA-protein, protein-protein, and protein-inorganics in several nanobiotechnology application areas, including molecular biology, diagnostics, medical treatment, industrial biocatalysts, and environmental separations.

MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers

Directory of Open Access Journals (Sweden)

Elisabetta Farella

2007-01-01

Full Text Available Human-computer interaction (HCI and virtual reality applications pose the challenge of enabling real-time interfaces for natural interaction. Gesture recognition based on body-mounted accelerometers has been proposed as a viable solution to translate patterns of movements that are associated with user commands, thus substituting point-and-click methods or other cumbersome input devices. On the other hand, cost and power constraints make the implementation of a natural and efficient interface suitable for consumer applications a critical task. Even though several gesture recognition solutions exist, their use in HCI context has been poorly characterized. For this reason, in this paper, we consider a low-cost/low-power wearable motion tracking system based on integrated accelerometers called motion capture with accelerometers (MOCA that we evaluated for navigation in virtual spaces. Recognition is based on a geometric algorithm that enables efficient and robust detection of rotational movements. Our objective is to demonstrate that such a low-cost and a low-power implementation is suitable for HCI applications. To this purpose, we characterized the system from both a quantitative point of view and a qualitative point of view. First, we performed static and dynamic assessment of movement recognition accuracy. Second, we evaluated the effectiveness of user experience using a 3D game application as a test bed.

Bending-Twisting Motions and Main Interactions in Nucleoplasmin Nuclear Import.

Directory of Open Access Journals (Sweden)

Marcos Tadeu Geraldo

Full Text Available Alpha solenoid proteins play a key role in regulating the classical nuclear import pathway, recognizing a target protein and transporting it into the nucleus. Importin-α (Impα is the solenoid responsible for cargo protein recognition, and it has been extensively studied by X-ray crystallography to understand the binding specificity. To comprehend the main motions of Impα and to extend the information about the critical interactions during carrier-cargo recognition, we surveyed different conformational states based on molecular dynamics (MD and normal mode (NM analyses. Our model of study was a crystallographic structure of Impα complexed with the classical nuclear localization sequence (cNLS from nucleoplasmin (Npl, which was submitted to multiple 100 ns of MD simulations. Representative conformations were selected for calculating the 87 lowest frequencies NMs of vibration, and a displacement approach was applied along each NM. Based on geometric criteria, using the radius of curvature and inter-repeat angles as the reference metrics, the main motions of Impα were described. Moreover, we determined the salt bridges, hydrogen bonds and hydrophobic interactions in the Impα-NplNLS interface. Our results show the bending and twisting motions participating in the recognition of nuclear proteins, allowing the accommodation and adjustment of a classical bipartite NLS sequence. The essential contacts for the nuclear import were also described and were mostly in agreement with previous studies, suggesting that the residues in the cNLS linker region establish important contacts with Impα adjusting the cNLS backbone. The MD simulations combined with NM analysis can be applied to the Impα-NLS system to help understand interactions between Impα and cNLSs and the analysis of non-classic NLSs.

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

NARCIS (Netherlands)

Kamminga, Jacob Wilhelm; Le Viet Duc, Duc Viet; Meijers, Jan Pieter; Bisby, Helena C.; Meratnia, Nirvana; Havinga, Paul J.M.

2018-01-01

Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust

Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

Directory of Open Access Journals (Sweden)

Chunjie Chen

2017-01-01

Full Text Available The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS, which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

Muscle Synergy-Driven Robust Motion Control.

Science.gov (United States)

Min, Kyuengbo; Iwamoto, Masami; Kakei, Shinji; Kimpara, Hideyuki

2018-04-01

Humans are able to robustly maintain desired motion and posture under dynamically changing circumstances, including novel conditions. To accomplish this, the brain needs to optimize the synergistic control between muscles against external dynamic factors. However, previous related studies have usually simplified the control of multiple muscles using two opposing muscles, which are minimum actuators to simulate linear feedback control. As a result, they have been unable to analyze how muscle synergy contributes to motion control robustness in a biological system. To address this issue, we considered a new muscle synergy concept used to optimize the synergy between muscle units against external dynamic conditions, including novel conditions. We propose that two main muscle control policies synergistically control muscle units to maintain the desired motion against external dynamic conditions. Our assumption is based on biological evidence regarding the control of multiple muscles via the corticospinal tract. One of the policies is the group control policy (GCP), which is used to control muscle group units classified based on functional similarities in joint control. This policy is used to effectively resist external dynamic circumstances, such as disturbances. The individual control policy (ICP) assists the GCP in precisely controlling motion by controlling individual muscle units. To validate this hypothesis, we simulated the reinforcement of the synergistic actions of the two control policies during the reinforcement learning of feedback motion control. Using this learning paradigm, the two control policies were synergistically combined to result in robust feedback control under novel transient and sustained disturbances that did not involve learning. Further, by comparing our data to experimental data generated by human subjects under the same conditions as those of the simulation, we showed that the proposed synergy concept may be used to analyze muscle synergy

Pattern recognition in cyclic and discrete skills performance from inertial measurement units

NARCIS (Netherlands)

Seifert, Ludovic; L'Hermette, Maxime; Komar, John; Orth, Dominic; Mell, Florian; Merriaux, Pierre; Grenet, Pierre; Caritu, Yanis; Hérault, Romain; Dovgalecs, Vladislavs; Davids, Keith

2014-01-01

The aim of this study is to compare and validate an Inertial Measurement Unit (IMU) relative to an optic system, and to propose methods for pattern recognition to capture behavioural dynamics during sport performance. IMU validation was conducted by comparing the motions of the two arms of a

Accessing Specific Peptide Recognition by Combinatorial Chemistry

DEFF Research Database (Denmark)

Li, Ming

Molecular recognition is at the basis of all processes for life, and plays a central role in many biological processes, such as protein folding, the structural organization of cells and organelles, signal transduction, and the immune response. Hence, my PhD project is entitled “Accessing Specific...... Peptide Recognition by Combinatorial Chemistry”. Molecular recognition is a specific interaction between two or more molecules through noncovalent bonding, such as hydrogen bonding, metal coordination, van der Waals forces, π−π, hydrophobic, or electrostatic interactions. The association involves kinetic....... Combinatorial chemistry was invented in 1980s based on observation of functional aspects of the adaptive immune system. It was employed for drug development and optimization in conjunction with high-throughput synthesis and screening. (chapter 2) Combinatorial chemistry is able to rapidly produce many thousands...

No strong evidence for lateralisation of word reading and face recognition deficits following posterior brain injury

DEFF Research Database (Denmark)

Gerlach, Christian; Marstrand, Lisbet; Starrfelt, Randi

2014-01-01

Face recognition and word reading are thought to be mediated by relatively independent cognitive systems lateralized to the right and left hemisphere respectively. In this case, we should expect a higher incidence of face recognition problems in patients with right hemisphere injury and a higher......-construction, motion perception), we found that both patient groups performed significantly worse than a matched control group. In particular we found a significant number of face recognition deficits in patients with left hemisphere injury and a significant number of patients with word reading deficits following...... right hemisphere injury. This suggests that face recognition and word reading may be mediated by more bilaterally distributed neural systems than is commonly assumed....

Visual Hierarchy and Mind Motion in Advertising Design

Directory of Open Access Journals (Sweden)

Doaa Farouk Badawy Eldesouky

2013-06-01

Full Text Available Visual hierarchy is a significant concept in the field of advertising, a field that is dominated by effective communication, visual recognition and motion. Designers of advertisements have always been trying to organize the visual hierarchy throughout their advertising designs to aid the eye to recognize information in the desired order, to achieve the ultimate goals of clear perception and effectively delivering the advertising messages. However many assumptions and questions usually rise on how to create effective hierarchy throughout advertising designs and lead the eye and mind of the viewer in the most favorable way. This paper attempts to study visual hierarchy and mind motion in advertising designs and why it is important to develop visual paths when designing an advertisement. It explores the theory behind it, and how the very principles can be used to put these concepts into practice. The paper demonstrates some advertising samples applying visual hierarchy and mind motion in a representation of applying the basics and discussing the results.

Visual Hierarchy and Mind Motion in Advertising Design

Directory of Open Access Journals (Sweden)

Doaa Farouk Badawy Eldesouky

2013-06-01

Full Text Available Visual hierarchy is a significant concept in the field of advertising, a field that is dominated by effective communication, visual recognition and motion. Designers of advertisements have always been trying to organize the visual hierarchy throughout their advertising designs to aid the eye to recognize information in the desired order, to achieve the ultimate goals of clear perception and effectively delivering the advertising messages. However many assumptions and questions usually rise on how to create effective hierarchy throughout advertising designs and lead the eye and mind of the viewer in the most favorable way. This paper attempts to study visual hierarchy and mind motion in advertising designs and why it is important to develop visual paths when designing an advertisement. It explores the theory behind it, and how the very principles can be used to put these concepts into practice. The paper demonstrates some advertising samples applying visual hierarchy and mind motion in a representation of applying the basics and discussing the results. 

Synthesis and Guest Recognition of Switchable Pt-Salphen Based Molecular Tweezers

Directory of Open Access Journals (Sweden)

Lorien Benda

2018-04-01

Full Text Available Molecular tweezers are artificial receptors that have an open cavity generated by two recognition units pre-organized by a spacer. Switchable molecular tweezers, using a stimuli-responsive spacer, are particularly appealing as prototypes of the molecular machines that combine mechanical motion and allosteric recognition properties. In this present study, the synthesis of switchable molecular tweezers composed of a central terpyridine unit substituted in 4,4″ positions by two Pt(II-salphen complexes is reported. The terpyridine ligand can be reversibly converted upon Zn(II coordination from a free ‘U’-shaped closed form to a coordinated ‘W’ open form. This new substitution pattern enables a reverse control of the mechanical motion compared to the previously reported 6,6″ substituted terpyridine-based tweezers. Guest binding studies with aromatic guests showed an intercalation of coronene in the cavity created by the Pt-salphen moieties in the closed conformation. The formation of 1:1 host-guest complex was investigated by a combination of NMR studies and DFT calculations.

Children's conceptions of physical events: explicit and tacit understanding of horizontal motion.

Science.gov (United States)

Howe, Christine; Taylor Tavares, Joana; Devine, Amy

2014-06-01

The conceptual understanding that children display when predicting physical events has been shown to be inferior to the understanding they display when recognizing whether events proceed naturally. This has often been attributed to differences between the explicit engagement with conceptual knowledge required for prediction and the tacit engagement that suffices for recognition, and contrasting theories have been formulated to characterize the differences. Focusing on a theory that emphasizes omission at the explicit level of conceptual elements that are tacitly understood, the paper reports two studies that attempt clarification. The studies are concerned with 6- to 10-year-old children's understanding of, respectively, the direction (141 children) and speed (132 children) of motion in a horizontal direction. Using computer-presented billiards scenarios, the children predicted how balls would move (prediction task) and judged whether or not simulated motion was correct (recognition task). Results indicate that the conceptions underpinning prediction are sometimes interpretable as partial versions of the conceptions underpinning recognition, as the omission hypothesis would imply. However, there are also qualitative differences, which suggest partial dissociation between explicit and tacit understanding. It is suggested that a theoretical perspective that acknowledges this dissociation would provide the optimal framework for future research. © 2013 The British Psychological Society.

An Efficient Solution for Hand Gesture Recognition from Video Sequence

Directory of Open Access Journals (Sweden)

PRODAN, R.-C.

2012-08-01

Full Text Available The paper describes a system of hand gesture recognition by image processing for human robot interaction. The recognition and interpretation of the hand postures acquired through a video camera allow the control of the robotic arm activity: motion - translation and rotation in 3D - and tightening/releasing the clamp. A gesture dictionary was defined and heuristic algorithms for recognition were developed and tested. The system can be used for academic and industrial purposes, especially for those activities where the movements of the robotic arm were not previously scheduled, for training the robot easier than using a remote control. Besides the gesture dictionary, the novelty of the paper consists in a new technique for detecting the relative positions of the fingers in order to recognize the various hand postures, and in the achievement of a robust system for controlling robots by postures of the hands.

Emotion recognition through static faces and moving bodies: a comparison between typically developed adults and individuals with high level of autistic traits.

Science.gov (United States)

Actis-Grosso, Rossana; Bossi, Francesco; Ricciardelli, Paola

2015-01-01

We investigated whether the type of stimulus (pictures of static faces vs. body motion) contributes differently to the recognition of emotions. The performance (accuracy and response times) of 25 Low Autistic Traits (LAT group) young adults (21 males) and 20 young adults (16 males) with either High Autistic Traits or with High Functioning Autism Spectrum Disorder (HAT group) was compared in the recognition of four emotions (Happiness, Anger, Fear, and Sadness) either shown in static faces or conveyed by moving body patch-light displays (PLDs). Overall, HAT individuals were as accurate as LAT ones in perceiving emotions both with faces and with PLDs. Moreover, they correctly described non-emotional actions depicted by PLDs, indicating that they perceived the motion conveyed by the PLDs per se. For LAT participants, happiness proved to be the easiest emotion to be recognized: in line with previous studies we found a happy face advantage for faces, which for the first time was also found for bodies (happy body advantage). Furthermore, LAT participants recognized sadness better by static faces and fear by PLDs. This advantage for motion kinematics in the recognition of fear was not present in HAT participants, suggesting that (i) emotion recognition is not generally impaired in HAT individuals, (ii) the cues exploited for emotion recognition by LAT and HAT groups are not always the same. These findings are discussed against the background of emotional processing in typically and atypically developed individuals.

Emotion recognition through static faces and moving bodies: a comparison between typically-developed adults and individuals with high level of autistic traits

Directory of Open Access Journals (Sweden)

Rossana eActis-Grosso

2015-10-01

Full Text Available We investigated whether the type of stimulus (pictures of static faces vs. body motion contributes differently to the recognition of emotions. The performance (accuracy and response times of 25 Low Autistic Traits (LAT group young adults (21 males and 20 young adults (16 males with either High Autistic Traits (HAT group or with High Functioning Autism Spectrum Disorder was compared in the recognition of four emotions (Happiness, Anger, Fear and Sadness either shown in static faces or conveyed by moving bodies (patch-light displays, PLDs. Overall, HAT individuals were as accurate as LAT ones in perceiving emotions both with faces and with PLDs. Moreover, they correctly described non-emotional actions depicted by PLDs, indicating that they perceived the motion conveyed by the PLDs per se. For LAT participants, happiness proved to be the easiest emotion to be recognized: in line with previous studies we found a happy face advantage for faces, which for the first time was also found for bodies (happy body advantage. Furthermore, LAT participants recognized sadness better by static faces and fear by PLDs. This advantage for motion kinematics in the recognition of fear was not present in HAT participants, suggesting that i emotion recognition is not generally impaired in HAT individuals, ii the cues exploited for emotion recognition by LAT and HAT groups are not always the same. These findings are discussed against the background of emotional processing in typically and atypically developed individuals.

Pattern recognition and modelling of earthquake registrations with interactive computer support

International Nuclear Information System (INIS)

Manova, Katarina S.

2004-01-01

The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

Biological motion perception links diverse facets of theory of mind during middle childhood.

Science.gov (United States)

Rice, Katherine; Anderson, Laura C; Velnoskey, Kayla; Thompson, James C; Redcay, Elizabeth

2016-06-01

Two cornerstones of social development--social perception and theory of mind--undergo brain and behavioral changes during middle childhood, but the link between these developing domains is unclear. One theoretical perspective argues that these skills represent domain-specific areas of social development, whereas other perspectives suggest that both skills may reflect a more integrated social system. Given recent evidence from adults that these superficially different domains may be related, the current study examined the developmental relation between these social processes in 52 children aged 7 to 12 years. Controlling for age and IQ, social perception (perception of biological motion in noise) was significantly correlated with two measures of theory of mind: one in which children made mental state inferences based on photographs of the eye region of the face and another in which children made mental state inferences based on stories. Social perception, however, was not correlated with children's ability to make physical inferences from stories about people. Furthermore, the mental state inference tasks were not correlated with each other, suggesting a role for social perception in linking various facets of theory of mind. Copyright © 2015 Elsevier Inc. All rights reserved.

Human detection and motion analysis at security points

Science.gov (United States)

Ozer, I. Burak; Lv, Tiehan; Wolf, Wayne H.

2003-08-01

This paper presents a real-time video surveillance system for the recognition of specific human activities. Specifically, the proposed automatic motion analysis is used as an on-line alarm system to detect abnormal situations in a campus environment. A smart multi-camera system developed at Princeton University is extended for use in smart environments in which the camera detects the presence of multiple persons as well as their gestures and their interaction in real-time.

Computational structural biology: methods and applications

National Research Council Canada - National Science Library

Schwede, Torsten; Peitsch, Manuel Claude

2008-01-01

... sequencing reinforced the observation that structural information is needed to understand the detailed function and mechanism of biological molecules such as enzyme reactions and molecular recognition events. Furthermore, structures are obviously key to the design of molecules with new or improved functions. In this context, computational structural biology...

Projective Structure from Two Uncalibrated Images: Structure from Motion and Recognition

Science.gov (United States)

1992-09-01

correspondence between points in Maybank 1990). The question, therefore, is why look for both views more of a problem, and hence, may make the...plane is fixed with respect to the 1987, Faugeras, Luong and Maybank 1992). The prob- camera coordinate frame. A rigid camera motion, there- lem of...the second reference Rieger-Lawton 1985, Faugeras and Maybank 1990, Hil- plane (assuming the four object points Pi, j = 1, ...,4, dreth 1991, Faugeras

Speech recognition employing biologically plausible receptive fields

DEFF Research Database (Denmark)

Fereczkowski, Michal; Bothe, Hans-Heinrich

2011-01-01

spectro-temporal receptive fields to auditory spectrogram input, motivated by the auditory pathway of humans, and ii) the adaptation or learning algorithms involved are biologically inspired. This is in contrast to state-of-the-art combinations of Mel-frequency cepstral coefficients and Hidden Markov...

Individual differences in the perception of biological motion and fragmented figures are not correlated

Directory of Open Access Journals (Sweden)

Eunice L Jung

2013-10-01

Full Text Available We live in a cluttered, dynamic visual environment that poses a challenge for the visual system: for objects, including those that move about, to be perceived, information specifying those objects must be integrated over space and over time. Does a single, omnibus mechanism perform this grouping operation, or does grouping depend on separate processes specialized for different feature aspects of the object? To address this question, we tested a large group of healthy young adults on their abilities to perceive static fragmented figures embedded in noise and to perceive dynamic point-light biological motion figures embedded in dynamic noise. There were indeed substantial individual differences in performance on both tasks, but none of the statistical tests we applied to this data set uncovered a significant correlation between those performance measures. These results suggest that the two tasks, despite their superficial similarity, require different segmentation and grouping processes that are largely unrelated to one another. Whether those processes are embodied in distinct neural mechanisms remains an open question.

Noisy Ocular Recognition Based on Three Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Min Beom Lee

2017-12-01

Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

Science.gov (United States)

Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

2017-12-17

In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

A recurrent dynamic model for correspondence-based face recognition.

Science.gov (United States)

Wolfrum, Philipp; Wolff, Christian; Lücke, Jörg; von der Malsburg, Christoph

2008-12-29

Our aim here is to create a fully neural, functionally competitive, and correspondence-based model for invariant face recognition. By recurrently integrating information about feature similarities, spatial feature relations, and facial structure stored in memory, the system evaluates face identity ("what"-information) and face position ("where"-information) using explicit representations for both. The network consists of three functional layers of processing, (1) an input layer for image representation, (2) a middle layer for recurrent information integration, and (3) a gallery layer for memory storage. Each layer consists of cortical columns as functional building blocks that are modeled in accordance with recent experimental findings. In numerical simulations we apply the system to standard benchmark databases for face recognition. We find that recognition rates of our biologically inspired approach lie in the same range as recognition rates of recent and purely functionally motivated systems.

Restoration of non-uniform exposure motion blurred image

Science.gov (United States)

Luo, Yuanhong; Xu, Tingfa; Wang, Ningming; Liu, Feng

2014-11-01

Restoring motion-blurred image is the key technologies in the opto-electronic detection system. The imaging sensors such as CCD and infrared imaging sensor, which are mounted on the motion platforms, quickly move together with the platforms of high speed. As a result, the images become blur. The image degradation will cause great trouble for the succeeding jobs such as objects detection, target recognition and tracking. So the motion-blurred images must be restoration before detecting motion targets in the subsequent images. On the demand of the real weapon task, in order to deal with targets in the complex background, this dissertation uses the new theories in the field of image processing and computer vision to research the new technology of motion deblurring and motion detection. The principle content is as follows: 1) When the prior knowledge about degradation function is unknown, the uniform motion blurred images are restored. At first, the blur parameters, including the motion blur extent and direction of PSF(point spread function), are estimated individually in domain of logarithmic frequency. The direction of PSF is calculated by extracting the central light line of the spectrum, and the extent is computed by minimizing the correction between the fourier spectrum of the blurred image and a detecting function. Moreover, in order to remove the strip in the deblurred image, windows technique is employed in the algorithm, which makes the deblurred image clear. 2) According to the principle of infrared image non-uniform exposure, a new restoration model for infrared blurred images is developed. The fitting of infrared image non-uniform exposure curve is performed by experiment data. The blurred images are restored by the fitting curve.

Improving on hidden Markov models: An articulatorily constrained, maximum likelihood approach to speech recognition and speech coding

Energy Technology Data Exchange (ETDEWEB)

Hogden, J.

1996-11-05

The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation may decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.

Auditory perception of a human walker.

Science.gov (United States)

Cottrell, David; Campbell, Megan E J

2014-01-01

When one hears footsteps in the hall, one is able to instantly recognise it as a person: this is an everyday example of auditory biological motion perception. Despite the familiarity of this experience, research into this phenomenon is in its infancy compared with visual biological motion perception. Here, two experiments explored sensitivity to, and recognition of, auditory stimuli of biological and nonbiological origin. We hypothesised that the cadence of a walker gives rise to a temporal pattern of impact sounds that facilitates the recognition of human motion from auditory stimuli alone. First a series of detection tasks compared sensitivity with three carefully matched impact sounds: footsteps, a ball bouncing, and drumbeats. Unexpectedly, participants were no more sensitive to footsteps than to impact sounds of nonbiological origin. In the second experiment participants made discriminations between pairs of the same stimuli, in a series of recognition tasks in which the temporal pattern of impact sounds was manipulated to be either that of a walker or the pattern more typical of the source event (a ball bouncing or a drumbeat). Under these conditions, there was evidence that both temporal and nontemporal cues were important in recognising theses stimuli. It is proposed that the interval between footsteps, which reflects a walker's cadence, is a cue for the recognition of the sounds of a human walking.

THE ALLWISE MOTION SURVEY, PART 2

Energy Technology Data Exchange (ETDEWEB)

Kirkpatrick, J. Davy; Kellogg, Kendra; Fajardo-Acosta, Sergio; Gelino, Christopher R.; Schurr, Steven D.; Cutri, Roc M.; Conrow, Tim [Infrared Processing and Analysis Center, MS 100-22, California Institute of Technology, Pasadena, CA 91125 (United States); Schneider, Adam C.; Cushing, Michael C.; Greco, Jennifer [Department of Physics and Astronomy, MS 111, University of Toledo, 2801 W. Bancroft Street, Toledo, OH 43606-3328 (United States); Mace, Gregory N. [Department of Astronomy, University of Texas at Austin, Austin, TX 78712 (United States); Wright, Edward L.; Logsdon, Sarah E.; Martin, Emily C.; McLean, Ian S. [Department of Physics and Astronomy, UCLA, 430 Portola Plaza, Box 951547, Los Angeles, CA 90095-1547 (United States); Eisenhardt, Peter R. M.; Stern, Daniel [Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109 (United States); Faherty, Jacqueline K.; Sheppard, Scott S. [Department of Terrestrial Magnetism, Carnegie Institution of Washington, Washington, DC 20015 (United States); Lansbury, George B., E-mail: davy@ipac.caltech.edu [Department of Physics, Durham University, Durham DH1 3LE (United Kingdom)

2016-06-01

We use the AllWISE Data Release to continue our search for Wide-field Infrared Survey Explorer ( WISE )-detected motions. In this paper, we publish another 27,846 motion objects, bringing the total number to 48,000 when objects found during our original AllWISE motion survey are included. We use this list, along with the lists of confirmed WISE -based motion objects from the recent papers by Luhman and by Schneider et al., and candidate motion objects from the recent paper by Gagné et al., to search for widely separated, common-proper-motion systems. We identify 1039 such candidate systems. All 48,000 objects are further analyzed using color–color and color–mag plots to provide possible characterizations prior to spectroscopic follow-up. We present spectra of 172 of these, supplemented with new spectra of 23 comparison objects from the literature, and provide classifications and physical interpretations of interesting sources. Highlights include: (1) the identification of three G/K dwarfs that can be used as standard candles to study clumpiness and grain size in nearby molecular clouds because these objects are currently moving behind the clouds, (2) the confirmation/discovery of several M, L, and T dwarfs and one white dwarf whose spectrophotometric distance estimates place them 5–20 pc from the Sun, (3) the suggestion that the Na i “D” line be used as a diagnostic tool for interpreting and classifying metal-poor late-M and L dwarfs, (4) the recognition of a triple system including a carbon dwarf and late-M subdwarf, for which model fits of the late-M subdwarf (giving [Fe/H] ≈ −1.0) provide a measured metallicity for the carbon star, and (5) a possible 24 pc distant K5 dwarf + peculiar red L5 system with an apparent physical separation of 0.1 pc.

THE ALLWISE MOTION SURVEY, PART 2

International Nuclear Information System (INIS)

Kirkpatrick, J. Davy; Kellogg, Kendra; Fajardo-Acosta, Sergio; Gelino, Christopher R.; Schurr, Steven D.; Cutri, Roc M.; Conrow, Tim; Schneider, Adam C.; Cushing, Michael C.; Greco, Jennifer; Mace, Gregory N.; Wright, Edward L.; Logsdon, Sarah E.; Martin, Emily C.; McLean, Ian S.; Eisenhardt, Peter R. M.; Stern, Daniel; Faherty, Jacqueline K.; Sheppard, Scott S.; Lansbury, George B.

2016-01-01

We use the AllWISE Data Release to continue our search for Wide-field Infrared Survey Explorer ( WISE )-detected motions. In this paper, we publish another 27,846 motion objects, bringing the total number to 48,000 when objects found during our original AllWISE motion survey are included. We use this list, along with the lists of confirmed WISE -based motion objects from the recent papers by Luhman and by Schneider et al., and candidate motion objects from the recent paper by Gagné et al., to search for widely separated, common-proper-motion systems. We identify 1039 such candidate systems. All 48,000 objects are further analyzed using color–color and color–mag plots to provide possible characterizations prior to spectroscopic follow-up. We present spectra of 172 of these, supplemented with new spectra of 23 comparison objects from the literature, and provide classifications and physical interpretations of interesting sources. Highlights include: (1) the identification of three G/K dwarfs that can be used as standard candles to study clumpiness and grain size in nearby molecular clouds because these objects are currently moving behind the clouds, (2) the confirmation/discovery of several M, L, and T dwarfs and one white dwarf whose spectrophotometric distance estimates place them 5–20 pc from the Sun, (3) the suggestion that the Na i “D” line be used as a diagnostic tool for interpreting and classifying metal-poor late-M and L dwarfs, (4) the recognition of a triple system including a carbon dwarf and late-M subdwarf, for which model fits of the late-M subdwarf (giving [Fe/H] ≈ −1.0) provide a measured metallicity for the carbon star, and (5) a possible 24 pc distant K5 dwarf + peculiar red L5 system with an apparent physical separation of 0.1 pc.

The role of pattern recognition in creative problem solving: a case study in search of new mathematics for biology.

Science.gov (United States)

Hong, Felix T

2013-09-01

Rosen classified sciences into two categories: formalizable and unformalizable. Whereas formalizable sciences expressed in terms of mathematical theories were highly valued by Rutherford, Hutchins pointed out that unformalizable parts of soft sciences are of genuine interest and importance. Attempts to build mathematical theories for biology in the past century was met with modest and sporadic successes, and only in simple systems. In this article, a qualitative model of humans' high creativity is presented as a starting point to consider whether the gap between soft and hard sciences is bridgeable. Simonton's chance-configuration theory, which mimics the process of evolution, was modified and improved. By treating problem solving as a process of pattern recognition, the known dichotomy of visual thinking vs. verbal thinking can be recast in terms of analog pattern recognition (non-algorithmic process) and digital pattern recognition (algorithmic process), respectively. Additional concepts commonly encountered in computer science, operations research and artificial intelligence were also invoked: heuristic searching, parallel and sequential processing. The refurbished chance-configuration model is now capable of explaining several long-standing puzzles in human cognition: a) why novel discoveries often came without prior warning, b) why some creators had no ideas about the source of inspiration even after the fact, c) why some creators were consistently luckier than others, and, last but not least, d) why it was so difficult to explain what intuition, inspiration, insight, hunch, serendipity, etc. are all about. The predictive power of the present model was tested by means of resolving Zeno's paradox of Achilles and the Tortoise after one deliberately invoked visual thinking. Additional evidence of its predictive power must await future large-scale field studies. The analysis was further generalized to constructions of scientific theories in general. This approach

Human Activity Recognition in a Car with Embedded Devices

Directory of Open Access Journals (Sweden)

Danilo Burbano

2015-11-01

Full Text Available Detection and prediction of drowsiness is key for the implementation of intelligent vehicles aimed to prevent highway crashes. There are several approaches for such solution. In thispaper the computer vision approach will be analysed, where embedded devices (e.g.videocameras are used along with machine learning and pattern recognition techniques for implementing suitable solutions for detecting driver fatigue. Most of the research in computer vision systems focused on the analysis of blinks, this is a notable solution when it is combined with additional patterns like yawing or head motion for the recognition of drowsiness. The first step in this approach is the face recognition, where AdaBoost algorithm shows accurate results for the feature extraction, whereas regarding the detection of drowsiness the data-driven classifiers such as Support Vector Machine (SVM yields remarkable results. One underlying component for implementing a computer vision technology for detection of drowsiness is a database of spontaneous images from the Facial Action Coding System (FACS, where the classifier can be trained accordingly. This paper introduces a straightforward prototype for detection of drowsiness, where the Viola-Jones method is used for face recognition and cascade classifier is used for the detection of a contiguous sequence of eyes closed, which a reconsidered as drowsiness.

Brownian motion of tethered nanowires.

Science.gov (United States)

Ota, Sadao; Li, Tongcang; Li, Yimin; Ye, Ziliang; Labno, Anna; Yin, Xiaobo; Alam, Mohammad-Reza; Zhang, Xiang

2014-05-01

Brownian motion of slender particles near a boundary is ubiquitous in biological systems and in nanomaterial assembly, but the complex hydrodynamic interaction in those systems is still poorly understood. Here, we report experimental and computational studies of the Brownian motion of silicon nanowires tethered on a substrate. An optical interference method enabled direct observation of microscopic rotations of the slender bodies in three dimensions with high angular and temporal resolutions. This quantitative observation revealed anisotropic and angle-dependent hydrodynamic wall effects: rotational diffusivity in inclined and azimuth directions follows different power laws as a function of the length, ∼ L(-2.5) and ∼ L(-3), respectively, and is more hindered for smaller inclined angles. In parallel, we developed an implicit simulation technique that takes the complex wire-wall hydrodynamic interactions into account efficiently, the result of which agreed well with the experimentally observed angle-dependent diffusion. The demonstrated techniques provide a platform for studying the microrheology of soft condensed matters, such as colloidal and biological systems near interfaces, and exploring the optimal self-assembly conditions of nanostructures.

Object Recognition and Localization: The Role of Tactile Sensors

Directory of Open Access Journals (Sweden)

Achint Aggarwal

2014-02-01

Full Text Available Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.

The Constitutionality of a Biological Father's Recognition as a Parent

Directory of Open Access Journals (Sweden)

A Louw

2010-12-01

Full Text Available Despite the increased recognition afforded to biological fathers as legal parents, the Children's Act 38 of 2005 still does not treat fathers on the same basis as mothers as far as the automatic allocation of parental responsibilities and rights is concerned. This article investigates the constitutionality of the differential treatment of fathers in this respect, given South Africa's international obligations, especially in terms of the United Nations Convention on the Rights of the Child, to ensure that both parents have common responsibilities for the upbringing of their child. After a brief consideration of the constitutionality of the mother's position as parent, the constitutionality of the father's position is investigated, firstly, with reference to Section 9 of the Constitution and the question of whether the differentiation between mothers and fathers as far as the allocation of parental responsibilities and rights is concerned, amounts to unfair discrimination. The inquiry also considers whether the differentiation between committed fathers (that is, those who have shown the necessary commitment in terms of Sections 20 and 21 of the Children's Act to acquire parental responsibilities and rights and uncommitted fathers may amount to discrimination on an unspecified ground. Since the limitation of the father's rights to equality may be justifiable, the outcomes of both inquiries are shown to be inconclusive. Finally, the legal position of the father is considered in relation to the child's constitutional rights – the rights to parental care and the right of the child to the paramountcy of its interests embodied in Section 28 of the Constitution. While there appears to be some justification for the limitation of the child's right to committed paternal care, it is submitted that an equalisation of the legal position of mothers and fathers as far as the automatic acquisition of parental responsibilities and rights is concerned, is not

Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences

Directory of Open Access Journals (Sweden)

Mozerov M

2010-01-01

Full Text Available A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.

Multi-task learning with group information for human action recognition

Science.gov (United States)

Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

NUI framework based on real-time head pose estimation and hand gesture recognition

Directory of Open Access Journals (Sweden)

Kim Hyunduk

2016-01-01

Full Text Available The natural user interface (NUI is used for the natural motion interface without using device or tool such as mice, keyboards, pens and markers. In this paper, we develop natural user interface framework based on two recognition module. First module is real-time head pose estimation module using random forests and second module is hand gesture recognition module, named Hand gesture Key Emulation Toolkit (HandGKET. Using the head pose estimation module, we can know where the user is looking and what the user’s focus of attention is. Moreover, using the hand gesture recognition module, we can also control the computer using the user’s hand gesture without mouse and keyboard. In proposed framework, the user’s head direction and hand gesture are mapped into mouse and keyboard event, respectively.

RGBD Video Based Human Hand Trajectory Tracking and Gesture Recognition System

Directory of Open Access Journals (Sweden)

Weihua Liu

2015-01-01

Full Text Available The task of human hand trajectory tracking and gesture trajectory recognition based on synchronized color and depth video is considered. Toward this end, in the facet of hand tracking, a joint observation model with the hand cues of skin saliency, motion and depth is integrated into particle filter in order to move particles to local peak in the likelihood. The proposed hand tracking method, namely, salient skin, motion, and depth based particle filter (SSMD-PF, is capable of improving the tracking accuracy considerably, in the context of the signer performing the gesture toward the camera device and in front of moving, cluttered backgrounds. In the facet of gesture recognition, a shape-order context descriptor on the basis of shape context is introduced, which can describe the gesture in spatiotemporal domain. The efficient shape-order context descriptor can reveal the shape relationship and embed gesture sequence order information into descriptor. Moreover, the shape-order context leads to a robust score for gesture invariant. Our approach is complemented with experimental results on the settings of the challenging hand-signed digits datasets and American sign language dataset, which corroborate the performance of the novel techniques.

Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

Directory of Open Access Journals (Sweden)

Sara Saeedi

2015-08-01

Full Text Available Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the computation cost of activity recognition on the smartphones. We evaluated a variety of feature spaces and a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Trees, Artificial Neural Networks and Support Vector Machine classifiers. A smartphone app that performs activity recognition is being developed to collect data and send them to a server for activity recognition. Using extensive experiments, the performance of various feature spaces has been evaluated. The results showed that the Bayesian Network classifier yields recognition accuracy of 96.21% using four features while requiring fewer computations.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

Directory of Open Access Journals (Sweden)

Shu-Yin Chiang

2016-12-01

Full Text Available Ubiquitous health care (UHC is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN. The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

Science.gov (United States)

Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

2016-12-03

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

Mobile user identity sensing using the motion sensor

Science.gov (United States)

Zhao, Xi; Feng, Tao; Xu, Lei; Shi, Weidong

2014-05-01

Employing mobile sensor data to recognize user behavioral activities has been well studied in recent years. However, to adopt the data as a biometric modality has rarely been explored. Existing methods either used the data to recognize gait, which is considered as a distinguished identity feature; or segmented a specific kind of motion for user recognition, such as phone picking-up motion. Since the identity and the motion gesture jointly affect motion data, to fix the gesture (walking or phone picking-up) definitively simplifies the identity sensing problem. However, it meanwhile introduces the complexity from gesture detection or requirement on a higher sample rate from motion sensor readings, which may draw the battery fast and affect the usability of the phone. In general, it is still under investigation that motion based user authentication in a large scale satisfies the accuracy requirement as a stand-alone biometrics modality. In this paper, we propose a novel approach to use the motion sensor readings for user identity sensing. Instead of decoupling the user identity from a gesture, we reasonably assume users have their own distinguishing phone usage habits and extract the identity from fuzzy activity patterns, represented by a combination of body movements whose signals in chains span in relative low frequency spectrum and hand movements whose signals span in relative high frequency spectrum. Then Bayesian Rules are applied to analyze the dependency of different frequency components in the signals. During testing, a posterior probability of user identity given the observed chains can be computed for authentication. Tested on an accelerometer dataset with 347 users, our approach has demonstrated the promising results.

DESIGN REVIEW OF CAD MODELS USING A NUI LEAP MOTION SENSOR

Directory of Open Access Journals (Sweden)

GÎRBACIA Florin

2015-06-01

Full Text Available Natural User Interfaces (NUI is a relatively new area of research that aims to develop humancomputer interfaces, natural and intuitive, using voice commands, hand movements and gesture recognition, similar to communication between people which also implies body language and gestures. In this paper is presented a natural designed workspace which acquires the user's motion using a Leap Motion sensor and visualizes the CAD models using a CAVE-like 3D visualisation system. The user can modify complex CAD models using bimanual gesture commands in a 3D virtual environment. The developed bimanual gestures for rotate, pan, zoom and explode are presented. From the conducted experiments is established that Leap Motion NUI sensor provides an intuitive tool for design review of CAD models, performed even by users with no experience in CAD systems and virtual environments.

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Directory of Open Access Journals (Sweden)

Yanjuan Geng

2017-01-01

Full Text Available Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC and multiposition classifier (MPC have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.. The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.

Evaluation of the leap motion controller as a new contact-free pointing device.

Science.gov (United States)

Bachmann, Daniel; Weichert, Frank; Rinkenauer, Gerhard

2014-12-24

This paper presents a Fitts' law-based analysis of the user's performance in selection tasks with the Leap Motion Controller compared with a standard mouse device. The Leap Motion Controller (LMC) is a new contact-free input system for gesture-based human-computer interaction with declared sub-millimeter accuracy. Up to this point, there has hardly been any systematic evaluation of this new system available. With an error rate of 7.8% for the LMC and 2.8% for the mouse device, movement times twice as large as for a mouse device and high overall effort ratings, the Leap Motion Controller's performance as an input device for everyday generic computer pointing tasks is rather limited, at least with regard to the selection recognition provided by the LMC.

Robotic Hand-Assisted Training for Spinal Cord Injury Driven by Myoelectric Pattern Recognition: A Case Report.

Science.gov (United States)

Lu, Zhiyuan; Tong, Kai-Yu; Shin, Henry; Stampas, Argyrios; Zhou, Ping

2017-10-01

A 51-year-old man with an incomplete C6 spinal cord injury sustained 26 yrs ago attended twenty 2-hr visits over 10 wks for robot-assisted hand training driven by myoelectric pattern recognition. In each visit, his right hand was assisted to perform motions by an exoskeleton robot, while the robot was triggered by his own motion intentions. The hand robot was designed for this study, which can perform six kinds of motions, including hand closing/opening; thumb, index finger, and middle finger closing/opening; and middle, ring, and little fingers closing/opening. After the training, his grip force increased from 13.5 to 19.6 kg, his pinch force remained the same (5.0 kg), his score of Box and Block test increased from 32 to 39, and his score from the Graded Redefined Assessment of Strength, Sensibility, and Prehension test Part 4.B increased from 22 to 24. He accomplished the tasks in the Graded Redefined Assessment of Strength, Sensibility, and Prehension test Part 4.B 28.8% faster on average. The results demonstrate the feasibility and effectiveness of robot-assisted training driven by myoelectric pattern recognition after spinal cord injury.

Who was that masked man? Conjoint representations of intrinsic motions with actor appearance.

Science.gov (United States)

Kersten, Alan W; Earles, Julie L; Negri, Leehe

2018-09-01

Motion plays an important role in recognising animate creatures. This research supports a distinction between intrinsic and extrinsic motions in their relationship to identifying information about the characters performing the motions. Participants viewed events involving costumed human characters. Intrinsic motions involved relative movements of a character's body parts, whereas extrinsic motions involved movements with respect to external landmarks. Participants were later tested for recognition of the motions and who had performed them. The critical test items involved familiar characters performing motions that had previously been performed by other characters. Participants falsely recognised extrinsic conjunction items, in which characters followed the paths of other characters, more often than intrinsic conjunction items, in which characters moved in the manner of other characters. In contrast, participants falsely recognised new extrinsic motions less often than new intrinsic motions, suggesting that they remembered extrinsic motions but had difficulty remembering who had performed them. Modelling of receiver operating characteristics indicated that participants discriminated old items from intrinsic conjunction items via familiarity, consistent with conjoint representations of intrinsic motion and identity information. In contrast, participants used recollection to distinguish old items from extrinsic conjunction items, consistent with separate but associated representations of extrinsic motion and identity information.

A Motion Planning Approach to Studying Molecular Motions

KAUST Repository

Amato, Nancy M.

2010-01-01

While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer\\'s disease are associated with protein misfolding and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA folding kinetics can regulate gene expression at the translational level. Knowledge of the stability, folding, kinetics and detailed mechanics of the folding process may help provide insight into how proteins and RNAs fold. In this paper, we present an overview of our work with a computational method we have adapted from robotic motion planning to study molecular motions. We have validated against experimental data and have demonstrated that our method can capture biological results such as stochastic folding pathways, population kinetics of various conformations, and relative folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes of both proteins and RNAs. We have validated these techniques by showing that we observe the same relative folding rates as shown in experiments for structurally similar protein molecules that exhibit different folding behaviors. Our analysis has also been able to predict the same relative gene expression rate for wild-type MS2 phage RNA and three of its mutants.

Appearance-based human gesture recognition using multimodal features for human computer interaction

Science.gov (United States)

Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

2011-03-01

The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

Detecting Lateral Motion using Light's Orbital Angular Momentum.

Science.gov (United States)

Cvijetic, Neda; Milione, Giovanni; Ip, Ezra; Wang, Ting

2015-10-23

Interrogating an object with a light beam and analyzing the scattered light can reveal kinematic information about the object, which is vital for applications ranging from autonomous vehicles to gesture recognition and virtual reality. We show that by analyzing the change in the orbital angular momentum (OAM) of a tilted light beam eclipsed by a moving object, lateral motion of the object can be detected in an arbitrary direction using a single light beam and without object image reconstruction. We observe OAM spectral asymmetry that corresponds to the lateral motion direction along an arbitrary axis perpendicular to the plane containing the light beam and OAM measurement axes. These findings extend OAM-based remote sensing to detection of non-rotational qualities of objects and may also have extensions to other electromagnetic wave regimes, including radio and sound.

Sampling protein motion and solvent effect during ligand binding

Science.gov (United States)

Limongelli, Vittorio; Marinelli, Luciana; Cosconati, Sandro; La Motta, Concettina; Sartini, Stefania; Mugnaini, Laura; Da Settimo, Federico; Novellino, Ettore; Parrinello, Michele

2012-01-01

An exhaustive description of the molecular recognition mechanism between a ligand and its biological target is of great value because it provides the opportunity for an exogenous control of the related process. Very often this aim can be pursued using high resolution structures of the complex in combination with inexpensive computational protocols such as docking algorithms. Unfortunately, in many other cases a number of factors, like protein flexibility or solvent effects, increase the degree of complexity of ligand/protein interaction and these standard techniques are no longer sufficient to describe the binding event. We have experienced and tested these limits in the present study in which we have developed and revealed the mechanism of binding of a new series of potent inhibitors of Adenosine Deaminase. We have first performed a large number of docking calculations, which unfortunately failed to yield reliable results due to the dynamical character of the enzyme and the complex role of the solvent. Thus, we have stepped up the computational strategy using a protocol based on metadynamics. Our approach has allowed dealing with protein motion and solvation during ligand binding and finally identifying the lowest energy binding modes of the most potent compound of the series, 4-decyl-pyrazolo[1,5-a]pyrimidin-7-one. PMID:22238423

Biological origins of color categorization.

Science.gov (United States)

Skelton, Alice E; Catchpole, Gemma; Abbott, Joshua T; Bosten, Jenny M; Franklin, Anna

2017-05-23

The biological basis of the commonality in color lexicons across languages has been hotly debated for decades. Prior evidence that infants categorize color could provide support for the hypothesis that color categorization systems are not purely constructed by communication and culture. Here, we investigate the relationship between infants' categorization of color and the commonality across color lexicons, and the potential biological origin of infant color categories. We systematically mapped infants' categorical recognition memory for hue onto a stimulus array used previously to document the color lexicons of 110 nonindustrialized languages. Following familiarization to a given hue, infants' response to a novel hue indicated that their recognition memory parses the hue continuum into red, yellow, green, blue, and purple categories. Infants' categorical distinctions aligned with common distinctions in color lexicons and are organized around hues that are commonly central to lexical categories across languages. The boundaries between infants' categorical distinctions also aligned, relative to the adaptation point, with the cardinal axes that describe the early stages of color representation in retinogeniculate pathways, indicating that infant color categorization may be partly organized by biological mechanisms of color vision. The findings suggest that color categorization in language and thought is partially biologically constrained and have implications for broader debate on how biology, culture, and communication interact in human cognition.

Recognition of tennis serve performed by a digital player: comparison among polygon, shadow, and stick-figure models.

Directory of Open Access Journals (Sweden)

Hirofumi Ida

Full Text Available The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster of one of the joint rotations (wrist, elbow, or shoulder. Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.

Recognition of tennis serve performed by a digital player: comparison among polygon, shadow, and stick-figure models.

Science.gov (United States)

Ida, Hirofumi; Fukuhara, Kazunobu; Ishii, Motonobu

2012-01-01

The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure) were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster) of one of the joint rotations (wrist, elbow, or shoulder). Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.

iFER: facial expression recognition using automatically selected geometric eye and eyebrow features

Science.gov (United States)

Oztel, Ismail; Yolcu, Gozde; Oz, Cemil; Kazan, Serap; Bunyak, Filiz

2018-03-01

Facial expressions have an important role in interpersonal communications and estimation of emotional states or intentions. Automatic recognition of facial expressions has led to many practical applications and became one of the important topics in computer vision. We present a facial expression recognition system that relies on geometry-based features extracted from eye and eyebrow regions of the face. The proposed system detects keypoints on frontal face images and forms a feature set using geometric relationships among groups of detected keypoints. Obtained feature set is refined and reduced using the sequential forward selection (SFS) algorithm and fed to a support vector machine classifier to recognize five facial expression classes. The proposed system, iFER (eye-eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production. Preliminary experiments on benchmark datasets produced promising results outperforming previous facial expression recognition studies using partial face features, and comparable results to studies using whole face information, only slightly lower by ˜ 2.5 % compared to the best whole face facial recognition system while using only ˜ 1 / 3 of the facial region.

3D Interest Point Detection using Local Surface Characteristics with Application in Action Recognition

DEFF Research Database (Denmark)

Holte, Michael Boelstoft

2014-01-01

. The proposed Difference-of-Normals (DoN) 3D IP detector operates on the surface mesh, and evaluates the surface structure (curvature) locally (per vertex) in the mesh data. We present an exam- ple of application in action recognition from a sequence of 3-dimensional geometrical data, where local 3D motion de...

Flapping motion and force generation in a viscoelastic fluid

Science.gov (United States)

Normand, Thibaud; Lauga, Eric

2008-12-01

In a variety of biological situations, swimming cells have to move through complex fluids. Similarly, mucociliary clearance involves the transport of polymeric fluids by beating cilia. Here, we consider the extent to which complex fluids could be exploited for force generation on small scales. We consider a prototypical reciprocal motion (i.e., identical under time-reversal symmetry): the periodic flapping of a tethered semi-infinite plane. In the Newtonian limit, such motion cannot be used for force generation according to Purcell’s scallop theorem. In a polymeric fluid (Oldroyd-B, and its generalization), we show that this is not the case and calculate explicitly the forces on the flapper for small-amplitude sinusoidal motion. Three setups are considered: a flapper near a wall, a flapper in a wedge, and a two-dimensional scalloplike flapper. In all cases, we show that at quadratic order in the oscillation amplitude, the tethered flapping motion induces net forces, but no average flow. Our results demonstrate therefore that the scallop theorem is not valid in polymeric fluids. The reciprocal component of the movement of biological appendages such as cilia can thus generate nontrivial forces in polymeric fluid such as mucus, and normal-stress differences can be exploited as a pure viscoelastic force generation and propulsion method.

8 CFR 1292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

Science.gov (United States)

2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 1292.2...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization...

Threshold models of recognition and the recognition heuristic

Directory of Open Access Journals (Sweden)

Edgar Erdfelder

2011-02-01

Full Text Available According to the recognition heuristic (RH theory, decisions follow the recognition principle: Given a high validity of the recognition cue, people should prefer recognized choice options compared to unrecognized ones. Assuming that the memory strength of choice options is strongly correlated with both the choice criterion and recognition judgments, the RH is a reasonable strategy that approximates optimal decisions with a minimum of cognitive effort (Davis-Stober, Dana, and Budescu, 2010. However, theories of recognition memory are not generally compatible with this assumption. For example, some threshold models of recognition presume that recognition judgments can arise from two types of cognitive states: (1 certainty states in which judgments are almost perfectly correlated with memory strength and (2 uncertainty states in which recognition judgments reflect guessing rather than differences in memory strength. We report an experiment designed to test the prediction that the RH applies to certainty states only. Our results show that memory states rather than recognition judgments affect use of recognition information in binary decisions.

Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition.

Science.gov (United States)

Lu, Zhiyuan; Chen, Xiang; Zhang, Xu; Tong, Kay-Yu; Zhou, Ping

2017-08-01

Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user's intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.

Eye tracking reveals a crucial role for facial motion in recognition of faces by infants.

Science.gov (United States)

Xiao, Naiqi G; Quinn, Paul C; Liu, Shaoying; Ge, Liezhong; Pascalis, Olivier; Lee, Kang

2015-06-01

Current knowledge about face processing in infancy comes largely from studies using static face stimuli, but faces that infants see in the real world are mostly moving ones. To bridge this gap, 3-, 6-, and 9-month-old Asian infants (N = 118) were familiarized with either moving or static Asian female faces, and then their face recognition was tested with static face images. Eye-tracking methodology was used to record eye movements during the familiarization and test phases. The results showed a developmental change in eye movement patterns, but only for the moving faces. In addition, the more infants shifted their fixations across facial regions, the better their face recognition was, but only for the moving faces. The results suggest that facial movement influences the way faces are encoded from early in development. (c) 2015 APA, all rights reserved).

Evaluation of the Leap Motion Controller as a New Contact-Free Pointing Device

Directory of Open Access Journals (Sweden)

Daniel Bachmann

2014-12-01

Full Text Available This paper presents a Fitts’ law-based analysis of the user’s performance in selection tasks with the Leap Motion Controller compared with a standard mouse device. The Leap Motion Controller (LMC is a new contact-free input system for gesture-based human-computer interaction with declared sub-millimeter accuracy. Up to this point, there has hardly been any systematic evaluation of this new system available. With an error rate of 7.8% for the LMC and 2.8% for the mouse device, movement times twice as large as for a mouse device and high overall effort ratings, the Leap Motion Controller’s performance as an input device for everyday generic computer pointing tasks is rather limited, at least with regard to the selection recognition provided by the LMC.

Introducing memory and association mechanism into a biologically inspired visual model.

Science.gov (United States)

Qiao, Hong; Li, Yinlin; Tang, Tang; Wang, Peng

2014-09-01

A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.

Modeling recognition memory using the similarity structure of natural input

NARCIS (Netherlands)

Lacroix, J.P.W.; Murre, J.M.J.; Postma, E.O.; van den Herik, H.J.

2006-01-01

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During

Neural representations of kinematic laws of motion: evidence for action-perception coupling.

Science.gov (United States)

Dayan, Eran; Casile, Antonino; Levit-Binnun, Nava; Giese, Martin A; Hendler, Talma; Flash, Tamar

2007-12-18

Behavioral and modeling studies have established that curved and drawing human hand movements obey the 2/3 power law, which dictates a strong coupling between movement curvature and velocity. Human motion perception seems to reflect this constraint. The functional MRI study reported here demonstrates that the brain's response to this law of motion is much stronger and more widespread than to other types of motion. Compliance with this law is reflected in the activation of a large network of brain areas subserving motor production, visual motion processing, and action observation functions. Hence, these results strongly support the notion of similar neural coding for motion perception and production. These findings suggest that cortical motion representations are optimally tuned to the kinematic and geometrical invariants characterizing biological actions.

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

Directory of Open Access Journals (Sweden)

Tomoaki Nakamura

2017-12-01

Full Text Available Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM, the emission distributions of which are Gaussian processes (GPs. Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods.

Investigating biomolecular recognition at the cell surface using atomic force microscopy.

Science.gov (United States)

Wang, Congzhou; Yadavalli, Vamsi K

2014-05-01

Probing the interaction forces that drive biomolecular recognition on cell surfaces is essential for understanding diverse biological processes. Force spectroscopy has been a widely used dynamic analytical technique, allowing measurement of such interactions at the molecular and cellular level. The capabilities of working under near physiological environments, combined with excellent force and lateral resolution make atomic force microscopy (AFM)-based force spectroscopy a powerful approach to measure biomolecular interaction forces not only on non-biological substrates, but also on soft, dynamic cell surfaces. Over the last few years, AFM-based force spectroscopy has provided biophysical insight into how biomolecules on cell surfaces interact with each other and induce relevant biological processes. In this review, we focus on describing the technique of force spectroscopy using the AFM, specifically in the context of probing cell surfaces. We summarize recent progress in understanding the recognition and interactions between macromolecules that may be found at cell surfaces from a force spectroscopy perspective. We further discuss the challenges and future prospects of the application of this versatile technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

[Recognition of walking stance phase and swing phase based on moving window].

Science.gov (United States)

Geng, Xiaobo; Yang, Peng; Wang, Xinran; Geng, Yanli; Han, Yu

2014-04-01

Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.

Detecting Lateral Motion using Light’s Orbital Angular Momentum

Science.gov (United States)

Cvijetic, Neda; Milione, Giovanni; Ip, Ezra; Wang, Ting

2015-01-01

Interrogating an object with a light beam and analyzing the scattered light can reveal kinematic information about the object, which is vital for applications ranging from autonomous vehicles to gesture recognition and virtual reality. We show that by analyzing the change in the orbital angular momentum (OAM) of a tilted light beam eclipsed by a moving object, lateral motion of the object can be detected in an arbitrary direction using a single light beam and without object image reconstruction. We observe OAM spectral asymmetry that corresponds to the lateral motion direction along an arbitrary axis perpendicular to the plane containing the light beam and OAM measurement axes. These findings extend OAM-based remote sensing to detection of non-rotational qualities of objects and may also have extensions to other electromagnetic wave regimes, including radio and sound. PMID:26493681

Structure of the mouse galectin-4 N-terminal carbohydrate-recognition domain reveals the mechanism of oligosaccharide recognition

Czech Academy of Sciences Publication Activity Database

Krejčiříková, Veronika; Pachl, Petr; Fábry, Milan; Malý, Petr; Řezáčová, Pavlína; Brynda, Jiří

2011-01-01

Roč. 67, Pt3 (2011), 204-211 ISSN 0907-4449 R&D Projects: GA ČR GA203/09/0820; GA ČR GA304/03/0090; GA ČR GA301/07/0600 Institutional research plan: CEZ:AV0Z50520514; CEZ:AV0Z50520701; CEZ:AV0Z40550506 Keywords : S-type lectins * carbohydrate binding * molecular recognition Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 12.619, year: 2011

Reprint of "Biological motion perception links diverse facets of theory of mind during middle childhood".

Science.gov (United States)

Rice, Katherine; Anderson, Laura C; Velnoskey, Kayla; Thompson, James C; Redcay, Elizabeth

2016-09-01

Two cornerstones of social development-social perception and theory of mind-undergo brain and behavioral changes during middle childhood, but the link between these developing domains is unclear. One theoretical perspective argues that these skills represent domain-specific areas of social development, whereas other perspectives suggest that both skills may reflect a more integrated social system. Given recent evidence from adults that these superficially different domains may be related, the current study examined the developmental relation between these social processes in 52 children aged 7 to 12years. Controlling for age and IQ, social perception (perception of biological motion in noise) was significantly correlated with two measures of theory of mind: one in which children made mental state inferences based on photographs of the eye region of the face and another in which children made mental state inferences based on stories. Social perception, however, was not correlated with children's ability to make physical inferences from stories about people. Furthermore, the mental state inference tasks were not correlated with each other, suggesting a role for social perception in linking various facets of theory of mind. Copyright © 2015 Elsevier Inc. All rights reserved.

Molecular imprinting at walls of silica nanotubes for TNT recognition.

Science.gov (United States)

Xie, Chenggen; Liu, Bianhua; Wang, Zhenyang; Gao, Daming; Guan, Guijian; Zhang, Zhongping

2008-01-15

This paper reports the molecular imprinting at the walls of highly uniform silica nanotubes for the recognition of 2,4,6-trinitrotoluene (TNT). It has been demonstrated that TNT templates were efficiently imprinted into the matrix of silica through the strong acid-base pairing interaction between TNT and 3-aminopropyltriethoxysilane (APTS). TNT-imprinted silica nanotubes were synthesized by the gelation reaction between APTS and tetraethylorthosilicate (TEOS), selectively occurring at the porous walls of APTS-modified alumina membranes. The removal of the original TNT templates leaves the imprinted cavities with covalently anchored amine groups at the cavity walls. A high density of recognition sites with molecular selectivity to the TNT analyte was created at the wall of silica nanotubes. Furthermore, most of these recognition sites are situated at the inside and outside surfaces of tubular walls and in the proximity of the two surfaces due to the ultrathin wall thickness of only 15 nm, providing a better site accessibility and lower mass-transfer resistance. Therefore, greater capacity and faster kinetics of uptaking target species were achieved. The silica nanotube reported herein is an ideal form of material for imprinting various organic or biological molecules toward applications in chemical/biological sensors and bioassay.

Pattern recognition

CERN Document Server

Theodoridis, Sergios

2003-01-01

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

Frequency-Domain Joint Motion and Disparity Estimation Using Steerable Filters

Directory of Open Access Journals (Sweden)

Dimitrios Alexiadis

2018-02-01

Full Text Available In this paper, the problem of joint disparity and motion estimation from stereo image sequences is formulated in the spatiotemporal frequency domain, and a novel steerable filter-based approach is proposed. Our rationale behind coupling the two problems is that according to experimental evidence in the literature, the biological visual mechanisms for depth and motion are not independent of each other. Furthermore, our motivation to study the problem in the frequency domain and search for a filter-based solution is based on the fact that, according to early experimental studies, the biological visual mechanisms can be modelled based on frequency-domain or filter-based considerations, for both the perception of depth and the perception of motion. The proposed framework constitutes the first attempt to solve the joint estimation problem through a filter-based solution, based on frequency-domain considerations. Thus, the presented ideas provide a new direction of work and could be the basis for further developments. From an algorithmic point of view, we additionally extend state-of-the-art ideas from the disparity estimation literature to handle the joint disparity-motion estimation problem and formulate an algorithm that is evaluated through a number of experimental results. Comparisons with state-of-the-art-methods demonstrate the accuracy of the proposed approach.

Interactions between motion and form processing in the human visual system.

Science.gov (United States)

Mather, George; Pavan, Andrea; Bellacosa Marotti, Rosilari; Campana, Gianluca; Casco, Clara

2013-01-01

The predominant view of motion and form processing in the human visual system assumes that these two attributes are handled by separate and independent modules. Motion processing involves filtering by direction-selective sensors, followed by integration to solve the aperture problem. Form processing involves filtering by orientation-selective and size-selective receptive fields, followed by integration to encode object shape. It has long been known that motion signals can influence form processing in the well-known Gestalt principle of common fate; texture elements which share a common motion property are grouped into a single contour or texture region. However, recent research in psychophysics and neuroscience indicates that the influence of form signals on motion processing is more extensive than previously thought. First, the salience and apparent direction of moving lines depends on how the local orientation and direction of motion combine to match the receptive field properties of motion-selective neurons. Second, orientation signals generated by "motion-streaks" influence motion processing; motion sensitivity, apparent direction and adaptation are affected by simultaneously present orientation signals. Third, form signals generated by human body shape influence biological motion processing, as revealed by studies using point-light motion stimuli. Thus, form-motion integration seems to occur at several different levels of cortical processing, from V1 to STS.

Coupled motions direct electrons along human microsomal P450 Chains.

Directory of Open Access Journals (Sweden)

Christopher R Pudney

2011-12-01

Full Text Available Protein domain motion is often implicated in biological electron transfer, but the general significance of motion is not clear. Motion has been implicated in the transfer of electrons from human cytochrome P450 reductase (CPR to all microsomal cytochrome P450s (CYPs. Our hypothesis is that tight coupling of motion with enzyme chemistry can signal "ready and waiting" states for electron transfer from CPR to downstream CYPs and support vectorial electron transfer across complex redox chains. We developed a novel approach to study the time-dependence of dynamical change during catalysis that reports on the changing conformational states of CPR. FRET was linked to stopped-flow studies of electron transfer in CPR that contains donor-acceptor fluorophores on the enzyme surface. Open and closed states of CPR were correlated with key steps in the catalytic cycle which demonstrated how redox chemistry and NADPH binding drive successive opening and closing of the enzyme. Specifically, we provide evidence that reduction of the flavin moieties in CPR induces CPR opening, whereas ligand binding induces CPR closing. A dynamic reaction cycle was created in which CPR optimizes internal electron transfer between flavin cofactors by adopting closed states and signals "ready and waiting" conformations to partner CYP enzymes by adopting more open states. This complex, temporal control of enzyme motion is used to catalyze directional electron transfer from NADPH→FAD→FMN→heme, thereby facilitating all microsomal P450-catalysed reactions. Motions critical to the broader biological functions of CPR are tightly coupled to enzyme chemistry in the human NADPH-CPR-CYP redox chain. That redox chemistry alone is sufficient to drive functionally necessary, large-scale conformational change is remarkable. Rather than relying on stochastic conformational sampling, our study highlights a need for tight coupling of motion to enzyme chemistry to give vectorial electron

Active motions of Brownian particles in a generalized energy-depot model

International Nuclear Information System (INIS)

Zhang Yong; Koo Kim, Chul; Lee, Kong-Ju-Bock

2008-01-01

We present a generalized energy-depot model in which the rate of conversion of the internal energy into motion can be dependent on the position and velocity of a particle. When the conversion rate is a general function of the velocity, the active particle exhibits diverse patterns of motion, including a braking mechanism and a stepping motion. The phase trajectories of the motion are investigated in a systematic way. With a particular form of the conversion rate dependent on the position and velocity, the particle shows a spontaneous oscillation characterizing a negative stiffness. These types of active behaviors are compared with similar phenomena observed in biology, such as the stepping motion of molecular motors and amplification in the hearing mechanism. Hence, our model can provide a generic understanding of the active motion related to the energy conversion and also a new control mechanism for nano-robots. We also investigate the effect of noise, especially on the stepping motion, and observe random walk-like behavior as expected.

Commercial Motion Sensor Based Low-Cost and Convenient Interactive Treadmill

Directory of Open Access Journals (Sweden)

Jonghyun Kim

2015-09-01

Full Text Available Interactive treadmills were developed to improve the simulation of overground walking when compared to conventional treadmills. However, currently available interactive treadmills are expensive and inconvenient, which limits their use. We propose a low-cost and convenient version of the interactive treadmill that does not require expensive equipment and a complicated setup. As a substitute for high-cost sensors, such as motion capture systems, a low-cost motion sensor was used to recognize the subject’s intention for speed changing. Moreover, the sensor enables the subject to make a convenient and safe stop using gesture recognition. For further cost reduction, the novel interactive treadmill was based on an inexpensive treadmill platform and a novel high-level speed control scheme was applied to maximize performance for simulating overground walking. Pilot tests with ten healthy subjects were conducted and results demonstrated that the proposed treadmill achieves similar performance to a typical, costly, interactive treadmill that contains a motion capture system and an instrumented treadmill, while providing a convenient and safe method for stopping.

Human Pose Estimation and Activity Recognition from Multi-View Videos

DEFF Research Database (Denmark)

Holte, Michael Boelstoft; Tran, Cuong; Trivedi, Mohan

2012-01-01

approaches which have been proposed to comply with these requirements. We report a comparison of the most promising methods for multi-view human action recognition using two publicly available datasets: the INRIA Xmas Motion Acquisition Sequences (IXMAS) Multi-View Human Action Dataset, and the i3DPost Multi......–computer interaction (HCI), assisted living, gesture-based interactive games, intelligent driver assistance systems, movies, 3D TV and animation, physical therapy, autonomous mental development, smart environments, sport motion analysis, video surveillance, and video annotation. Next, we review and categorize recent......-View Human Action and Interaction Dataset. To compare the proposed methods, we give a qualitative assessment of methods which cannot be compared quantitatively, and analyze some prominent 3D pose estimation techniques for application, where not only the performed action needs to be identified but a more...

Analysis of nematode motion using an improved light-scatter based system.

Directory of Open Access Journals (Sweden)

Chuck S Nutting

2015-02-01

Full Text Available The detailed assessment of nematode activity and viability still remains a relatively undeveloped area of biological and medical research. Computer-based approaches to assessing the motility of larger nematode stages have been developed, yet these lack the capability to detect and analyze the more subtle and important characteristics of the motion of nematodes. There is currently a need to improved methods of assessing the viability and health of parasitic worms.We describe here a system that converts the motion of nematodes through a light-scattering system into an electrical waveform, and allows for reproducible, and wholly non-subjective, assessment of alterations in motion, as well as estimation of the number of nematode worms of different forms and sizes. Here we have used Brugia sp. microfilariae (L1, infective larvae (L3 and adults, together with the free-living nematode Caenorhabditis elegans.The motion of worms in a small (200 ul volume can be detected, with the presence of immotile worms not interfering with the readings at practical levels (up to at least 500 L1 /200 ul. Alterations in the frequency of parasite movement following the application of the anti-parasitic drugs, (chloroquine and imatinib; the anti-filarial effect of the latter agent is the first demonstrated here for the first time. This system can also be used to estimate the number of parasites, and shortens the time required to estimate parasites numbers, and eliminates the need for microscopes and trained technicians to provide an estimate of microfilarial sample sizes up to 1000 parasites/ml. Alterations in the form of motion of the worms can also be depicted.This new instrument, named a "WiggleTron", offers exciting opportunities to further study nematode biology and to aid drug discovery, as well as contributing to a rapid estimate of parasite numbers in various biological samples.

Auditory motion capturing ambiguous visual motion

Directory of Open Access Journals (Sweden)

Arjen eAlink

2012-01-01

Full Text Available In this study, it is demonstrated that moving sounds have an effect on the direction in which one sees visual stimuli move. During the main experiment sounds were presented consecutively at four speaker locations inducing left- or rightwards auditory apparent motion. On the path of auditory apparent motion, visual apparent motion stimuli were presented with a high degree of directional ambiguity. The main outcome of this experiment is that our participants perceived visual apparent motion stimuli that were ambiguous (equally likely to be perceived as moving left- or rightwards more often as moving in the same direction than in the opposite direction of auditory apparent motion. During the control experiment we replicated this finding and found no effect of sound motion direction on eye movements. This indicates that auditory motion can capture our visual motion percept when visual motion direction is insufficiently determinate without affecting eye movements.

Attentional Selection for Object Recognition - A Gentle Way

National Research Council Canada - National Science Library

Walther, Dirk; Itti, Laurent; Riesenhuber, Maximilian; Poggio, Tomaso; Koch, Christof

2002-01-01

...% at a high level is sufficient to recognize multiple objects. To determine the size and shape of the region to be modulated, a rough segmentation is performed, based on pre-attentive features already computed to guide attention. Testing with synthetic and natural stimuli demonstrates that our new approach to attentional selection for recognition yields encouraging results in addition to being biologically plausible.

Motion dazzle and camouflage as distinct anti-predator defenses

Directory of Open Access Journals (Sweden)

Stevens Martin

2011-11-01

Full Text Available Abstract Background Camouflage patterns that hinder detection and/or recognition by antagonists are widely studied in both human and animal contexts. Patterns of contrasting stripes that purportedly degrade an observer's ability to judge the speed and direction of moving prey ('motion dazzle' are, however, rarely investigated. This is despite motion dazzle having been fundamental to the appearance of warships in both world wars and often postulated as the selective agent leading to repeated patterns on many animals (such as zebra and many fish, snake, and invertebrate species. Such patterns often appear conspicuous, suggesting that protection while moving by motion dazzle might impair camouflage when stationary. However, the relationship between motion dazzle and camouflage is unclear because disruptive camouflage relies on high-contrast markings. In this study, we used a computer game with human subjects detecting and capturing either moving or stationary targets with different patterns, in order to provide the first empirical exploration of the interaction of these two protective coloration mechanisms. Results Moving targets with stripes were caught significantly less often and missed more often than targets with camouflage patterns. However, when stationary, targets with camouflage markings were captured less often and caused more false detections than those with striped patterns, which were readily detected. Conclusions Our study provides the clearest evidence to date that some patterns inhibit the capture of moving targets, but that camouflage and motion dazzle are not complementary strategies. Therefore, the specific coloration that evolves in animals will depend on how the life history and ontogeny of each species influence the trade-off between the costs and benefits of motion dazzle and camouflage.

Motion dazzle and camouflage as distinct anti-predator defenses.

Science.gov (United States)

Stevens, Martin; Searle, W Tom L; Seymour, Jenny E; Marshall, Kate L A; Ruxton, Graeme D

2011-11-25

Camouflage patterns that hinder detection and/or recognition by antagonists are widely studied in both human and animal contexts. Patterns of contrasting stripes that purportedly degrade an observer's ability to judge the speed and direction of moving prey ('motion dazzle') are, however, rarely investigated. This is despite motion dazzle having been fundamental to the appearance of warships in both world wars and often postulated as the selective agent leading to repeated patterns on many animals (such as zebra and many fish, snake, and invertebrate species). Such patterns often appear conspicuous, suggesting that protection while moving by motion dazzle might impair camouflage when stationary. However, the relationship between motion dazzle and camouflage is unclear because disruptive camouflage relies on high-contrast markings. In this study, we used a computer game with human subjects detecting and capturing either moving or stationary targets with different patterns, in order to provide the first empirical exploration of the interaction of these two protective coloration mechanisms. Moving targets with stripes were caught significantly less often and missed more often than targets with camouflage patterns. However, when stationary, targets with camouflage markings were captured less often and caused more false detections than those with striped patterns, which were readily detected. Our study provides the clearest evidence to date that some patterns inhibit the capture of moving targets, but that camouflage and motion dazzle are not complementary strategies. Therefore, the specific coloration that evolves in animals will depend on how the life history and ontogeny of each species influence the trade-off between the costs and benefits of motion dazzle and camouflage.

8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

Science.gov (United States)

2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 292.2...; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization established in the United...

Hand Motion-Based Remote Control Interface with Vibrotactile Feedback for Home Robots

Directory of Open Access Journals (Sweden)

Juan Wu

2013-06-01

Full Text Available This paper presents the design and implementation of a hand-held interface system for the locomotion control of home robots. A handheld controller is proposed to implement hand motion recognition and hand motion-based robot control. The handheld controller can provide a ‘connect-and-play’ service for the users to control the home robot with visual and vibrotactile feedback. Six natural hand gestures are defined for navigating the home robots. A three-axis accelerometer is used to detect the hand motions of the user. The recorded acceleration data are analysed and classified to corresponding control commands according to their characteristic curves. A vibration motor is used to provide vibrotactile feedback to the user when an improper operation is performed. The performances of the proposed hand motion-based interface and the traditional keyboard and mouse interface have been compared in robot navigation experiments. The experimental results of home robot navigation show that the success rate of the handheld controller is 13.33% higher than the PC based controller. The precision of the handheld controller is 15.4% more than that of the PC and the execution time is 24.7% less than the PC based controller. This means that the proposed hand motion-based interface is more efficient and flexible.

Deep Learning For Sequential Pattern Recognition

OpenAIRE

Safari, Pooyan

2013-01-01

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM) In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with ...

Interactions between motion and form processing in the human visual system

Directory of Open Access Journals (Sweden)

George eMather

2013-05-01

Full Text Available The predominant view of motion and form processing in the human visual system assumes that these two attributes are handled by separate and independent modules. Motion processing involves filtering by direction-selective sensors, followed by integration to solve the aperture problem. Form processing involves filtering by orientation-selective and size-selective receptive fields, followed by integration to encode object shape. It has long been known that motion signals can influence form processing in the well-known Gestalt principle of common fate; texture elements which share a common motion property are grouped into a single contour or texture region. However recent research in psychophysics and neuroscience indicates that the influence of form signals on motion processing is more extensive than previously thought. First, the salience and apparent direction of moving lines depends on how the local orientation and direction of motion combine to match the receptive field properties of motion-selective neurons. Second, orientation signals generated by ‘motion-streaks’ influence motion processing; motion sensitivity, apparent direction and adaptation are affected by simultaneously present orientation signals. Third, form signals generated by human body shape influence biological motion processing, as revealed by studies using point-light motion stimuli. Thus form-motion integration seems to occur at several different levels of cortical processing, from V1 to STS.

Pattern recognition techniques and neo-deterministic seismic hazard: Time dependent scenarios for North-Eastern Italy

International Nuclear Information System (INIS)

Peresan, A.; Vaccari, F.; Panza, G.F.; Zuccolo, E.; Gorshkov, A.

2009-05-01

An integrated neo-deterministic approach to seismic hazard assessment has been developed that combines different pattern recognition techniques, designed for the space-time identification of strong earthquakes, with algorithms for the realistic modeling of seismic ground motion. The integrated approach allows for a time dependent definition of the seismic input, through the routine updating of earthquake predictions. The scenarios of expected ground motion, associated with the alarmed areas, are defined by means of full waveform modeling. A set of neo-deterministic scenarios of ground motion is defined at regional and local scale, thus providing a prioritization tool for timely prevention and mitigation actions. Constraints about the space and time of occurrence of the impending strong earthquakes are provided by three formally defined and globally tested algorithms, which have been developed according to a pattern recognition scheme. Two algorithms, namely CN and M8, are routinely used for intermediate-term middle-range earthquake predictions, while a third algorithm allows for the identification of the areas prone to large events. These independent procedures have been combined to better constrain the alarmed area. The pattern recognition of earthquake-prone areas does not belong to the family of earthquake prediction algorithms since it does not provide any information about the time of occurrence of the expected earthquakes. Nevertheless, it can be considered as the term-less zero-approximation, which restrains the alerted areas (e.g. defined by CN or M8) to the more precise location of large events. Italy is the only region of moderate seismic activity where the two different prediction algorithms CN and M8S (i.e. a spatially stabilized variant of M8) are applied simultaneously and a real-time test of predictions, for earthquakes with magnitude larger than 5.4, is ongoing since 2003. The application of the CN to the Adriatic region (s.l.), which is relevant

A Study on the Bio-mimetic Motion of Reptiles

Energy Technology Data Exchange (ETDEWEB)

Shin, Hochelo; Kim, Changhoi; Eom, Heungseop; Jeong, Kyungmin; Jung, Seungjo

2013-10-15

After investigating the locomotion based on the biological characteristics about the from a literature search about the reptile, the locomotion of lizards is captured with marker based motion capture system. Tested lizards are Cuban anole, bearded dragon, domestic lizards such as a white-striped grass lizard and a leopard lizard, After analyzing the motion of the lizards with the measured data, a 25 DOF kinematics model of a lizard was proposed. A periodic gait of the lizard was modeled by defining gait parameters. The body structure of the lizard was analyzed with a bone specimen for the kinematics modeling. Dynamics parameters such as a mass and a inertia of a link are obtained by measuring the weight and the volume of each link. The crawl and the trot gait were simulated with the dynamics model. To control the poly-morphic motion of snake robot, various locomotions of snakes and the motion algorithm of snake robots were investigated. A test model of snake robot and a control system were developed to analyzed the motion and energy efficiency according to the gaits and to realize the poly-morphic motion control.

A Study on the Bio-mimetic Motion of Reptiles

International Nuclear Information System (INIS)

Shin, Hochelo; Kim, Changhoi; Eom, Heungseop; Jeong, Kyungmin; Jung, Seungjo

2013-10-01

After investigating the locomotion based on the biological characteristics about the from a literature search about the reptile, the locomotion of lizards is captured with marker based motion capture system. Tested lizards are Cuban anole, bearded dragon, domestic lizards such as a white-striped grass lizard and a leopard lizard, After analyzing the motion of the lizards with the measured data, a 25 DOF kinematics model of a lizard was proposed. A periodic gait of the lizard was modeled by defining gait parameters. The body structure of the lizard was analyzed with a bone specimen for the kinematics modeling. Dynamics parameters such as a mass and a inertia of a link are obtained by measuring the weight and the volume of each link. The crawl and the trot gait were simulated with the dynamics model. To control the poly-morphic motion of snake robot, various locomotions of snakes and the motion algorithm of snake robots were investigated. A test model of snake robot and a control system were developed to analyzed the motion and energy efficiency according to the gaits and to realize the poly-morphic motion control

A Robust and Device-Free System for the Recognition and Classification of Elderly Activities.

Science.gov (United States)

Li, Fangmin; Al-Qaness, Mohammed Abdulaziz Aide; Zhang, Yong; Zhao, Bihai; Luan, Xidao

2016-12-01

Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise.

Eye Tracking Reveals a Crucial Role for Facial Motion in Recognition of Faces by Infants

Science.gov (United States)

Xiao, Naiqi G.; Quinn, Paul C.; Liu, Shaoying; Ge, Liezhong; Pascalis, Olivier; Lee, Kang

2015-01-01

Current knowledge about face processing in infancy comes largely from studies using static face stimuli, but faces that infants see in the real world are mostly moving ones. To bridge this gap, 3-, 6-, and 9-month-old Asian infants (N = 118) were familiarized with either moving or static Asian female faces, and then their face recognition was…

Identification of motion from multi-channel EMG signals for control of prosthetic hand

International Nuclear Information System (INIS)

Geethanjali, P.; Ray, K.K.

2011-01-01

Full text: The authors in this paper propose an effective and efficient pattern recognition technique from four channel electromyogram (EMG) signals for control of multifunction prosthetic hand. Time domain features such as mean absolute value, number of zero crossings, number of slope sign changes and waveform length are considered for pattern recognition. The patterns are classified using simple logistic regression (SLR) technique and decision tree (DT) using J48 algorithm. In this study six specific hand and wrist motions are identified from the EMG signals obtained from ten different able-bodied. By considering relevant dominant features for pattern recognition, the processing time as well as memory space of the SLR and DT classifiers is found to be less in comparison with neural network (NN), k-nearest neighbour model 1 (kNN Model-1), k-nearest neighbour model 2 (kNN-Model-2) and linear discriminant analysis. The classification accuracy of SLR classifier is found to be 91 ± 1.9%. (author)

Hand gesture recognition in confined spaces with partial observability and occultation constraints

Science.gov (United States)

Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2016-05-01

Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

Pattern recognition as a method of data analysis

Energy Technology Data Exchange (ETDEWEB)

Caputo, M.

1978-11-15

The method of pattern recognition has been used in biological and social sciences and has been recently introduced for the solution of geological and geophysical problems such as oil and ore prospecting and seismological prediction. The method is briefly illustrated by an application to earthquake prediction in Italy in which topographic and geologic maps are used in conjunction with earthquake catalogs. 3 figures, 1 table.

Interacting with mobile devices by fusion eye and hand gestures recognition systems based on decision tree approach

Science.gov (United States)

Elleuch, Hanene; Wali, Ali; Samet, Anis; Alimi, Adel M.

2017-03-01

Two systems of eyes and hand gestures recognition are used to control mobile devices. Based on a real-time video streaming captured from the device's camera, the first system recognizes the motion of user's eyes and the second one detects the static hand gestures. To avoid any confusion between natural and intentional movements we developed a system to fuse the decision coming from eyes and hands gesture recognition systems. The phase of fusion was based on decision tree approach. We conducted a study on 5 volunteers and the results that our system is robust and competitive.

New physical concepts for cell amoeboid motion.

Science.gov (United States)

Evans, E

1993-04-01

Amoeboid motion of cells is an essential mechanism in the function of many biological organisms (e.g., the regiment of scavenger cells in the immune defense system of animals). This process involves rapid chemical polymerization (with numerous protein constituents) to create a musclelike contractile network that advances the cell over the surface. Significant progress has been made in the biology and biochemistry of motile cells, but the physical dynamics of cell spreading and contraction are not well understood. The reason is that general approaches are formulated from complex mass, momentum, and chemical reaction equations for multiphase-multicomponent flow with the nontrivial difficulty of moving boundaries. However, there are strong clues to the dynamics that allow bold steps to be taken in simplifying the physics of motion. First, amoeboid cells often exhibit exceptional kinematics, i.e., steady advance and retraction of local fixed-shape patterns. Second, recent evidence has shown that cell projections "grow" by polymerization along the advancing boundary of the cell. Together, these characteristics represent a local growth process pinned to the interfacial contour of a contractile network. As such, the moving boundary becomes tractable, but subtle features of the motion lead to specific requirements for the chemical nature of the boundary polymerization process. To demonstrate these features, simple examples for limiting conditions of substrate interaction (i.e., "strong" and "weak" adhesion) are compared with data from experimental studies of yeast particle engulfment by blood granulocytes and actin network dynamics in fishscale keratocytes.

Neutron structural biology

International Nuclear Information System (INIS)

Niimura, Nobuo

1999-01-01

Neutron structural biology will be one of the most important fields in the life sciences which will interest human beings in the 21st century because neutrons can provide not only the position of hydrogen atoms in biological macromolecules but also the dynamic molecular motion of hydrogen atoms and water molecules. However, there are only a few examples experimentally determined at present because of the lack of neutron source intensity. Next generation neutron source scheduled in JAERI (Performance of which is 100 times better than that of JRR-3M) opens the life science of the 21st century. (author)

Temporal lobe structures and facial emotion recognition in schizophrenia patients and nonpsychotic relatives.

Science.gov (United States)

Goghari, Vina M; Macdonald, Angus W; Sponheim, Scott R

2011-11-01

Temporal lobe abnormalities and emotion recognition deficits are prominent features of schizophrenia and appear related to the diathesis of the disorder. This study investigated whether temporal lobe structural abnormalities were associated with facial emotion recognition deficits in schizophrenia and related to genetic liability for the disorder. Twenty-seven schizophrenia patients, 23 biological family members, and 36 controls participated. Several temporal lobe regions (fusiform, superior temporal, middle temporal, amygdala, and hippocampus) previously associated with face recognition in normative samples and found to be abnormal in schizophrenia were evaluated using volumetric analyses. Participants completed a facial emotion recognition task and an age recognition control task under time-limited and self-paced conditions. Temporal lobe volumes were tested for associations with task performance. Group status explained 23% of the variance in temporal lobe volume. Left fusiform gray matter volume was decreased by 11% in patients and 7% in relatives compared with controls. Schizophrenia patients additionally exhibited smaller hippocampal and middle temporal volumes. Patients were unable to improve facial emotion recognition performance with unlimited time to make a judgment but were able to improve age recognition performance. Patients additionally showed a relationship between reduced temporal lobe gray matter and poor facial emotion recognition. For the middle temporal lobe region, the relationship between greater volume and better task performance was specific to facial emotion recognition and not age recognition. Because schizophrenia patients exhibited a specific deficit in emotion recognition not attributable to a generalized impairment in face perception, impaired emotion recognition may serve as a target for interventions.

A Biologically Derived Pectoral Fin for Yaw Turn Manoeuvres

Directory of Open Access Journals (Sweden)

Jonah R. Gottlieb

2010-01-01

Full Text Available A bio-robotic fin has been developed that models the pectoral fin of the bluegill sunfish as the fish turned to avoid an obstacle. This work involved biological studies of the sunfish fin, the development of kinematic models of the motions of the fin's rays, CFD based predictions of the 3D forces and flows created by the fin, and the implementation of simplified models of the fin's kinematics and mechanical properties in a physical model. The resulting robotic fin produced the forces and flows that drove the manoeuvre and had a sufficiently high number of degrees of freedom to create a variety of non-biologically derived motions. The results indicate that for robotic fins to produce a level of performance on par with biological fins, both the kinematics and the mechanical properties of the biological fin must be modelled well.

Biological Motion induced mu suppression is reduced in Early Psychosis (EP) patients with active negative symptoms and Autism Spectrum Disorders (ASD).

Science.gov (United States)

Minichino, Amedeo; Singh, Fiza; Pineda, Jaime; Friederich, Elisabeth; Cadenhead, Kristin S

2016-04-30

There is evidence of genetic and neural system overlap in Autism Spectrum Disorder (ASD) and Early Psychosis (EP). Five datasets were pooled to compare mu suppression index (MSI), a proxy of mirror neuron activity, in EP, high functioning ASD, and healthy subjects (HS). ASDs and EPs with "active" negative symptoms showed significant differences in mu suppression, in response to Biological Motion/point-light display animation, compared to HS. Preliminary findings suggest that similar neural network deficits in ASD and EP could be driven by the expression of negative symptoms in the latter group of patients. These findings may aid future studies on EP and ASD and facilitate the formulation of new hypotheses regarding their pathophysiology. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Functions of galectins as 'self/non-self'-recognition and effector factors.

Science.gov (United States)

Vasta, Gerardo R; Feng, Chiguang; González-Montalbán, Nuria; Mancini, Justin; Yang, Lishi; Abernathy, Kelsey; Frost, Graeme; Palm, Cheyenne

2017-07-31

Carbohydrate structures on the cell surface encode complex information that through specific recognition by carbohydrate-binding proteins (lectins) modulates interactions between cells, cells and the extracellular matrix, or mediates recognition of potential microbial pathogens. Galectins are a family of ß-galactoside-binding lectins, which are evolutionary conserved and have been identified in most organisms, from fungi to invertebrates and vertebrates, including mammals. Since their discovery in the 1970s, their biological roles, initially understood as limited to recognition of endogenous carbohydrate ligands in embryogenesis and development, have expanded in recent years by the discovery of their roles in tissue repair and regulation of immune homeostasis. More recently, evidence has accumulated to support the notion that galectins can also bind glycans on the surface of potentially pathogenic microbes, and function as recognition and effector factors in innate immunity, thus establishing a new paradigm. Furthermore, some parasites 'subvert' the recognition roles of the vector/host galectins for successful attachment or invasion. These recent findings have revealed a striking functional diversification in this structurally conserved lectin family. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Manipulation and controlled amplification of Brownian motion of microcantilever sensors

International Nuclear Information System (INIS)

Mehta, Adosh; Cherian, Suman; Hedden, David; Thundat, Thomas

2001-01-01

Microcantilevers, such as those used in atomic force microscopy, undergo Brownian motion due to mechanical thermal noise. The root mean square amplitude of the Brownian motion of a cantilever typically ranges from 0.01--0.1 nm, which limits its use in practical applications. Here we describe a technique by which the Brownian amplitude and the Q factor in air and water can be amplified by three and two orders of magnitude, respectively. This technique is similar to a positive feedback oscillator, wherein the Brownian motion of the vibrating cantilever controls the frequency output of the oscillator. This technique can be exploited to improve sensitivity of microcantilever-based chemical and biological sensors, especially for sensors in liquid environments

Constructive role of Brownian motion: Brownian motors and Stochastic Resonance

Science.gov (United States)

Hänggi, Peter

2005-03-01

Noise is usually thought of as the enemy of order rather as a constructive influence. For the phenomena of Stochastic Resonance [1] and Brownian motors [2], however, stochastic noise can play a beneficial role in enhancing detection and/or facilitating directed transmission of information in absence of biasing forces. Brownian motion assisted Stochastic Resonance finds useful applications in physical, technological, biological and biomedical contexts [1,3]. The basic principles that underpin Stochastic Resonance are elucidated and novel applications for nonlinear classical and quantum systems will be addressed. The presence of non-equilibrium disturbances enables to rectify Brownian motion so that quantum and classical objects can be directed around on a priori designed routes in biological and physical systems (Brownian motors). In doing so, the energy from the haphazard motion of (quantum) Brownian particles is extracted to perform useful work against an external load. This very concept together with first experimental realizations are discussed [2,4,5]. [1] L. Gammaitoni, P. Hä'nggi, P. Jung and F. Marchesoni, Stochastic Resonance, Rev. Mod. Phys. 70, 223 (1998).[2] R. D. Astumian and P. Hä'nggi, Brownian motors, Physics Today 55 (11), 33 (2002).[3] P. Hä'nggi, Stochastic Resonace in Physics and Biology, ChemPhysChem 3, 285 (2002).[4] H. Linke, editor, Special Issue on Brownian Motors, Applied Physics A 75, No. 2 (2002).[5] P. Hä'nggi, F. Marchesoni, F. Nori, Brownian motors, Ann. Physik (Leipzig) 14, xxx (2004); cond-mat/0410033.

Auditory Motion Elicits a Visual Motion Aftereffect.

Science.gov (United States)

Berger, Christopher C; Ehrsson, H Henrik

2016-01-01

The visual motion aftereffect is a visual illusion in which exposure to continuous motion in one direction leads to a subsequent illusion of visual motion in the opposite direction. Previous findings have been mixed with regard to whether this visual illusion can be induced cross-modally by auditory stimuli. Based on research on multisensory perception demonstrating the profound influence auditory perception can have on the interpretation and perceived motion of visual stimuli, we hypothesized that exposure to auditory stimuli with strong directional motion cues should induce a visual motion aftereffect. Here, we demonstrate that horizontally moving auditory stimuli induced a significant visual motion aftereffect-an effect that was driven primarily by a change in visual motion perception following exposure to leftward moving auditory stimuli. This finding is consistent with the notion that visual and auditory motion perception rely on at least partially overlapping neural substrates.

Auditory Motion Elicits a Visual Motion Aftereffect

Directory of Open Access Journals (Sweden)

Christopher C. Berger

2016-12-01

Full Text Available The visual motion aftereffect is a visual illusion in which exposure to continuous motion in one direction leads to a subsequent illusion of visual motion in the opposite direction. Previous findings have been mixed with regard to whether this visual illusion can be induced cross-modally by auditory stimuli. Based on research on multisensory perception demonstrating the profound influence auditory perception can have on the interpretation and perceived motion of visual stimuli, we hypothesized that exposure to auditory stimuli with strong directional motion cues should induce a visual motion aftereffect. Here, we demonstrate that horizontally moving auditory stimuli induced a significant visual motion aftereffect—an effect that was driven primarily by a change in visual motion perception following exposure to leftward moving auditory stimuli. This finding is consistent with the notion that visual and auditory motion perception rely on at least partially overlapping neural substrates.

Protein structure analysis using the resonant recognition model and wavelet transforms

International Nuclear Information System (INIS)

Fang, Q.; Cosic, I.

1998-01-01

An approach based on the resonant recognition model and the discrete wavelet transform is introduced here for characterising proteins' biological function. The protein sequence is converted into a numerical series by assigning the electron-ion interaction potential to each amino acid from N-terminal to C-terminal. A set of peaks is found after performing a wavelet transform onto a numerical series representing a group of homologous proteins. These peaks are related to protein structural and functional properties and named characteristic vector of that protein group. Further more, the amino acids contributing mostly to a protein's biological functions, the so-called 'hot spots' amino acids, are predicted by the continuous wavelet transform. It is found that the hot spots are clustered around the protein's cleft structure. The wavelets approach provides a novel methods for amino acid sequence analysis as well as an expansion for the newly established macromolecular interaction model: the resonant recognition model. Copyright (1998) Australasian Physical and Engineering Sciences in Medicine

Emotion recognition through static faces and moving bodies: a comparison between typically-developed adults and individuals with high level of autistic traits

OpenAIRE

Rossana eActis-Grosso; Rossana eActis-Grosso; Francesco eBossi; Paola eRicciardelli; Paola eRicciardelli

2015-01-01

We investigated whether the type of stimulus (pictures of static faces vs. body motion) contributes differently to the recognition of emotions. The performance (accuracy and response times) of 25 Low Autistic Traits (LAT group) young adults (21 males) and 20 young adults (16 males) with either High Autistic Traits (HAT group) or with High Functioning Autism Spectrum Disorder was compared in the recognition of four emotions (Happiness, Anger, Fear and Sadness) either shown in static faces or c...

Emotion recognition through static faces and moving bodies: a comparison between typically developed adults and individuals with high level of autistic traits

OpenAIRE

Actis-Grosso, Rossana; Bossi, Francesco; Ricciardelli, Paola

2015-01-01

We investigated whether the type of stimulus (pictures of static faces vs. body motion) contributes differently to the recognition of emotions. The performance (accuracy and response times) of 25 Low Autistic Traits (LAT group) young adults (21 males) and 20 young adults (16 males) with either High Autistic Traits or with High Functioning Autism Spectrum Disorder (HAT group) was compared in the recognition of four emotions (Happiness, Anger, Fear, and Sadness) either shown in static faces or ...

The Physics of Marine Biology.

Science.gov (United States)

Conn, Kathleen

1992-01-01

Discusses ways in which marine biology can be integrated into the physics classroom. Topics suggested for incorporation include the harmonic motion of ocean waves, ocean currents, the interaction of visible light with ocean water, pressure, light absorption, and sound transfer in water. (MDH)

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

Science.gov (United States)

Huntsberger, Terry

2011-01-01

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

Reading Emotion From Mouse Cursor Motions: Affective Computing Approach.

Science.gov (United States)

Yamauchi, Takashi; Xiao, Kunchen

2018-04-01

Affective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical. This study integrates mouse cursor motion analysis into affective computing and investigates the idea that movements of the computer cursor can provide information about emotion of the computer user. We extracted 16-26 trajectory features during a choice-reaching task and examined the link between emotion and cursor motions. Participants were induced for positive or negative emotions by music, film clips, or emotional pictures, and they indicated their emotions with questionnaires. Our 10-fold cross-validation analysis shows that statistical models formed from "known" participants (training data) could predict nearly 10%-20% of the variance of positive affect and attentiveness ratings of "unknown" participants, suggesting that cursor movement patterns such as the area under curve and direction change help infer emotions of computer users. Copyright © 2017 Cognitive Science Society, Inc.

Early mechanisms in radiation-induced biological damage

International Nuclear Information System (INIS)

Powers, E.L.

1983-01-01

An introduction to the mechanisms of radiation action in biological systems is presented. Several questions about the nature of the radiation damage process are discussed, including recognition of the oxygen effects, dose-response relationships, and the importance of the hydroxyl radical

The role of parietal cortex in the formation of colour and motion based concepts

Directory of Open Access Journals (Sweden)

Samuel William Cheadle

2014-07-01

Full Text Available Imaging evidence shows that separate subdivisions of parietal cortex, in and around the intraparietal sulcus (IPS, are engaged when stimuli are grouped according to colour and to motion (Zeki and Stutters 2013. Since grouping is an essential step in the formation of concepts, we wanted to learn whether parietal cortex is also engaged in the formation of concepts according to these two attributes. Using functional magnetic resonance imaging (fMRI, and choosing the recognition of concept-based colour or motion stimuli as our paradigm, we found that there was strong concept-related activity in and around the intraparietal sulcus (IPS, a region whose homologue in the macaque monkey is known to receive direct but segregated anatomical inputs from V4 and V5. Parietal activity related to colour concepts was juxtaposed but did not overlap with activity related to motion concepts, thus emphasizing the continuation of the segregation of colour and motion into the conceptual system. Concurrent retinotopic mapping experiments showed that within the parietal cortex, concept-related activity increases within later stage IPS areas.

Leap Motion Device Used to Control a Real Anthropomorphic Gripper

Directory of Open Access Journals (Sweden)

Ionel Staretu

2016-06-01

Full Text Available This paper presents for the first time the use of the Leap Motion device to control an anthropomorphic gripper with five fingers. First, a description of the Leap Motion device is presented, highlighting its main functional characteristics, followed by testing of its use for capturing the movements of a human hand's fingers in different configurations. Next, the HandCommander soft module and the Interface Controller application are described. The HandCommander is a software module created to facilitate interaction between a human hand and the GraspIT virtual environment, and the Interface Controller application is required to send motion data to the virtual environment and to test the communication protocol. For the test, a prototype of an anthropomorphic gripper with five fingers was made, including a proper hardware system of command and control, which is briefly presented in this paper. Following the creation of the prototype, the command system performance test was conducted under real conditions, evaluating the recognition efficiency of the objects to be gripped and the efficiency of the command and control strategies for the gripping process. The gripping test is exemplified by the gripping of an object, such as a screw spanner. It was found that the command system, both in terms of capturing human hand gestures with the Leap Motion device and effective object gripping, is operational. Suggestive figures are presented as examples.

Making Activity Recognition Robust against Deceptive Behavior.

Directory of Open Access Journals (Sweden)

Sohrab Saeb

Full Text Available Healthcare services increasingly use the activity recognition technology to track the daily activities of individuals. In some cases, this is used to provide incentives. For example, some health insurance companies offer discount to customers who are physically active, based on the data collected from their activity tracking devices. Therefore, there is an increasing motivation for individuals to cheat, by making activity trackers detect activities that increase their benefits rather than the ones they actually do. In this study, we used a novel method to make activity recognition robust against deceptive behavior. We asked 14 subjects to attempt to trick our smartphone-based activity classifier by making it detect an activity other than the one they actually performed, for example by shaking the phone while seated to make the classifier detect walking. If they succeeded, we used their motion data to retrain the classifier, and asked them to try to trick it again. The experiment ended when subjects could no longer cheat. We found that some subjects were not able to trick the classifier at all, while others required five rounds of retraining. While classifiers trained on normal activity data predicted true activity with ~38% accuracy, training on the data gathered during the deceptive behavior increased their accuracy to ~84%. We conclude that learning the deceptive behavior of one individual helps to detect the deceptive behavior of others. Thus, we can make current activity recognition robust to deception by including deceptive activity data from a few individuals.

Biologically inspired control of humanoid robot arms robust and adaptive approaches

CERN Document Server

Spiers, Adam; Herrmann, Guido

2016-01-01

This book investigates a biologically inspired method of robot arm control, developed with the objective of synthesising human-like motion dynamically, using nonlinear, robust and adaptive control techniques in practical robot systems. The control method caters to a rising interest in humanoid robots and the need for appropriate control schemes to match these systems. Unlike the classic kinematic schemes used in industrial manipulators, the dynamic approaches proposed here promote human-like motion with better exploitation of the robot’s physical structure. This also benefits human-robot interaction. The control schemes proposed in this book are inspired by a wealth of human-motion literature that indicates the drivers of motion to be dynamic, model-based and optimal. Such considerations lend themselves nicely to achievement via nonlinear control techniques without the necessity for extensive and complex biological models. The operational-space method of robot control forms the basis of many of the techniqu...

Assisting doctors on assessing movements in infants using motion tracking

DEFF Research Database (Denmark)

Olsen, Mikkel; Herskind, Anna; Nielsen, Jens Bo

2015-01-01

In this work, we consider the possibilities of having an automatic computer-based system for tracking the movements of infants. An existing motion tracking system is used to process recorded video sequences containing both color and spatial information of the infant's body pose and movements....... The system uses these sequences of data to estimate the underlying skeleton of the infant and parametrize the movements. Post-processing of these parameters can yield objective measurements of an infant's movement patterns. This could e.g. be quantification of (a)symmetry and recognition of certain gestures/actions...

Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

Directory of Open Access Journals (Sweden)

Shirui Huo

2017-01-01

Full Text Available Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs and extracting deep convolutional neural network (DCNN features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

Handwritten-word spotting using biologically inspired features

NARCIS (Netherlands)

van der Zant, Tijn; Schomaker, Lambert; Haak, Koen

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language, and collection. We propose a biologically inspired

Optical Pattern Recognition

Science.gov (United States)

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

Three-dimensional motion tracking correlates with skill level in upper gastrointestinal endoscopy

DEFF Research Database (Denmark)

Arnold, Sif H.; Svendsen, Morten Bo Søndergaard; Konge, Lars

2015-01-01

untrained medical students) were tested using a virtual reality simulator. A motion sensor was used to collect data regarding the distance between the hands, and height and movement of the scope hand. Test characteristics between groups were explored using Kruskal-Wallis H and Man-Whitney U exact tests......Background and study aim: Feedback is an essential part of training in upper gastrointestinal endoscopy. Virtual reality simulators provide limited feedback, focusing only on visual recognition with no feedback on the procedural part of training. Motion tracking identifies patterns of movement......, and this study aimed to explore the correlation between skill level and operator movement using an objective automated tool. Methods: In this medical education study, 37 operators (12 senior doctors who performed endoscopic retrograde cholangiopancreatography, 13 doctors with varying levels of experience, and 12...

A new selective developmental deficit: Impaired object recognition with normal face recognition.

Science.gov (United States)

Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

2011-05-01

Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual

First-person and third-person verbs in visual motion-perception regions.

Science.gov (United States)

Papeo, Liuba; Lingnau, Angelika

2015-02-01

Verb-related activity is consistently found in the left posterior lateral cortex (PLTC), encompassing also regions that respond to visual-motion perception. Besides motion, those regions appear sensitive to distinctions among the entities beyond motion, including that between first- vs. third-person ("third-person bias"). In two experiments, using functional magnetic resonance imaging (fMRI), we studied whether the implied subject (first/third-person) and/or the semantic content (motor/non-motor) of verbs modulate the neural activity in the left PLTC-regions responsive during basic- and biological-motion perception. In those sites, we found higher activity for verbs than for nouns. This activity was modulated by the person (but not the semantic content) of the verbs, with stronger response to third- than first-person verbs. The third-person bias elicited by verbs supports a role of motion-processing regions in encoding information about the entity beyond (and independently from) motion, and sets in a new light the role of these regions in verb processing. Copyright © 2014 Elsevier Inc. All rights reserved.

Pattern recognition methodologies and deterministic evaluation of seismic hazard: A strategy to increase earthquake preparedness

International Nuclear Information System (INIS)

Peresan, Antonella; Panza, Giuliano F.; Gorshkov, Alexander I.; Aoudia, Abdelkrim

2001-05-01

Several algorithms, structured according to a general pattern-recognition scheme, have been developed for the space-time identification of strong events. Currently, two of such algorithms are applied to the Italian territory, one for the recognition of earthquake-prone areas and the other, namely CN algorithm, for earthquake prediction purposes. These procedures can be viewed as independent experts, hence they can be combined to better constrain the alerted seismogenic area. We examine here the possibility to integrate CN intermediate-term medium-range earthquake predictions, pattern recognition of earthquake-prone areas and deterministic hazard maps, in order to associate CN Times of Increased Probability (TIPs) to a set of appropriate scenarios of ground motion. The advantage of this procedure mainly consists in the time information provided by predictions, useful to increase preparedness of safety measures and to indicate a priority for detailed seismic risk studies to be performed at a local scale. (author)

What are the visual features underlying rapid object recognition?

Directory of Open Access Journals (Sweden)

Sébastien M Crouzet

2011-11-01

Full Text Available Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically-plausible computational models of (bottom-up pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task.

Sensor agnostic object recognition using a map seeking circuit

Science.gov (United States)

Overman, Timothy L.; Hart, Michael

2012-05-01

Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

Younger and Older Users’ Recognition of Virtual Agent Facial Expressions

Science.gov (United States)

Beer, Jenay M.; Smarr, Cory-Ann; Fisk, Arthur D.; Rogers, Wendy A.

2015-01-01

As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent’s social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell, Sullivan, Prevost, & Churchill, 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck & Reichenbach, 2005; Courgeon et al. 2009; 2011; Breazeal, 2003); however, little research has compared in-depth younger and older adults’ ability to label a virtual agent’s facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a

Gravity Cues Embedded in the Kinematics of Human Motion Are Detected in Form-from-Motion Areas of the Visual System and in Motor-Related Areas.

Science.gov (United States)

Cignetti, Fabien; Chabeauti, Pierre-Yves; Menant, Jasmine; Anton, Jean-Luc J J; Schmitz, Christina; Vaugoyeau, Marianne; Assaiante, Christine

2017-01-01

The present study investigated the cortical areas engaged in the perception of graviceptive information embedded in biological motion (BM). To this end, functional magnetic resonance imaging was used to assess the cortical areas active during the observation of human movements performed under normogravity and microgravity (parabolic flight). Movements were defined by motion cues alone using point-light displays. We found that gravity modulated the activation of a restricted set of regions of the network subtending BM perception, including form-from-motion areas of the visual system (kinetic occipital region, lingual gyrus, cuneus) and motor-related areas (primary motor and somatosensory cortices). These findings suggest that compliance of observed movements with normal gravity was carried out by mapping them onto the observer's motor system and by extracting their overall form from local motion of the moving light points. We propose that judgment on graviceptive information embedded in BM can be established based on motor resonance and visual familiarity mechanisms and not necessarily by accessing the internal model of gravitational motion stored in the vestibular cortex.

Activation of Supraoptic Oxytocin Neurons by Secretin Facilitates Social Recognition.

Science.gov (United States)

Takayanagi, Yuki; Yoshida, Masahide; Takashima, Akihide; Takanami, Keiko; Yoshida, Shoma; Nishimori, Katsuhiko; Nishijima, Ichiko; Sakamoto, Hirotaka; Yamagata, Takanori; Onaka, Tatsushi

2017-02-01

Social recognition underlies social behavior in animals, and patients with psychiatric disorders associated with social deficits show abnormalities in social recognition. Oxytocin is implicated in social behavior and has received attention as an effective treatment for sociobehavioral deficits. Secretin receptor-deficient mice show deficits in social behavior. The relationship between oxytocin and secretin concerning social behavior remains to be determined. Expression of c-Fos in oxytocin neurons and release of oxytocin from their dendrites after secretin application were investigated. Social recognition was examined after intracerebroventricular or local injection of secretin, oxytocin, or an oxytocin receptor antagonist in rats, oxytocin receptor-deficient mice, and secretin receptor-deficient mice. Electron and light microscopic immunohistochemical analysis was also performed to determine whether oxytocin neurons extend their dendrites into the medial amygdala. Supraoptic oxytocin neurons expressed the secretin receptor. Secretin activated supraoptic oxytocin neurons and facilitated oxytocin release from dendrites. Secretin increased acquisition of social recognition in an oxytocin receptor-dependent manner. Local application of secretin into the supraoptic nucleus facilitated social recognition, and this facilitation was blocked by an oxytocin receptor antagonist injected into, but not outside of, the medial amygdala. In the medial amygdala, dendrite-like thick oxytocin processes were found to extend from the supraoptic nucleus. Furthermore, oxytocin treatment restored deficits of social recognition in secretin receptor-deficient mice. The results of our study demonstrate that secretin-induced dendritic oxytocin release from supraoptic neurons enhances social recognition. The newly defined secretin-oxytocin system may lead to a possible treatment for social deficits. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights

Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment

Directory of Open Access Journals (Sweden)

Muhammad Arsalan

2017-11-01

Full Text Available Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN; which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II training database (selected from the UBIRIS.v2 database and mobile iris challenge evaluation (MICHE dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.

Motion of the esophagus due to cardiac motion.

Directory of Open Access Journals (Sweden)

Jacob Palmer

Full Text Available When imaging studies (e.g. CT are used to quantify morphological changes in an anatomical structure, it is necessary to understand the extent and source of motion which can give imaging artifacts (e.g. blurring or local distortion. The objective of this study was to assess the magnitude of esophageal motion due to cardiac motion. We used retrospective electrocardiogram-gated contrast-enhanced computed tomography angiography images for this study. The anatomic region from the carina to the bottom of the heart was taken at deep-inspiration breath hold with the patients' arms raised above their shoulders, in a position similar to that used for radiation therapy. The esophagus was delineated on the diastolic phase of cardiac motion, and deformable registration was used to sequentially deform the images in nearest-neighbor phases among the 10 cardiac phases, starting from the diastolic phase. Using the 10 deformation fields generated from the deformable registration, the magnitude of the extreme displacements was then calculated for each voxel, and the mean and maximum displacement was calculated for each computed tomography slice for each patient. The average maximum esophageal displacement due to cardiac motion for all patients was 5.8 mm (standard deviation: 1.6 mm, maximum: 10.0 mm in the transverse direction. For 21 of 26 patients, the largest esophageal motion was found in the inferior region of the heart; for the other patients, esophageal motion was approximately independent of superior-inferior position. The esophagus motion was larger at cardiac phases where the electrocardiogram R-wave occurs. In conclusion, the magnitude of esophageal motion near the heart due to cardiac motion is similar to that due to other sources of motion, including respiratory motion and intra-fraction motion. A larger cardiac motion will result into larger esophagus motion in a cardiac cycle.

Influence of Visual Motion, Suggestion, and Illusory Motion on Self-Motion Perception in the Horizontal Plane.

Science.gov (United States)

Rosenblatt, Steven David; Crane, Benjamin Thomas

2015-01-01

A moving visual field can induce the feeling of self-motion or vection. Illusory motion from static repeated asymmetric patterns creates a compelling visual motion stimulus, but it is unclear if such illusory motion can induce a feeling of self-motion or alter self-motion perception. In these experiments, human subjects reported the perceived direction of self-motion for sway translation and yaw rotation at the end of a period of viewing set visual stimuli coordinated with varying inertial stimuli. This tested the hypothesis that illusory visual motion would influence self-motion perception in the horizontal plane. Trials were arranged into 5 blocks based on stimulus type: moving star field with yaw rotation, moving star field with sway translation, illusory motion with yaw, illusory motion with sway, and static arrows with sway. Static arrows were used to evaluate the effect of cognitive suggestion on self-motion perception. Each trial had a control condition; the illusory motion controls were altered versions of the experimental image, which removed the illusory motion effect. For the moving visual stimulus, controls were carried out in a dark room. With the arrow visual stimulus, controls were a gray screen. In blocks containing a visual stimulus there was an 8s viewing interval with the inertial stimulus occurring over the final 1s. This allowed measurement of the visual illusion perception using objective methods. When no visual stimulus was present, only the 1s motion stimulus was presented. Eight women and five men (mean age 37) participated. To assess for a shift in self-motion perception, the effect of each visual stimulus on the self-motion stimulus (cm/s) at which subjects were equally likely to report motion in either direction was measured. Significant effects were seen for moving star fields for both translation (p = 0.001) and rotation (pperception was shifted in the direction consistent with the visual stimulus. Arrows had a small effect on self-motion

Hybrid magnetic mechanism for active locomotion based on inchworm motion

International Nuclear Information System (INIS)

Kim, Sung Hoon; Hashi, Shuichiro; Ishiyama, Kazushi

2013-01-01

Magnetic robots have been studied in the past. Insect-type micro-robots are used in various biomedical applications; researchers have developed inchworm micro-robots for endoscopic use. A biological inchworm has a looping locomotion gait. However, most inchworm micro-robots depend on a general bending, or bellows, motion. In this paper, we introduce a new robotic mechanism using magnetic force and torque control in a rotating magnetic field for a looping gait. The proposed robot is controlled by the magnetic torque, attractive force, and body mechanisms (two stoppers, flexible body, and different frictional legs). The magnetic torque generates a general bending motion. In addition, the attractive force and body mechanisms produce the robot’s looping motion within a rotating magnetic field and without the use of an algorithm for field control. We verified the device’s performance and analyzed the motion through simulations and various experiments. The robot mechanism can be applied to active locomotion for various medical robots, such as wireless endoscopes. (technical note)

Object recognition with hierarchical discriminant saliency networks

Directory of Open Access Journals (Sweden)

Sunhyoung eHan

2014-09-01

Full Text Available The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognitionmodel, the hierarchical discriminant saliency network (HDSN, whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. The HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a neuralnetwork implementation, all layers are convolutional and implement acombination of filtering, rectification, and pooling. The rectificationis performed with a parametric extension of the now popular rectified linearunits (ReLUs, whose parameters can be tuned for the detection of targetobject classes. This enables a number of functional enhancementsover neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation ofsaliency responses by the discriminant power of the underlying features,and the ability to detect both feature presence and absence.In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity totarget object classes and invariance. The resulting performance demonstrates benefits for all the functional enhancements of the HDSN.

Recognizing human actions by learning and matching shape-motion prototype trees.

Science.gov (United States)

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2012-03-01

A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.

Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

OpenAIRE

Kruger, Norbert; Janssen, Peter; Kalkan, Sinan; Lappe, Markus; Leonardis, Ales; Piater, Justus; Rodriguez-Sanchez, Antonio J.; Wiskott, Laurenz

2013-01-01

Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition or vision-based navigation and manipulation. This article reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer ...

Nano-motion dynamics are determined by surface-tethered selectin mechanokinetics and bond formation.

Directory of Open Access Journals (Sweden)

Brian J Schmidt

2009-12-01

Full Text Available The interaction of proteins at cellular interfaces is critical for many biological processes, from intercellular signaling to cell adhesion. For example, the selectin family of adhesion receptors plays a critical role in trafficking during inflammation and immunosurveillance. Quantitative measurements of binding rates between surface-constrained proteins elicit insight into how molecular structural details and post-translational modifications contribute to function. However, nano-scale transport effects can obfuscate measurements in experimental assays. We constructed a biophysical simulation of the motion of a rigid microsphere coated with biomolecular adhesion receptors in shearing flow undergoing thermal motion. The simulation enabled in silico investigation of the effects of kinetic force dependence, molecular deformation, grouping adhesion receptors into clusters, surface-constrained bond formation, and nano-scale vertical transport on outputs that directly map to observable motions. Simulations recreated the jerky, discrete stop-and-go motions observed in P-selectin/PSGL-1 microbead assays with physiologic ligand densities. Motion statistics tied detailed simulated motion data to experimentally reported quantities. New deductions about biomolecular function for P-selectin/PSGL-1 interactions were made. Distributing adhesive forces among P-selectin/PSGL-1 molecules closely grouped in clusters was necessary to achieve bond lifetimes observed in microbead assays. Initial, capturing bond formation effectively occurred across the entire molecular contour length. However, subsequent rebinding events were enhanced by the reduced separation distance following the initial capture. The result demonstrates that vertical transport can contribute to an enhancement in the apparent bond formation rate. A detailed analysis of in silico motions prompted the proposition of wobble autocorrelation as an indicator of two-dimensional function. Insight into two

Speech Recognition

Directory of Open Access Journals (Sweden)

Adrian Morariu

2009-01-01

Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

Conserving forest biological diversity: How the Montreal Process helps achieve sustainability

Science.gov (United States)

Mark Nelson; Guy Robertson; Kurt. Riitters

2015-01-01

Forests support a variety of ecosystems, species and genes — collectively referred to as biological diversity — along with important processes that tie these all together. With the growing recognition that biological diversity contributes to human welfare in a number of important ways such as providing food, medicine and fiber (provisioning services...

Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

Directory of Open Access Journals (Sweden)

Koji Iwano

2007-03-01

Full Text Available This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments. Our proposed method assumes that lip images can be captured using a small camera installed in a handset. Two different kinds of lip features, lip-contour geometric features and lip-motion velocity features, are used individually or jointly, in combination with audio features. Phoneme HMMs modeling the audio and visual features are built based on the multistream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions. These visual features were confirmed to be effective even when the audio HMM was adapted to noise by the MLLR method.

Influence of Visual Motion, Suggestion, and Illusory Motion on Self-Motion Perception in the Horizontal Plane.

Directory of Open Access Journals (Sweden)

Steven David Rosenblatt

Full Text Available A moving visual field can induce the feeling of self-motion or vection. Illusory motion from static repeated asymmetric patterns creates a compelling visual motion stimulus, but it is unclear if such illusory motion can induce a feeling of self-motion or alter self-motion perception. In these experiments, human subjects reported the perceived direction of self-motion for sway translation and yaw rotation at the end of a period of viewing set visual stimuli coordinated with varying inertial stimuli. This tested the hypothesis that illusory visual motion would influence self-motion perception in the horizontal plane. Trials were arranged into 5 blocks based on stimulus type: moving star field with yaw rotation, moving star field with sway translation, illusory motion with yaw, illusory motion with sway, and static arrows with sway. Static arrows were used to evaluate the effect of cognitive suggestion on self-motion perception. Each trial had a control condition; the illusory motion controls were altered versions of the experimental image, which removed the illusory motion effect. For the moving visual stimulus, controls were carried out in a dark room. With the arrow visual stimulus, controls were a gray screen. In blocks containing a visual stimulus there was an 8s viewing interval with the inertial stimulus occurring over the final 1s. This allowed measurement of the visual illusion perception using objective methods. When no visual stimulus was present, only the 1s motion stimulus was presented. Eight women and five men (mean age 37 participated. To assess for a shift in self-motion perception, the effect of each visual stimulus on the self-motion stimulus (cm/s at which subjects were equally likely to report motion in either direction was measured. Significant effects were seen for moving star fields for both translation (p = 0.001 and rotation (p0.1 for both. Thus, although a true moving visual field can induce self-motion, results of this

Near-infrared light-triggered "on/off" motion of polymer multilayer rockets.

Science.gov (United States)

Wu, Zhiguang; Lin, Xiankun; Wu, Yingjie; Si, Tieyan; Sun, Jianmin; He, Qiang

2014-06-24

We describe an approach to modulating the on-demand motion of catalytic polymer-based microengines via near-infrared (NIR) laser irradiation. The polymer multilayer motor was fabricated by the template-assisted layer-by-layer assembly and subsequently deposition of platinum nanoparticles inside and a thin gold shell outside. Then a mixed monolayer of a tumor-targeted peptide and an antifouling poly(ethylene glycol) was functionalized on the gold shell. The microengines remain motionless at the critical peroxide concentration (0.1%, v/v); however, NIR illumination on the engines leads to a photothermal effect and thus rapidly triggers the motion of the catalytic engines. Computational modeling explains the photothermal effect and gives the temperature profile accordingly. Also, the photothermal effect can alone activate the motion of the engines in the absence of the peroxide fuel, implying that it may eliminate the use of toxic fuel in the future. The targeted recognition ability and subsequently killing of cancer cells by the photothermal effect under the higher power of a NIR laser were illustrated. Our results pave the way to apply self-propelled synthetic engines in biomedical fields.

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.

Science.gov (United States)

Janidarmian, Majid; Roshan Fekr, Atena; Radecka, Katarzyna; Zilic, Zeljko

2017-03-07

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

Automatic recognition of falls in gait-slip training: Harness load cell based criteria.

Science.gov (United States)

Yang, Feng; Pai, Yi-Chung

2011-08-11

Over-head-harness systems, equipped with load cell sensors, are essential to the participants' safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force, and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects' trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects' data revealed that the peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. Copyright © 2011 Elsevier Ltd. All rights reserved.

AUTOMATIC RECOGNITION OF FALLS IN GAIT-SLIP: A HARNESS LOAD CELL BASED CRITERION

Science.gov (United States)

Yang, Feng; Pai, Yi-Chung

2012-01-01

Over-head-harness systems, equipped with load cell sensors, are essential to the participants’ safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7-m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects’ trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects’ data revealed that peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1-s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. PMID:21696744

Computing motion using resistive networks

Science.gov (United States)

Koch, Christof; Luo, Jin; Mead, Carver; Hutchinson, James

1988-01-01

Recent developments in the theory of early vision are described which lead from the formulation of the motion problem as an ill-posed one to its solution by minimizing certain 'cost' functions. These cost or energy functions can be mapped onto simple analog and digital resistive networks. It is shown how the optical flow can be computed by injecting currents into resistive networks and recording the resulting stationary voltage distribution at each node. These networks can be implemented in cMOS VLSI circuits and represent plausible candidates for biological vision systems.

Character recognition from trajectory by recurrent spiking neural networks.

Science.gov (United States)

Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

2017-07-01

Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

Primate Auditory Recognition Memory Performance Varies With Sound Type

OpenAIRE

Chi-Wing, Ng; Bethany, Plakke; Amy, Poremba

2009-01-01

Neural correlates of auditory processing, including for species-specific vocalizations that convey biological and ethological significance (e.g. social status, kinship, environment),have been identified in a wide variety of areas including the temporal and frontal cortices. However, few studies elucidate how non-human primates interact with these vocalization signals when they are challenged by tasks requiring auditory discrimination, recognition, and/or memory. The present study employs a de...

Perspective: Watching low-frequency vibrations of water in biomolecular recognition by THz spectroscopy

Science.gov (United States)

Xu, Yao; Havenith, Martina

2015-11-01

Terahertz (THz) spectroscopy has turned out to be a powerful tool which is able to shed new light on the role of water in biomolecular processes. The low frequency spectrum of the solvated biomolecule in combination with MD simulations provides deep insights into the collective hydrogen bond dynamics on the sub-ps time scale. The absorption spectrum between 1 THz and 10 THz of solvated biomolecules is sensitive to changes in the fast fluctuations of the water network. Systematic studies on mutants of antifreeze proteins indicate a direct correlation between biological activity and a retardation of the (sub)-ps hydration dynamics at the protein binding site, i.e., a "hydration funnel." Kinetic THz absorption studies probe the temporal changes of THz absorption during a biological process, and give access to the kinetics of the coupled protein-hydration dynamics. When combined with simulations, the observed results can be explained in terms of a two-tier model involving a local binding and a long range influence on the hydration bond dynamics of the water around the binding site that highlights the significance of the changes in the hydration dynamics at recognition site for biomolecular recognition. Water is shown to assist molecular recognition processes.

Parallel Molecular Distributed Detection With Brownian Motion.

Science.gov (United States)

Rogers, Uri; Koh, Min-Sung

2016-12-01

This paper explores the in vivo distributed detection of an undesired biological agent's (BAs) biomarkers by a group of biological sized nanomachines in an aqueous medium under drift. The term distributed, indicates that the system information relative to the BAs presence is dispersed across the collection of nanomachines, where each nanomachine possesses limited communication, computation, and movement capabilities. Using Brownian motion with drift, a probabilistic detection and optimal data fusion framework, coined molecular distributed detection, will be introduced that combines theory from both molecular communication and distributed detection. Using the optimal data fusion framework as a guide, simulation indicates that a sub-optimal fusion method exists, allowing for a significant reduction in implementation complexity while retaining BA detection accuracy.

Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei

KAUST Repository

Wang, Jim Jing-Yan; Gao, Xin

2014-01-01

for the purposes of both biological research and drug design. In this paper, we investigate computationally recognizing flagellar proteins in T. Brucei by pattern recognition methods. It is argued that an optimal decision function can be obtained as the difference

Entity recognition in the biomedical domain using a hybrid approach.

Science.gov (United States)

Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

2017-11-09

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

Super Normal Vector for Human Activity Recognition with Depth Cameras.

Science.gov (United States)

Yang, Xiaodong; Tian, YingLi

2017-05-01

The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information. We then propose a general scheme of super normal vector (SNV) to aggregate the low-level polynormals into a discriminative representation, which can be viewed as a simplified version of the Fisher kernel representation. In order to globally capture the spatial layout and temporal order, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time cells. In the extensive experiments, the proposed approach achieves superior performance to the state-of-the-art methods on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.

Circular random motion in diatom gliding under isotropic conditions

International Nuclear Information System (INIS)

Gutiérrez-Medina, Braulio; Maldonado, Ana Iris Peña; Guerra, Andrés Jiménez; Rubio, Yadiralia Covarrubias; Meza, Jessica Viridiana García

2014-01-01

How cells migrate has been investigated primarily for the case of trajectories composed by joined straight segments. In contrast, little is known when cellular motion follows intrinsically curved paths. Here, we use time-lapse optical microscopy and automated trajectory tracking to investigate how individual cells of the diatom Nitzschia communis glide across surfaces under isotropic environmental conditions. We find a distinct kind of random motion, where trajectories are formed by circular arcs traveled at constant speed, alternated with random stoppages, direction reversals and changes in the orientation of the arcs. Analysis of experimental and computer-simulated trajectories show that the circular random motion of diatom gliding is not optimized for long-distance travel but rather for recurrent coverage of limited surface area. These results suggest that one main biological role for this type of diatom motility is to efficiently build the foundation of algal biofilms. (paper)

Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices

OpenAIRE

Daoudi , Mohamed; Berretti , Stefano; Pala , Pietro; Delevoye , Yvonne ,; Bimbo , Alberto ,

2017-01-01

International audience; Emotion recognition is attracting great interest for its potential application in a multitude of real-life situations. Much of the Computer Vision research in this field has focused on relating emotions to facial expressions, with investigations rarely including more than upper body. In this work, we propose a new scenario, for which emotional states are related to 3D dynamics of the whole body motion. To address the complexity of human body movement, we used covarianc...

RECOGNITION AND VALUATION OF BIOLOGICAL ASSETS IN TOURISM AREA. INTERNATIONAL ACCOUNTING STANDARDS

Directory of Open Access Journals (Sweden)

Corina IOANĂŞ

2009-06-01

Full Text Available Consistent with the Financial Reporting Standards Board's international convergence and harmonization policy it is proposed that a new accounting regime will prescribe the financial reporting practice and minimum disclosure requirements for agricultural activities, including the fair value of biological assets. In any financial report, the inclusion of biological assets may confuse the reality of the income profit and the wealth profit. There are many reasons it may provide misleading figures, the most obvious would be because the entity may have reported the value of heritage properties that do not actually generate any income but rather they are properties, which actually generate expenses for the entity, for example in maintenance costs. For any regime that requires entities to account and report on biological assets there should be a clear classification system that takes into account the different types of ownership structures in a society. Therefore in Romania, it is important that any financial reporting regime on biological assets should provide for the difference between business assets and cultural assets.

Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition

Science.gov (United States)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2018-03-01

The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference

Laban movement analysis to classify emotions from motion

Science.gov (United States)

Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

2018-04-01

In this paper, we present the study of Laban Movement Analysis (LMA) to understand basic human emotions from nonverbal human behaviors. While there are a lot of studies on understanding behavioral patterns based on natural language processing and speech processing applications, understanding emotions or behavior from non-verbal human motion is still a very challenging and unexplored field. LMA provides a rich overview of the scope of movement possibilities. These basic elements can be used for generating movement or for describing movement. They provide an inroad to understanding movement and for developing movement efficiency and expressiveness. Each human being combines these movement factors in his/her own unique way and organizes them to create phrases and relationships which reveal personal, artistic, or cultural style. In this work, we build a motion descriptor based on a deep understanding of Laban theory. The proposed descriptor builds up on previous works and encodes experiential features by using temporal windows. We present a more conceptually elaborate formulation of Laban theory and test it in a relatively new domain of behavioral research with applications in human-machine interaction. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. We test our algorithm on UCLIC dataset which consists of body motions of 13 non-professional actors portraying angry, fear, happy and sad emotions. We achieve an accuracy of 87.30% on this dataset.

Fiscal 1993 technological survey report. R and D project for industrial science and technology (Assignment by NEDO/R and D of biochip - survey on biological research of biochip); 1993 nendo bio soshi no kenkyu kaihatsu seika hokokusho. Bio soshi seibutsu kenkyu chosa

Energy Technology Data Exchange (ETDEWEB)

NONE

1994-04-01

As a part of the R and D of a biochip, for the purpose of establishing fundamental technology in connection with a biochip having a new function that is nonexistent in a semiconductor, a committee for biological research and survey of biochip was organized, operating research activities, surveying and compiling research situations and technological trend at the forefront of each field, in regard to information processing in the biological field, brain-memory related information processing in the medical field, and biologically simulated information processing in the engineering field. The results were summarized in the following seven areas. 1. neurology of memory, 2. the frontal lobe and recognition, 3. function of the olfactory lobe and neural connection between the olfactory lobe and the hippocampus, 4. the cerebral fundus nucleus and function, 5. coordinate change from vision to motion in the brain, and 6. control of transient potassium current by the astroglia cell in the mouth and the hippocampus cultured nerve cell. (NEDO)

Structure and function in biology

International Nuclear Information System (INIS)

Hirs, C.H.W.

1976-01-01

A summary is given of the history of the developments of structural chemistry in biology beginning with the work of the bacteriologist Ehrlich leading to a comprehensive examination of the influence of size and configuration on the interaction between specific antibodies and side-chain determinants. Recent developments include the recognition of a higher order of specificity in the interaction of proteins with one another

Re-thinking employee recognition: understanding employee experiences of recognition

OpenAIRE

Smith, Charlotte

2013-01-01

Despite widespread acceptance of the importance of employee recognition for both individuals and organisations and evidence of its increasing use in organisations, employee recognition has received relatively little focused attention from academic researchers. Particularly lacking is research exploring the lived experience of employee recognition and the interpretations and meanings which individuals give to these experiences. Drawing on qualitative interviews conducted as part of my PhD rese...

The Role of Motion Concepts in Understanding Non-Motion Concepts

Directory of Open Access Journals (Sweden)

Omid Khatin-Zadeh

2017-12-01

Full Text Available This article discusses a specific type of metaphor in which an abstract non-motion domain is described in terms of a motion event. Abstract non-motion domains are inherently different from concrete motion domains. However, motion domains are used to describe abstract non-motion domains in many metaphors. Three main reasons are suggested for the suitability of motion events in such metaphorical descriptions. Firstly, motion events usually have high degrees of concreteness. Secondly, motion events are highly imageable. Thirdly, components of any motion event can be imagined almost simultaneously within a three-dimensional space. These three characteristics make motion events suitable domains for describing abstract non-motion domains, and facilitate the process of online comprehension throughout language processing. Extending the main point into the field of mathematics, this article discusses the process of transforming abstract mathematical problems into imageable geometric representations within the three-dimensional space. This strategy is widely used by mathematicians to solve highly abstract and complex problems.

Use of the recognition heuristic depends on the domain's recognition validity, not on the recognition validity of selected sets of objects.

Science.gov (United States)

Pohl, Rüdiger F; Michalkiewicz, Martha; Erdfelder, Edgar; Hilbig, Benjamin E

2017-07-01

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

Electron spin echo studies of the internal motion of radicals in crystals: Phase memory vs correlation time

International Nuclear Information System (INIS)

Kispert, L.D.; Bowman, M.K.; Norris, J.R.; Brown, M.S.

1982-01-01

An electron spin echo (ESE) study of the internal motion of the CH 2 protons in irradiated zinc acetate dihydrate crystals shows that quantitative measurements of the motional correlation time can be obtained quite directly from pulsed measurements. In the slow motional limit, the motional correlation time is equal to the phase memory time determined by ESE. In the fast motional limit, the motional correlation time is proportional to the no motion spectral second moment divided by the ESE phase memory time. ESE offers a convenient method of studying motion, electron transfer, conductivity, etc. in a variety of systems too complicated for study by ordinary EPR. New systems for study by ESE include biological samples, organic polymers, liquid solutions of radicals with unresolved hyperfine, etc. When motion modulates large anisotropic hyperfine couplings, ESE measurements of the phase memory time are sensitive to modulation of pseudosecular hyperfine interactions

Autonomous learning in gesture recognition by using lobe component analysis

Science.gov (United States)

Lu, Jian; Weng, Juyang

2007-02-01

Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.

Graphical symbol recognition

OpenAIRE

K.C. , Santosh; Wendling , Laurent

2015-01-01

International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

Off-lattice pattern recognition scheme for kinetic Monte Carlo simulations

International Nuclear Information System (INIS)

Nandipati, Giridhar; Kara, Abdelkader; Shah, Syed Islamuddin; Rahman, Talat S.

2012-01-01

We report the development of a pattern-recognition scheme for the off-lattice self-learning kinetic Monte Carlo (KMC) method, one that is simple and flexible enough that it can be applied to all types of surfaces. In this scheme, to uniquely identify the local environment and associated processes involving three-dimensional (3D) motion of an atom or atoms, space around a central atom is divided into 3D rectangular boxes. The dimensions and the number of 3D boxes are determined by the accuracy with which a process needs to be identified and a process is described as the central atom moving to a neighboring vacant box accompanied by the motion of any other atom or atoms in its surrounding boxes. As a test of this method to we apply it to examine the decay of 3D Cu islands on the Cu(100) and to the surface diffusion of a Cu monomer and a dimer on Cu(111) and compare the results and computational efficiency to those available in the literature.

Effects of Age and Gender on Hand Motion Tasks

Directory of Open Access Journals (Sweden)

Wing Lok Au

2015-01-01

Full Text Available Objective. Wearable and wireless motion sensor devices have facilitated the automated computation of speed, amplitude, and rhythm of hand motion tasks. The aim of this study is to determine if there are any biological influences on these kinematic parameters. Methods. 80 healthy subjects performed hand motion tasks twice for each hand, with movements measured using a wireless motion sensor device (Kinesia, Cleveland Medical Devices Inc., Cleveland, OH. Multivariate analyses were performed with age, gender, and height added into the model. Results. Older subjects performed poorer in finger tapping (FT speed (r=0.593, p<0.001, hand-grasp (HG speed (r=0.517, p<0.001, and pronation-supination (PS speed (r=0.485, p<0.001. Men performed better in FT rhythm p<0.02, HG speed p<0.02, HG amplitude p<0.02, and HG rhythm p<0.05. Taller subjects performed better in the speed and amplitude components of FT p<0.02 and HG tasks p<0.02. After multivariate analyses, only age and gender emerged as significant independent factors influencing the speed but not the amplitude and rhythm components of hand motion tasks. Gender exerted an independent influence only on HG speed, with better performance in men p<0.05. Conclusions. Age, gender, and height are not independent factors influencing the amplitude and rhythm components of hand motion tasks. The speed component is affected by age and gender differences.

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

Science.gov (United States)

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

Streaming and particle motion in acoustically-actuated leaky systems

Science.gov (United States)

Nama, Nitesh; Barnkob, Rune; Jun Huang, Tony; Kahler, Christian; Costanzo, Francesco

2017-11-01

The integration of acoustics with microfluidics has shown great promise for applications within biology, chemistry, and medicine. A commonly employed system to achieve this integration consists of a fluid-filled, polymer-walled microchannel that is acoustically actuated via standing surface acoustic waves. However, despite significant experimental advancements, the precise physical understanding of such systems remains a work in progress. In this work, we investigate the nature of acoustic fields that are setup inside the microchannel as well as the fundamental driving mechanism governing the fluid and particle motion in these systems. We provide an experimental benchmark using state-of-art 3D measurements of fluid and particle motion and present a Lagrangian velocity based temporal multiscale numerical framework to explain the experimental observations. Following verification and validation, we employ our numerical model to reveal the presence of a pseudo-standing acoustic wave that drives the acoustic streaming and particle motion in these systems.

Human action recognition using trajectory-based representation

Directory of Open Access Journals (Sweden)

Haiam A. Abdul-Azim

2015-07-01

Full Text Available Recognizing human actions in video sequences has been a challenging problem in the last few years due to its real-world applications. A lot of action representation approaches have been proposed to improve the action recognition performance. Despite the popularity of local features-based approaches together with “Bag-of-Words” model for action representation, it fails to capture adequate spatial or temporal relationships. In an attempt to overcome this problem, a trajectory-based local representation approaches have been proposed to capture the temporal information. This paper introduces an improvement of trajectory-based human action recognition approaches to capture discriminative temporal relationships. In our approach, we extract trajectories by tracking the detected spatio-temporal interest points named “cuboid features” with matching its SIFT descriptors over the consecutive frames. We, also, propose a linking and exploring method to obtain efficient trajectories for motion representation in realistic conditions. Then the volumes around the trajectories’ points are described to represent human actions based on the Bag-of-Words (BOW model. Finally, a support vector machine is used to classify human actions. The effectiveness of the proposed approach was evaluated on three popular datasets (KTH, Weizmann and UCF sports. Experimental results showed that the proposed approach yields considerable performance improvement over the state-of-the-art approaches.

An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity.

Science.gov (United States)

Shaikh, Danish; Manoonpong, Poramate

2017-01-01

Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities-0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking.

Intelligent Facial Recognition Systems: Technology advancements for security applications

Energy Technology Data Exchange (ETDEWEB)

Beer, C.L.

1993-07-01

Insider problems such as theft and sabotage can occur within the security and surveillance realm of operations when unauthorized people obtain access to sensitive areas. A possible solution to these problems is a means to identify individuals (not just credentials or badges) in a given sensitive area and provide full time personnel accountability. One approach desirable at Department of Energy facilities for access control and/or personnel identification is an Intelligent Facial Recognition System (IFRS) that is non-invasive to personnel. Automatic facial recognition does not require the active participation of the enrolled subjects, unlike most other biological measurement (biometric) systems (e.g., fingerprint, hand geometry, or eye retinal scan systems). It is this feature that makes an IFRS attractive for applications other than access control such as emergency evacuation verification, screening, and personnel tracking. This paper discusses current technology that shows promising results for DOE and other security applications. A survey of research and development in facial recognition identified several companies and universities that were interested and/or involved in the area. A few advanced prototype systems were also identified. Sandia National Laboratories is currently evaluating facial recognition systems that are in the advanced prototype stage. The initial application for the evaluation is access control in a controlled environment with a constant background and with cooperative subjects. Further evaluations will be conducted in a less controlled environment, which may include a cluttered background and subjects that are not looking towards the camera. The outcome of the evaluations will help identify areas of facial recognition systems that need further development and will help to determine the effectiveness of the current systems for security applications.

Biological markers for kidney injury and renal function in the intensive care unit

NARCIS (Netherlands)

Royakkers, A.A.N.M.

2014-01-01

The purpose of the investigations described in this thesis was to seek for answers to two relevant questions in ICUs in resource-rich settings, i.e., can new biological markers play a role in early recognition of AKI, and can new biological markers predict recovery of renal function in patients who

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Directory of Open Access Journals (Sweden)

Majid Janidarmian

2017-03-01

Full Text Available Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

The Value of Humans in the Biological Exploration of Space

Science.gov (United States)

Cockell, C. S.

2004-06-01

Regardless of the discovery of life on Mars, or of "no apparent life" on Mars, the questions that follow will provide a rich future for biological exploration. Extraordinary pattern recognition skills, decadal assimilation of data and experience, and rapid sample acquisition are just three of the characteristics that make humans the best means we have to explore the biological potential of Mars and other planetary surfaces. I make the case that instead of seeing robots as in conflict, or even in support, of human exploration activity, from the point of view of scientific data gathering and analysis, we should view humans as the most powerful robots we have, thus removing the separation that dogs discussions on the exploration of space. The narrow environmental requirements of humans, although imposing constraints on the life support systems required, is more than compensated for by their capabilities in biological exploration. I support this view with an example of the "Christmas present effect," a simple demonstration of human data and pattern recognition capabilities.

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

Hand motion segmentation against skin colour background in breast awareness applications.

Science.gov (United States)

Hu, Yuqin; Naguib, Raouf N G; Todman, Alison G; Amin, Saad A; Al-Omishy, Hassanein; Oikonomou, Andreas; Tucker, Nick

2004-01-01

Skin colour modelling and classification play significant roles in face and hand detection, recognition and tracking. A hand is an essential tool used in breast self-examination, which needs to be detected and analysed during the process of breast palpation. However, the background of a woman's moving hand is her breast that has the same or similar colour as the hand. Additionally, colour images recorded by a web camera are strongly affected by the lighting or brightness conditions. Hence, it is a challenging task to segment and track the hand against the breast without utilising any artificial markers, such as coloured nail polish. In this paper, a two-dimensional Gaussian skin colour model is employed in a particular way to identify a breast but not a hand. First, an input image is transformed to YCbCr colour space, which is less sensitive to the lighting conditions and more tolerant of skin tone. The breast, thus detected by the Gaussian skin model, is used as the baseline or framework for the hand motion. Secondly, motion cues are used to segment the hand motion against the detected baseline. Desired segmentation results have been achieved and the robustness of this algorithm is demonstrated in this paper.

Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

Science.gov (United States)

Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

2016-06-01

Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

A survey of visual preprocessing and shape representation techniques

Science.gov (United States)

Olshausen, Bruno A.

1988-01-01

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Interests-in-motion in an informal, media-rich learning setting

Directory of Open Access Journals (Sweden)

Ty Hollett

2016-01-01

Full Text Available Much of the literature related to connected learning approaches youth interests as fixed on specific disciplines or activities (e.g. STEM, music production, or game design. As such, mentors design youth-focused programs to serve those interests. Through a micro-ethnographic analysis of two youth’s Minecraft-centered gameplay in a public library, this article makes two primary contributions to research on learning within, and the design of, informal, media-rich settings. First, rather than approach youth interests as fixed on specific disciplines or activities (e.g. STEM, music production, or video games, this article traces youth interests as they spark and emerge among individuals and groups. Then, it follows those interests as they subsequently spread over time, becoming interests-in-motion. Second, recognition of these interests-in-motion can lead mentors to develop program designs that enable learners to work with artifacts (digital and physical that learners can progressively configure and re-configure over time. Mentors, then, design-in-time as they harness the energy surrounding those emergent interests, creating extending learning opportunities in response.

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

Directory of Open Access Journals (Sweden)

Peter U. Diehl

2015-08-01

Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

Applications of chaotic neurodynamics in pattern recognition

Science.gov (United States)

Baird, Bill; Freeman, Walter J.; Eeckman, Frank H.; Yao, Yong

1991-08-01

Network algorithms and architectures for pattern recognition derived from neural models of the olfactory system are reviewed. These span a range from highly abstract to physiologically detailed, and employ the kind of dynamical complexity observed in olfactory cortex, ranging from oscillation to chaos. A simple architecture and algorithm for analytically guaranteed associative memory storage of analog patterns, continuous sequences, and chaotic attractors in the same network is described. A matrix inversion determines network weights, given prototype patterns to be stored. There are N units of capacity in an N node network with 3N2 weights. It costs one unit per static attractor, two per Fourier component of each sequence, and three to four per chaotic attractor. There are no spurious attractors, and for sequences there is a Liapunov function in a special coordinate system which governs the approach of transient states to stored trajectories. Unsupervised or supervised incremental learning algorithms for pattern classification, such as competitive learning or bootstrap Widrow-Hoff can easily be implemented. The architecture can be ''folded'' into a recurrent network with higher order weights that can be used as a model of cortex that stores oscillatory and chaotic attractors by a Hebb rule. Network performance is demonstrated by application to the problem of real-time handwritten digit recognition. An effective system with on-line learning has been written by Eeckman and Baird for the Macintosh. It utilizes static, oscillatory, and/or chaotic attractors of two kinds--Lorenze attractors, or attractors resulting from chaotically interacting oscillatory modes. The successful application to an industrial pattern recognition problem of a network architecture of considerable physiological and dynamical complexity, developed by Freeman and Yao, is described. The data sets of the problem come in three classes of difficulty, and performance of the biological network is

Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

KAUST Repository

Younis, Sohaib; Weiland, Claus; Hoehndorf, Robert; Dressler, Stefan; Hickler, Thomas; Seeger, Bernhard; Schmidt, Marco

2018-01-01

Herbaria worldwide are housing a treasure of hundreds of millions of herbarium specimens, which are increasingly being digitized and thereby more accessible to the scientific community. At the same time, deep-learning algorithms are rapidly improving pattern recognition from images and these techniques are more and more being applied to biological objects. In this study, we are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains suggests substantial potential of this approach for supporting taxonomy and natural history collection management. Trait recognition is also promising for applications in functional ecology.

Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

KAUST Repository

Younis, Sohaib

2018-03-13

Herbaria worldwide are housing a treasure of hundreds of millions of herbarium specimens, which are increasingly being digitized and thereby more accessible to the scientific community. At the same time, deep-learning algorithms are rapidly improving pattern recognition from images and these techniques are more and more being applied to biological objects. In this study, we are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains suggests substantial potential of this approach for supporting taxonomy and natural history collection management. Trait recognition is also promising for applications in functional ecology.

Improving the Robustness of Electromyogram-Pattern Recognition for Prosthetic Control by a Postprocessing Strategy

Directory of Open Access Journals (Sweden)

Xu Zhang

2017-09-01

Full Text Available Electromyogram (EMG contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager–Kaiser energy operator, or mechanomyogram (MMG. Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5% in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control.

Structure of the Melajo clay near Arima, Trinidad and strike-slip motion in the El Pilar fault zone

Science.gov (United States)

Robertson, P.; Burke, K.; Wadge, G.

1985-01-01

No consensus has yet emerged on the sense, timing and amount of motion in the El Pilar fault zone. As a contribution to the study of this problem, a critical area within the zone in North Central Trinidad has been mapped. On the basis of the mapping, it is concluded that the El Pilar zone has been active in right-lateral strike-slip motion during the Pleistocene. Recognition of structural styles akin to those of the mapped area leads to the suggestion that the El Pilar zone is part of a 300 km wide plate boundary zone extending from the Orinoco delta northward to Grenada. Lateral motion of the Caribbean plate with respect to South America has been suggested to amount to 1900 km in the last 38 Ma. Part of this displacement since the Miocene can be readily accommodated within the broad zone identified here. No one fault system need account for more than a fraction of the total motion and all faults need not be active simultaneously.

Motion of Knots in DNA Stretched by Elongational Fields

Science.gov (United States)

Klotz, Alexander R.; Soh, Beatrice W.; Doyle, Patrick S.

2018-05-01

Knots in DNA occur in biological systems, serve as a model system for polymer entanglement, and affect the efficacy of modern genomics technologies. We study the motion of complex knots in DNA by stretching molecules with a divergent electric field that provides an elongational force. We demonstrate that the motion of knots is nonisotropic and driven towards the closest end of the molecule. We show for the first time experimentally that knots can go from a mobile to a jammed state by varying an applied strain rate, and that this jamming is reversible. We measure the mobility of knots as a function of strain rate, demonstrating the conditions under which knots can be driven towards the ends of the molecule and untied.

Motion-blurred star acquisition method of the star tracker under high dynamic conditions.

Science.gov (United States)

Sun, Ting; Xing, Fei; You, Zheng; Wei, Minsong

2013-08-26

The star tracker is one of the most promising attitude measurement devices used in spacecraft due to its extremely high accuracy. However, high dynamic performance is still one of its constraints. Smearing appears, making it more difficult to distinguish the energy dispersive star point from the noise. An effective star acquisition approach for motion-blurred star image is proposed in this work. The correlation filter and mathematical morphology algorithm is combined to enhance the signal energy and evaluate slowly varying background noise. The star point can be separated from most types of noise in this manner, making extraction and recognition easier. Partial image differentiation is then utilized to obtain the motion parameters from only one image of the star tracker based on the above process. Considering the motion model, the reference window is adopted to perform centroid determination. Star acquisition results of real on-orbit star images and laboratory validation experiments demonstrate that the method described in this work is effective and the dynamic performance of the star tracker could be improved along with more identified stars and guaranteed position accuracy of the star point.

Specificity and multiplicity in the recognition of individuals: implications for the evolution of social behaviour.

Science.gov (United States)

Wiley, R H

2013-02-01

Recognition of conspecifics occurs when individuals classify sets of conspecifics based on sensory input from them and associate these sets with different responses. Classification of conspecifics can vary in specificity (the number of individuals included in a set) and multiplicity (the number of sets differentiated). In other words, the information transmitted varies in complexity. Although recognition of conspecifics has been reported in a wide variety of organisms, few reports have addressed the specificity or multiplicity of this capability. This review discusses examples of these patterns, the mechanisms that can produce them, and the evolution of these mechanisms. Individual recognition is one end of a spectrum of specificity, and binary classification of conspecifics is one end of a spectrum of multiplicity. In some cases, recognition requires no more than simple forms of learning, such as habituation, yet results in individually specific recognition. In other cases, recognition of individuals involves complex associations of multiple cues with multiple previous experiences in particular contexts. Complex mechanisms for recognition are expected to evolve only when simpler mechanisms do not provide sufficient specificity and multiplicity to obtain the available advantages. In particular, the evolution of cooperation and deception is always promoted by specificity and multiplicity in recognition. Nevertheless, there is only one demonstration that recognition of specific individuals contributes to cooperation in animals other than primates. Human capacities for individual recognition probably have a central role in the evolution of complex forms of human cooperation and deception. Although relatively little studied, this capability probably rivals cognitive abilities for language. © 2012 The Author. Biological Reviews © 2012 Cambridge Philosophical Society.

Barrier island facies models and recognition criteria

Science.gov (United States)

Mulhern, J.; Johnson, C. L.

2017-12-01

Barrier island outcrops record transgressive shoreline motion at geologic timescales, providing integral clues to understanding how coastlines respond to rising sea levels. However, barrier island deposits are difficult to recognize. While significant progress has been made in understanding the modern coastal morphodynamics, this insight is not fully leveraged in existing barrier island facies models. Excellent outcrop exposures of the paralic Upper Cretaceous Straight Cliffs Formation of southern Utah provide an opportunity to revise facies models and recognition criteria for barrier island deposits. Preserved barrier islands are composed of three main architectural elements (shorefaces, tidal inlets, and tidal channels) which occur independently or in combination to create larger-scale barrier island deposits. Barrier island shorefaces record progradation, while barrier island tidal inlets record lateral migration, and barrier island tidal channels record aggradation within the tidal inlet. Four facies associations are used to describe and characterize these barrier island architectural elements. Barrier islands occur in association with backarrier fill and internally contain lower and upper shoreface, high-energy upper shoreface, and tidal channel facies. Barrier islands bound lagoons or estuaries, and are distinguished from other shoreface deposits by their internal facies and geometry, association with backbarrier facies, and position within transgressive successions. Tidal processes, in particular tidal inlet migration and reworking of the upper shoreface, also distinguish barrier island deposits. Existing barrier island models highlight the short term heterogeneous and dynamic nature of barrier island systems, yet overlook processes tied to geologic time scales, such as multi-directional motion, erosion, and reworking, and their expressions in preserved barrier island strata. This study uses characteristic outcrop expressions of barrier island successions to

On the role of spatial phase and phase correlation in vision, illusion, and cognition.

Science.gov (United States)

Gladilin, Evgeny; Eils, Roland

2015-01-01

Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large body of phenomenological evidence not much is known yet about the role of phase information in neural mechanisms of visual perception and cognition. Here, we are concerned with analysis of the role of spatial phase in computational and biological vision, emergence of visual illusions and pattern recognition. We hypothesize that fundamental importance of phase information for invariant retrieval of structural image features and motion detection promoted development of phase-based mechanisms of neural image processing in course of evolution of biological vision. Using an extension of Fourier phase correlation technique, we show that the core functions of visual system such as motion detection and pattern recognition can be facilitated by the same basic mechanism. Our analysis suggests that emergence of visual illusions can be attributed to presence of coherently phase-shifted repetitive patterns as well as the effects of acuity compensation by saccadic eye movements. We speculate that biological vision relies on perceptual mechanisms effectively similar to phase correlation, and predict neural features of visual pattern (dis)similarity that can be used for experimental validation of our hypothesis of "cognition by phase correlation."

On the role of spatial phase and phase correlation in vision, illusion and cognition

Directory of Open Access Journals (Sweden)

Evgeny eGladilin

2015-04-01

Full Text Available Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large body of phenomenological evidence not much is known yet about the role of phase information in neural mechanisms of visual perception and cognition. Here, we are concerned with analysis of the role of spatial phase in computational and biological vision, emergence of visual illusions and pattern recognition. We hypothesize that fundamental importance of phase information for invariant retrieval of structural image features and motion detection promoted development of phase-based mechanisms of neural image processing in course of evolution of biological vision. Using an extension of Fourier phase correlation technique, we show that the core functions of visual system such as motion detection and pattern recognition can be facilitated by the same basic mechanism. Our analysis suggests that emergence of visual illusions can be attributed to presence of coherently phase-shifted repetitive patterns as well as the effects of acuity compensation by saccadic eye movements. We speculate that biological vision relies on perceptual mechanisms effectively similar to phase correlation, and predict neural features of visual pattern (dissimilarity that can be used for experimental validation of our hypothesis of 'cognition by phase correlation'.

[A method of recognizing biology surface spectrum using cascade-connection artificial neural nets].

Science.gov (United States)

Shi, Wei-Jie; Yao, Yong; Zhang, Tie-Qiang; Meng, Xian-Jiang

2008-05-01

A method of recognizing the visible spectrum of micro-areas on the biological surface with cascade-connection artificial neural nets is presented in the present paper. The visible spectra of spots on apples' pericarp, ranging from 500 to 730 nm, were obtained with a fiber-probe spectrometer, and a new spectrum recognition system consisting of three-level cascade-connection neural nets was set up. The experiments show that the spectra of rotten, scar and bumped spot on an apple's pericarp can be recognized by the spectrum recognition system, and the recognition accuracy is higher than 85% even when noise level is 15%. The new recognition system overcomes the disadvantages of poor accuracy and poor anti-noise with the traditional system based on single cascade neural nets. Finally, a new method of expression of recognition results was proved. The method is based on the conception of degree of membership in fuzzing mathematics, and through it the recognition results can be expressed exactly and objectively.

Measurement of shoulder motion fraction and motion ratio

International Nuclear Information System (INIS)

Kang, Yeong Han

2006-01-01

This study was to understand about the measurement of shoulder motion fraction and motion ratio. We proposed the radiological criterior of glenohumeral and scapulothoracic movement ratio. We measured the motion fraction of the glenohumeral and scapulothoracic movement using CR (computed radiological system) of arm elevation at neutral, 90 degree, full elevation. Central ray was 15 .deg., 19 .deg., 22 .deg. to the cephald for the parallel scapular spine, and the tilting of torso was external oblique 40 .deg., 36 .deg., 22 .deg. for perpendicular to glenohumeral surface. Healthful donor of 100 was divided 5 groups by age (20, 30, 40, 50, 60). The angle of glenohumeral motion and scapulothoracic motion could be taken from gross arm angle and radiological arm angle. We acquired 3 images at neutral, 90 .deg. and full elevation position and measured radiographic angle of glenoheumeral, scapulothoracic movement respectively. While the arm elevation was 90 .deg., the shoulder motion fraction was 1.22 (M), 1.70 (W) in right arm and 1.31, 1.54 in left. In full elevation, Right arm fraction was 1.63, 1.84 and left was 1.57, 1.32. In right dominant arm (78%), 90 .deg. and Full motion fraction was 1.58, 1.43, in left (22%) 1.82, 1.94. In generation 20, 90 .deg. and Full motion fraction was 1.56, 1.52, 30' was 1.82, 1.43, 40' was 1.23, 1.16, 50' was 1.80, 1.28,60' was 1.24, 1.75. There was not significantly by gender, dominant arm and age. The criteria of motion fraction was useful reference for clinical diagnosis the shoulder instability

Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei

KAUST Repository

Wang, Jim Jing-Yan

2014-12-01

Trypanosma brucei (T. Brucei) is an important pathogen agent of African trypanosomiasis. The flagellum is an essential and multifunctional organelle of T. Brucei, thus it is very important to recognize the flagellar proteins from T. Brucei proteins for the purposes of both biological research and drug design. In this paper, we investigate computationally recognizing flagellar proteins in T. Brucei by pattern recognition methods. It is argued that an optimal decision function can be obtained as the difference of probability functions of flagella protein and the non-flagellar protein for the purpose of flagella protein recognition. We propose to learn a multi-kernel classification function to approximate this optimal decision function, by minimizing the information loss of such approximation which is measured by the Kull back-Leibler (KL) divergence. An iterative multi-kernel classifier learning algorithm is developed to minimize the KL divergence for the problem of T. Brucei flagella protein recognition, experiments show its advantage over other T. Brucei flagellar protein recognition and multi-kernel learning methods. © 2014 IEEE.

Markerless motion estimation for motion-compensated clinical brain imaging

Science.gov (United States)

Kyme, Andre Z.; Se, Stephen; Meikle, Steven R.; Fulton, Roger R.

2018-05-01

Motion-compensated brain imaging can dramatically reduce the artifacts and quantitative degradation associated with voluntary and involuntary subject head motion during positron emission tomography (PET), single photon emission computed tomography (SPECT) and computed tomography (CT). However, motion-compensated imaging protocols are not in widespread clinical use for these modalities. A key reason for this seems to be the lack of a practical motion tracking technology that allows for smooth and reliable integration of motion-compensated imaging protocols in the clinical setting. We seek to address this problem by investigating the feasibility of a highly versatile optical motion tracking method for PET, SPECT and CT geometries. The method requires no attached markers, relying exclusively on the detection and matching of distinctive facial features. We studied the accuracy of this method in 16 volunteers in a mock imaging scenario by comparing the estimated motion with an accurate marker-based method used in applications such as image guided surgery. A range of techniques to optimize performance of the method were also studied. Our results show that the markerless motion tracking method is highly accurate (brain imaging and holds good promise for a practical implementation in clinical PET, SPECT and CT systems.

War and Medicine in a Culture of Peace. 2. Synopsis of Biological Weapons

OpenAIRE

Pierard, Gérald

2001-01-01

Biological warfare has a long history. Despite the 1972 international convention and several attempts at biological weapon eradication, some countries and non governmental groups still retain some of these agents. According to their potential use, they belong to bioterrorism or to massive destruction weapons. Any biological warfare put the civilian medical and paramedical assets at the frontline and at high risk for being rapidly contaminated. The prompt recognition of a bioterrorist attack a...

Normal mode analysis and applications in biological physics.

Science.gov (United States)

Dykeman, Eric C; Sankey, Otto F

2010-10-27

Normal mode analysis has become a popular and often used theoretical tool in the study of functional motions in enzymes, viruses, and large protein assemblies. The use of normal modes in the study of these motions is often extremely fruitful since many of the functional motions of large proteins can be described using just a few normal modes which are intimately related to the overall structure of the protein. In this review, we present a broad overview of several popular methods used in the study of normal modes in biological physics including continuum elastic theory, the elastic network model, and a new all-atom method, recently developed, which is capable of computing a subset of the low frequency vibrational modes exactly. After a review of the various methods, we present several examples of applications of normal modes in the study of functional motions, with an emphasis on viral capsids.

PET motion correction using PRESTO with ITK motion estimation

Energy Technology Data Exchange (ETDEWEB)

Botelho, Melissa [Institute of Biophysics and Biomedical Engineering, Science Faculty of University of Lisbon (Portugal); Caldeira, Liliana; Scheins, Juergen [Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich (Germany); Matela, Nuno [Institute of Biophysics and Biomedical Engineering, Science Faculty of University of Lisbon (Portugal); Kops, Elena Rota; Shah, N Jon [Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich (Germany)

2014-07-29

The Siemens BrainPET scanner is a hybrid MRI/PET system. PET images are prone to motion artefacts which degrade the image quality. Therefore, motion correction is essential. The library PRESTO converts motion-corrected LORs into highly accurate generic projection data [1], providing high-resolution PET images. ITK is an open-source software used for registering multidimensional data []. ITK provides motion estimation necessary to PRESTO.

PET motion correction using PRESTO with ITK motion estimation

International Nuclear Information System (INIS)

Botelho, Melissa; Caldeira, Liliana; Scheins, Juergen; Matela, Nuno; Kops, Elena Rota; Shah, N Jon

2014-01-01

The Siemens BrainPET scanner is a hybrid MRI/PET system. PET images are prone to motion artefacts which degrade the image quality. Therefore, motion correction is essential. The library PRESTO converts motion-corrected LORs into highly accurate generic projection data [1], providing high-resolution PET images. ITK is an open-source software used for registering multidimensional data []. ITK provides motion estimation necessary to PRESTO.

Familiar Person Recognition: Is Autonoetic Consciousness More Likely to Accompany Face Recognition Than Voice Recognition?

Science.gov (United States)

Barsics, Catherine; Brédart, Serge

2010-11-01

Autonoetic consciousness is a fundamental property of human memory, enabling us to experience mental time travel, to recollect past events with a feeling of self-involvement, and to project ourselves in the future. Autonoetic consciousness is a characteristic of episodic memory. By contrast, awareness of the past associated with a mere feeling of familiarity or knowing relies on noetic consciousness, depending on semantic memory integrity. Present research was aimed at evaluating whether conscious recollection of episodic memories is more likely to occur following the recognition of a familiar face than following the recognition of a familiar voice. Recall of semantic information (biographical information) was also assessed. Previous studies that investigated the recall of biographical information following person recognition used faces and voices of famous people as stimuli. In this study, the participants were presented with personally familiar people's voices and faces, thus avoiding the presence of identity cues in the spoken extracts and allowing a stricter control of frequency exposure with both types of stimuli (voices and faces). In the present study, the rate of retrieved episodic memories, associated with autonoetic awareness, was significantly higher from familiar faces than familiar voices even though the level of overall recognition was similar for both these stimuli domains. The same pattern was observed regarding semantic information retrieval. These results and their implications for current Interactive Activation and Competition person recognition models are discussed.

Gesture Recognition and Sensorimotor Learning-by-Doing of Motor Skills in Manual Professions: A Case Study in the Wheel-Throwing Art of Pottery

Science.gov (United States)

Glushkova, Alina; Manitsaris, Sotiris

2018-01-01

This paper presents a methodological framework for the use of gesture recognition technologies in the learning/mastery of the gestural skills required in wheel-throwing pottery. In the case of self-instruction or training, learners face difficulties due to the absence of the teacher/expert and the consequent lack of guidance. Motion capture…

Marine biology

International Nuclear Information System (INIS)

Thurman, H.V.; Webber, H.H.

1984-01-01

This book discusses both taxonomic and ecological topics on marine biology. Full coverage of marine organisms of all five kingdoms is provided, along with interesting and thorough discussion of all major marine habitats. Organization into six major parts allows flexibility. It also provides insight into important topics such as disposal of nuclear waste at sea, the idea that life began on the ocean floor, and how whales, krill, and people interact. A full-color photo chapter reviews questions, and exercises. The contents are: an overview marine biology: fundamental concepts/investigating life in the ocean; the physical ocean, the ocean floor, the nature of water, the nature and motion of ocean water; general ecology, conditions for life in the sea, biological productivity and energy transfer; marine organisms; monera, protista, mycota and metaphyta; the smaller marine animals, the large animals marine habitats, the intertidal zone/benthos of the continental shelf, the photic zone, the deep ocean, the ocean under stress, marine pollution, appendix a: the metric system and conversion factors/ appendix b: prefixes and suffixes/ appendix c: taxonomic classification of common marine organisms, and glossary, and index

Motion control report

CERN Document Server

2013-01-01

Please note this is a short discount publication. In today's manufacturing environment, Motion Control plays a major role in virtually every project.The Motion Control Report provides a comprehensive overview of the technology of Motion Control:* Design Considerations* Technologies* Methods to Control Motion* Examples of Motion Control in Systems* A Detailed Vendors List

Uncovering the underlying physical mechanisms of biological systems via quantification of landscape and flux

International Nuclear Information System (INIS)

Xu Li; Chu Xiakun; Yan Zhiqiang; Zheng Xiliang; Zhang Kun; Zhang Feng; Yan Han; Wu Wei; Wang Jin

2016-01-01

In this review, we explore the physical mechanisms of biological processes such as protein folding and recognition, ligand binding, and systems biology, including cell cycle, stem cell, cancer, evolution, ecology, and neural networks. Our approach is based on the landscape and flux theory for nonequilibrium dynamical systems. This theory provides a unifying principle and foundation for investigating the underlying mechanisms and physical quantification of biological systems. (topical review)

Foundations for a syntatic pattern recognition system for genomic DNA sequences

Energy Technology Data Exchange (ETDEWEB)

Searles, D.B.

1993-03-01

The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model.

Directory of Open Access Journals (Sweden)

Yiliang Zeng

Full Text Available Due to the rapid development of motor vehicle Driver Assistance Systems (DAS, the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.

Omics/systems biology and cancer cachexia.

Science.gov (United States)

Gallagher, Iain J; Jacobi, Carsten; Tardif, Nicolas; Rooyackers, Olav; Fearon, Kenneth

2016-06-01

Cancer cachexia is a complex syndrome generated by interaction between the host and tumour cells with a background of treatment effects and toxicity. The complexity of the physiological pathways likely involved in cancer cachexia necessitates a holistic view of the relevant biology. Emergent properties are characteristic of complex systems with the result that the end result is more than the sum of its parts. Recognition of the importance of emergent properties in biology led to the concept of systems biology wherein a holistic approach is taken to the biology at hand. Systems biology approaches will therefore play an important role in work to uncover key mechanisms with therapeutic potential in cancer cachexia. The 'omics' technologies provide a global view of biological systems. Genomics, transcriptomics, proteomics, lipidomics and metabolomics approaches all have application in the study of cancer cachexia to generate systems level models of the behaviour of this syndrome. The current work reviews recent applications of these technologies to muscle atrophy in general and cancer cachexia in particular with a view to progress towards integration of these approaches to better understand the pathology and potential treatment pathways in cancer cachexia. Copyright © 2016. Published by Elsevier Ltd.

[Prosopagnosia and facial expression recognition].

Science.gov (United States)

Koyama, Shinichi

2014-04-01

This paper reviews clinical neuropsychological studies that have indicated that the recognition of a person's identity and the recognition of facial expressions are processed by different cortical and subcortical areas of the brain. The fusiform gyrus, especially the right fusiform gyrus, plays an important role in the recognition of identity. The superior temporal sulcus, amygdala, and medial frontal cortex play important roles in facial-expression recognition. Both facial recognition and facial-expression recognition are highly intellectual processes that involve several regions of the brain.

GOCI Level-2 Processing Improvements and Cloud Motion Analysis

Science.gov (United States)

Robinson, Wayne D.

2015-01-01

The Ocean Biology Processing Group has been working with the Korean Institute of Ocean Science and Technology (KIOST) to process geosynchronous ocean color data from the GOCI (Geostationary Ocean Color Instrument) aboard the COMS (Communications, Ocean and Meteorological Satellite). The level-2 processing program, l2gen has GOCI processing as an option. Improvements made to that processing are discussed here as well as a discussion about cloud motion effects.

Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

Science.gov (United States)

Schafer, Phillip B; Jin, Dezhe Z

2014-03-01

Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

Problems of allometric scaling analysis : Examples from mammalian reproductive biology

NARCIS (Netherlands)

Martin, RD; Genoud, M; Hemelrijk, CK

Biological scaling analyses employing the widely used bivariate allometric model are beset by at least four interacting problems: (1) choice of an appropriate best-fit line with due attention to the influence of outliers; (2) objective recognition of divergent subsets in the data (allometric

Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty.

Science.gov (United States)

Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G

2016-09-01

Optimally discriminating familiar from novel stimuli demands a decision-making process informed by prior expectations. Here we demonstrate that pupillary dilation (PD) responses during recognition memory decisions are modulated by expectations, and more specifically, that pupil dilation increases for unexpected compared to expected recognition. Furthermore, multi-level modeling demonstrated that the time course of the dilation during each individual trial contains separable early and late dilation components, with the early amplitude capturing unexpected recognition, and the later trailing slope reflecting general judgment uncertainty or effort. This is the first demonstration that the early dilation response during recognition is dependent upon observer expectations and that separate recognition expectation and judgment uncertainty components are present in the dilation time course of every trial. The findings provide novel insights into adaptive memory-linked orienting mechanisms as well as the general cognitive underpinnings of the pupillary index of autonomic nervous system activity. Copyright © 2016 Elsevier B.V. All rights reserved.

Recognition of Indian Sign Language in Live Video

Science.gov (United States)

Singha, Joyeeta; Das, Karen

2013-05-01

Sign Language Recognition has emerged as one of the important area of research in Computer Vision. The difficulty faced by the researchers is that the instances of signs vary with both motion and appearance. Thus, in this paper a novel approach for recognizing various alphabets of Indian Sign Language is proposed where continuous video sequences of the signs have been considered. The proposed system comprises of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, histogram matching. Eigen values and Eigen Vectors were considered for feature extraction stage and finally Eigen value weighted Euclidean distance is used to recognize the sign. It deals with bare hands, thus allowing the user to interact with the system in natural way. We have considered 24 different alphabets in the video sequences and attained a success rate of 96.25%.