A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

Science.gov (United States)

Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

Probabilistic active recognition of multiple objects using Hough-based geometric matching features

CSIR Research Space (South Africa)

Govender, N

2015-01-01

Full Text Available be recognized simultaneously, and occlusion and clutter (through distracter objects) is common. We propose a representation for object viewpoints using Hough transform based geometric matching features, which are robust in such circumstances. We show how...

Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

CAO Yungang

2016-10-01

Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

Directory of Open Access Journals (Sweden)

Chunlan Yang

2017-04-01

Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Correlativity study on MRI morphologic features, pathology, and molecular biology of breast cancer

International Nuclear Information System (INIS)

Chen Rong; Gong Shuigen; Zhang Weiguo; Chen Jinhua; He Shuangwu; Liu Baohua; Li Zengpeng

2004-01-01

Objective: To investigate the correlation among MRI morphologic features, pathology, and molecular biology of breast cancer. Methods: MR scanning was performed in 78 patients with breast cancer before operation and MRI morphologic features of breast cancer were analyzed. The mastectomy specimens of the breast neoplasm were stained with immunohistochemistry, and the expression of estrogen receptor (ER), progesterone receptor (PR), C-erbB-2, p53, and the distribution of microvessel density (MVD) was measured. The pathologic results were compared with MRI features. Results: Among the 80 breast cancers, ER positive expression was positively correlated with the spiculate margin of breast cancer (P 0.05). Among the 41 breast cancers with dynamic MR scans, there was positive correlation between the spatial distribution of contrast agent and MVD (P<0.01). Conclusion: There exists some correlation among MRI morphologic features, pathology, and molecular biology factors in breast cancer to certain extent. The biologic behavior and prognosis of the breast cancer can be assessed according to MRI features

A neural network model of semantic memory linking feature-based object representation and words.

Science.gov (United States)

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

Morphological self-organizing feature map neural network with applications to automatic target recognition

Science.gov (United States)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Object-based classification of global undersea topography and geomorphological features from the SRTM30_PLUS data

Science.gov (United States)

Dekavalla, Maria; Argialas, Demetre

2017-07-01

The analysis of undersea topography and geomorphological features provides necessary information to related disciplines and many applications. The development of an automated knowledge-based classification approach of undersea topography and geomorphological features is challenging due to their multi-scale nature. The aim of the study is to develop and evaluate an automated knowledge-based OBIA approach to: i) decompose the global undersea topography to multi-scale regions of distinct morphometric properties, and ii) assign the derived regions to characteristic geomorphological features. First, the global undersea topography was decomposed through the SRTM30_PLUS bathymetry data to the so-called morphometric objects of discrete morphometric properties and spatial scales defined by data-driven methods (local variance graphs and nested means) and multi-scale analysis. The derived morphometric objects were combined with additional relative topographic position information computed with a self-adaptive pattern recognition method (geomorphons), and auxiliary data and were assigned to characteristic undersea geomorphological feature classes through a knowledge base, developed from standard definitions. The decomposition of the SRTM30_PLUS data to morphometric objects was considered successful for the requirements of maximizing intra-object and inter-object heterogeneity, based on the near zero values of the Moran's I and the low values of the weighted variance index. The knowledge-based classification approach was tested for its transferability in six case studies of various tectonic settings and achieved the efficient extraction of 11 undersea geomorphological feature classes. The classification results for the six case studies were compared with the digital global seafloor geomorphic features map (GSFM). The 11 undersea feature classes and their producer's accuracies in respect to the GSFM relevant areas were Basin (95%), Continental Shelf (94.9%), Trough (88

Morphological features in children with autism

NARCIS (Netherlands)

Özgen, Mihriban Heval

2008-01-01

The central research aim in the present thesis was to extend the insight in several aspects of the role of the morphological features in autism. Clinical morphology might be used as a biomarker for ASD to reveal insight into the complexity of the disorder. In Chapter 1 current terminology and

Feature confirmation in object perception: Feature integration theory 26 years on from the Treisman Bartlett lecture.

Science.gov (United States)

Humphreys, Glyn W

2016-10-01

The Treisman Bartlett lecture, reported in the Quarterly Journal of Experimental Psychology in 1988, provided a major overview of the feature integration theory of attention. This has continued to be a dominant account of human visual attention to this day. The current paper provides a summary of the work reported in the lecture and an update on critical aspects of the theory as applied to visual object perception. The paper highlights the emergence of findings that pose significant challenges to the theory and which suggest that revisions are required that allow for (a) several rather than a single form of feature integration, (b) some forms of feature integration to operate preattentively, (c) stored knowledge about single objects and interactions between objects to modulate perceptual integration, (d) the application of feature-based inhibition to object files where visual features are specified, which generates feature-based spreading suppression and scene segmentation, and (e) a role for attention in feature confirmation rather than feature integration in visual selection. A feature confirmation account of attention in object perception is outlined.

Retrospective cues based on object features improve visual working memory performance in older adults.

Science.gov (United States)

Gilchrist, Amanda L; Duarte, Audrey; Verhaeghen, Paul

2016-01-01

Research with younger adults has shown that retrospective cues can be used to orient top-down attention toward relevant items in working memory. We examined whether older adults could take advantage of these cues to improve memory performance. Younger and older adults were presented with visual arrays of five colored shapes; during maintenance, participants were presented either with an informative cue based on an object feature (here, object shape or color) that would be probed, or with an uninformative, neutral cue. Although older adults were less accurate overall, both age groups benefited from the presentation of an informative, feature-based cue relative to a neutral cue. Surprisingly, we also observed differences in the effectiveness of shape versus color cues and their effects upon post-cue memory load. These results suggest that older adults can use top-down attention to remove irrelevant items from visual working memory, provided that task-relevant features function as cues.

FEATUREOUS: AN INTEGRATED ENVIRONMENT FOR FEATURE-CENTRIC ANALYSIS AND MODIFICATION OF OBJECT-ORIENTED SOFTWARE

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2011-01-01

The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand the implementations of user-observable program features and their respective interdependencies. As feature-centric program understanding and modification are essential during...... software maintenance and evolution, this situation needs to change. In this paper, we present Featureous, an integrated development environment built on top of the NetBeans IDE that facilitates feature-centric analysis of object-oriented software. Our integrated development environment encompasses...... a lightweight feature location mechanism, a number of reusable analytical views, and necessary APIs for supporting future extensions. The base of the integrated development environment is a conceptual framework comprising of three complementary dimensions of comprehension: perspective, abstraction...

Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight.

Science.gov (United States)

Guo, Siqiu; Zhang, Tao; Song, Yulong; Qian, Feng

2018-04-23

This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.

Age, environment, object recognition and morphological diversity of GFAP-immunolabeled astrocytes.

Science.gov (United States)

Diniz, Daniel Guerreiro; de Oliveira, Marcus Augusto; de Lima, Camila Mendes; Fôro, César Augusto Raiol; Sosthenes, Marcia Consentino Kronka; Bento-Torres, João; da Costa Vasconcelos, Pedro Fernando; Anthony, Daniel Clive; Diniz, Cristovam Wanderley Picanço

2016-10-10

Few studies have explored the glial response to a standard environment and how the response may be associated with age-related cognitive decline in learning and memory. Here we investigated aging and environmental influences on hippocampal-dependent tasks and on the morphology of an unbiased selected population of astrocytes from the molecular layer of dentate gyrus, which is the main target of perforant pathway. Six and twenty-month-old female, albino Swiss mice were housed, from weaning, in a standard or enriched environment, including running wheels for exercise and tested for object recognition and contextual memories. Young adult and aged subjects, independent of environment, were able to distinguish familiar from novel objects. All experimental groups, except aged mice from standard environment, distinguish stationary from displaced objects. Young adult but not aged mice, independent of environment, were able to distinguish older from recent objects. Only young mice from an enriched environment were able to distinguish novel from familiar contexts. Unbiased selected astrocytes from the molecular layer of the dentate gyrus were reconstructed in three-dimensions and classified using hierarchical cluster analysis of bimodal or multimodal morphological features. We found two morphological phenotypes of astrocytes and we designated type I the astrocytes that exhibited significantly higher values of morphological complexity as compared with type II. Complexity = [Sum of the terminal orders + Number of terminals] × [Total branch length/Number of primary branches]. On average, type I morphological complexity seems to be much more sensitive to age and environmental influences than that of type II. Indeed, aging and environmental impoverishment interact and reduce the morphological complexity of type I astrocytes at a point that they could not be distinguished anymore from type II. We suggest these two types of astrocytes may have different physiological roles

Overt attention in natural scenes: objects dominate features.

Science.gov (United States)

Stoll, Josef; Thrun, Michael; Nuthmann, Antje; Einhäuser, Wolfgang

2015-02-01

Whether overt attention in natural scenes is guided by object content or by low-level stimulus features has become a matter of intense debate. Experimental evidence seemed to indicate that once object locations in a scene are known, salience models provide little extra explanatory power. This approach has recently been criticized for using inadequate models of early salience; and indeed, state-of-the-art salience models outperform trivial object-based models that assume a uniform distribution of fixations on objects. Here we propose to use object-based models that take a preferred viewing location (PVL) close to the centre of objects into account. In experiment 1, we demonstrate that, when including this comparably subtle modification, object-based models again are at par with state-of-the-art salience models in predicting fixations in natural scenes. One possible interpretation of these results is that objects rather than early salience dominate attentional guidance. In this view, early-salience models predict fixations through the correlation of their features with object locations. To test this hypothesis directly, in two additional experiments we reduced low-level salience in image areas of high object content. For these modified stimuli, the object-based model predicted fixations significantly better than early salience. This finding held in an object-naming task (experiment 2) and a free-viewing task (experiment 3). These results provide further evidence for object-based fixation selection--and by inference object-based attentional guidance--in natural scenes. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Geomorphological change detection using object-based feature extraction from multi-temporal LIDAR data

NARCIS (Netherlands)

Seijmonsbergen, A.C.; Anders, N.S.; Bouten, W.; Feitosa, R.Q.; da Costa, G.A.O.P.; de Almeida, C.M.; Fonseca, L.M.G.; Kux, H.J.H.

2012-01-01

Multi-temporal LiDAR DTMs are used for the development and testing of a method for geomorphological change analysis in western Austria. Our test area is located on a mountain slope in the Gargellen Valley in western Austria. Six geomorphological features were mapped by using stratified Object-Based

Conjunctive Coding of Complex Object Features

Science.gov (United States)

Erez, Jonathan; Cusack, Rhodri; Kendall, William; Barense, Morgan D.

2016-01-01

Critical to perceiving an object is the ability to bind its constituent features into a cohesive representation, yet the manner by which the visual system integrates object features to yield a unified percept remains unknown. Here, we present a novel application of multivoxel pattern analysis of neuroimaging data that allows a direct investigation of whether neural representations integrate object features into a whole that is different from the sum of its parts. We found that patterns of activity throughout the ventral visual stream (VVS), extending anteriorly into the perirhinal cortex (PRC), discriminated between the same features combined into different objects. Despite this sensitivity to the unique conjunctions of features comprising objects, activity in regions of the VVS, again extending into the PRC, was invariant to the viewpoints from which the conjunctions were presented. These results suggest that the manner in which our visual system processes complex objects depends on the explicit coding of the conjunctions of features comprising them. PMID:25921583

SU-D-207B-04: Morphological Features of MRI as a Correlate of Capsular Contracture in Breast Cancer Patients with Implant-Based Reconstructions

International Nuclear Information System (INIS)

Tyagi, N; Sutton, E; Hunt, M; Apte, A; Zhang, J; Oh, J; Mechalakos, J; Mehrara, B; Matros, E; Ho, A

2016-01-01

Purpose: Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. The goal of this study was to identify image-based correlates of CC using MRI imaging in breast cancer patients who received both MRI and clinical evaluation following reconstructive surgery. Methods: We analyzed a retrospective dataset of 50 patients who had both a diagnostic MR and a plastic surgeon’s evaluations of CC score (Baker’s score) within a six month period following mastectomy and reconstructive surgery. T2w sagittal MRIs (TR/TE = 3500/102 ms, slice thickness = 4 mm) were used for morphological shape features (roundness, eccentricity, solidity, extent and ratio-length) and histogram features (median, skewness and kurtosis) of the implant and the pectoralis muscle overlying the implant. Implant and pectoralis muscles were segmented in 3D using Computation Environment for Radiological Research (CERR) and shape and histogram features were calculated as a function of Baker’s score. Results: Shape features such as roundness and eccentricity were statistically significant in differentiating grade 1 and grade 2 (p = 0.009; p = 0.06) as well as grade 1 and grade 3 CC (p = 0.001; p = 0.006). Solidity and extent were statistically significant in differentiating grade 1 and grade 3 CC (p = 0.04; p = 0.04). Ratio-length was statistically significant in differentiating all grades of CC except grade 2 and grade 3 that showed borderline significance (p = 0.06). The muscle thickness, median intensity and kurtosis were significant in differentiating between grade 1 and grade 3 (p = 0.02), grade 1 and grade 2 (p = 0.03) and grade 1 and grade 3 (p = 0.01) respectively. Conclusion: Morphological shape features described on MR images were associated with the severity of CC. MRI may be important in objectively evaluating outcomes in breast cancer patients who undergo implant reconstruction.

[The morphological features of skin wounds inflicted by joinery hand saws designed for different types of sawing].

Science.gov (United States)

Sarkisian, B A; Azarov, P A

2014-01-01

The objective of the present work was to study the morphological features of skin wounds inflicted by joinery hand saws designed for longitudinal, transverse, and mixed sawing. A total of 60 injuries to the thigh skin inflicted by the recurring and reciprocating saw movements were simulated. The hand saws had 5 mm high "sharp" and "blunt"-tipped teeth. The analysis of the morphological features of the wounds revealed differences in their length and depth, shape of edge cuts and defects, and the relief of the walls depending on the sawtooth sharpness and the mode of sawing. It is concluded that morphological features of the wounds may be used to determine the type of the saw, the sharpness of its teeth, the direction and frequency of its movements.

Knowledge-based object recognition for different morphological classes of plants

Science.gov (United States)

Brendel, Thorsten; Schwanke, Joerg; Jensch, Peter F.; Megnet, Roland

1995-01-01

Micropropagation of plants is done by cutting juvenile plants and placing them into special container-boxes with nutrient-solution where the pieces can grow up and be cut again several times. To produce high amounts of biomass it is necessary to do plant micropropagation by a robotic syshoot. In this paper we describe parts of the vision syshoot that recognizes plants and their particular cutting points. Therefore, it is necessary to extract elements of the plants and relations between these elements (for example root, shoot, leaf). Different species vary in their morphological appearance, variation is also immanent in plants of the same species. Therefore, we introduce several morphological classes of plants from that we expect same recognition methods. As a result of our work we present rules which help users to create specific algorithms for object recognition of plant species.

Featureous: infrastructure for feature-centric analysis of object-oriented software

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2010-01-01

The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand how user-observable program features are implemented and how their implementations relate to each other. It is worthwhile to improve this situation, since feature-centric program...... understanding and modification are essential during software evolution and maintenance. In this paper, we present an infrastructure built on top of the NetBeans IDE called Featureous that allows for rapid construction of tools for feature-centric analysis of object-oriented software. Our infrastructure...... encompasses a lightweight feature location mechanism, a number of analytical views and an API allowing for addition of third-party extensions. To form a common conceptual framework for future feature-centric extensions, we propose to structure feature centric analysis along three dimensions: perspective...

Blind source separation based on time-frequency morphological characteristics for rigid acoustic scattering by underwater objects

Science.gov (United States)

Yang, Yang; Li, Xiukun

2016-06-01

Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation (BSS) can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution (WVD) observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. An experimental simulation has been used, with changes in the pulse width of the transmitted signal, the relative amplitude and the time delay parameter, in order to analyzing the feasibility of this new method. Simulation results show that the new method is not only able to separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects.

A foreground object features-based stereoscopic image visual comfort assessment model

Science.gov (United States)

Jin, Xin; Jiang, G.; Ying, H.; Yu, M.; Ding, S.; Peng, Z.; Shao, F.

2014-11-01

Since stereoscopic images provide observers with both realistic and discomfort viewing experience, it is necessary to investigate the determinants of visual discomfort. By considering that foreground object draws most attention when human observing stereoscopic images. This paper proposes a new foreground object based visual comfort assessment (VCA) metric. In the first place, a suitable segmentation method is applied to disparity map and then the foreground object is ascertained as the one having the biggest average disparity. In the second place, three visual features being average disparity, average width and spatial complexity of foreground object are computed from the perspective of visual attention. Nevertheless, object's width and complexity do not consistently influence the perception of visual comfort in comparison with disparity. In accordance with this psychological phenomenon, we divide the whole images into four categories on the basis of different disparity and width, and exert four different models to more precisely predict its visual comfort in the third place. Experimental results show that the proposed VCA metric outperformance other existing metrics and can achieve a high consistency between objective and subjective visual comfort scores. The Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are over 0.84 and 0.82, respectively.

Real-time object tracking based on scale-invariant features employing bio-inspired hardware.

Science.gov (United States)

Yasukawa, Shinsuke; Okuno, Hirotsugu; Ishii, Kazuo; Yagi, Tetsuya

2016-09-01

We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video. Copyright © 2016 Elsevier Ltd. All rights reserved.

Feature-Oriented Programming with Object Algebras

NARCIS (Netherlands)

B.C.d.S. Oliveira (Bruno); T. van der Storm (Tijs); A. Loh; W.R. Cook

2013-01-01

htmlabstractObject algebras are a new programming technique that enables a simple solution to basic extensibility and modularity issues in programming languages. While object algebras excel at defining modular features, the composition mechanisms for object algebras (and features) are still

Virtual Surveyor based Object Extraction from Airborne LiDAR data

Science.gov (United States)

Habib, Md. Ahsan

Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.

Retrospective Cues Based on Object Features Improve Visual Working Memory Performance in Older Adults

OpenAIRE

Gilchrist, Amanda L.; Duarte, Audrey; Verhaeghen, Paul

2015-01-01

Research with younger adults has shown that retrospective cues can be used to orient top-down attention toward relevant items in working memory. We examined whether older adults could take advantage of these cues to improve memory performance. Younger and older adults were presented with visual arrays of five colored shapes; during maintenance, participants were either presented with an informative cue based on an object feature (here, object shape or color) that would be probed, or with an u...

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

Science.gov (United States)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments

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Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke

2017-01-01

Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity. Here, we extend these findings by replicating them for a rich set of object images, comparing performance across layers within two DNNs of different depths, and examining how the DNNs’ performance compares to that of non-computational “conceptual” models. Human observers performed similarity judgments for a set of 92 images of real-world objects. Representations of the same images were obtained in each of the layers of two DNNs of different depths (8-layer AlexNet and 16-layer VGG-16). To create conceptual models, other human observers generated visual-feature labels (e.g., “eye”) and category labels (e.g., “animal”) for the same image set. Feature labels were divided into parts, colors, textures and contours, while category labels were divided into subordinate, basic, and superordinate categories. We fitted models derived from the features, categories, and from each layer of each DNN to the similarity judgments, using representational similarity analysis to evaluate model performance. In both DNNs, similarity within the last layer explains most of the explainable variance in human similarity judgments. The last layer outperforms almost all feature-based models. Late and mid-level layers outperform some but not all feature-based models. Importantly, categorical models predict similarity judgments significantly better than any DNN layer. Our results provide further evidence for commonalities between DNNs and brain representations. Models derived from visual features

What are the visual features underlying rapid object recognition?

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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.

Object-based attention: strength of object representation and attentional guidance.

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Shomstein, Sarah; Behrmann, Marlene

2008-01-01

Two or more features belonging to a single object are identified more quickly and more accurately than are features belonging to different objects--a finding attributed to sensory enhancement of all features belonging to an attended or selected object. However, several recent studies have suggested that this "single-object advantage" may be a product of probabilistic and configural strategic prioritizations rather than of object-based perceptual enhancement per se, challenging the underlying mechanism that is thought to give rise to object-based attention. In the present article, we further explore constraints on the mechanisms of object-based selection by examining the contribution of the strength of object representations to the single-object advantage. We manipulated factors such as exposure duration (i.e., preview time) and salience of configuration (i.e., objects). Varying preview time changes the magnitude of the object-based effect, so that if there is ample time to establish an object representation (i.e., preview time of 1,000 msec), then both probability and configuration (i.e., objects) guide attentional selection. If, however, insufficient time is provided to establish a robust object-based representation, then only probabilities guide attentional selection. Interestingly, at a short preview time of 200 msec, when the two objects were sufficiently different from each other (i.e., different colors), both configuration and probability guided attention selection. These results suggest that object-based effects can be explained both in terms of strength of object representations (established at longer exposure durations and by pictorial cues) and probabilistic contingencies in the visual environment.

Coding of visual object features and feature conjunctions in the human brain.

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Martinovic, Jasna; Gruber, Thomas; Müller, Matthias M

2008-01-01

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object's features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process--while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200-400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.

Object-based Morphological Building Index for Building Extraction from High Resolution Remote Sensing Imagery

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LIN Xiangguo

2017-06-01

Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. In this article, an object-based morphological building index (OBMBI is constructed based on both image segmentation and graph-based top-hat reconstruction, and OBMBI is used for building extraction from high resolution remote sensing images. First, bidirectional mapping relationship between pixels, objects and graph-nodes are constructed. Second, the OBMBI image is built based on both graph-based top-hat reconstruction and the above mapping relationship. Third, a binary thresholding is performed on the OBMBI image, and the binary image is converted into vector format to derive the building polygons. Finally, the post-processing is made to optimize the extracted building polygons. Two images, including an aerial image and a panchromatic satellite image, are used to test both the proposed method and classic PanTex method. The experimental results suggest that our proposed method has a higher accuracy in building extraction than the classic PanTex method. On average, the correctness, the completeness and the quality of our method are respectively 9.49%, 11.26% and 14.11% better than those of the PanTex.

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

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Xin Wang

2018-02-01

Full Text Available To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

Morphological features of indigenous chicken ecotype populations of Kenya

NARCIS (Netherlands)

Ngeno, K.; Waaij, van der E.H.; Kahi, A.K.; Arendonk, van J.A.M.

2014-01-01

This study characterized indigenous chicken (IC) ecotypes morphologically. Five IC ecotypes studied were Kakamega (KK), Siaya (BN), West Pokot (WP), Narok (NR) and Bomet (BM). Data on morphological features were collected from 1 580 chickens and 151 for zoometric measurements. Descriptive

Internal attention to features in visual short-term memory guides object learning.

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Fan, Judith E; Turk-Browne, Nicholas B

2013-11-01

Attending to objects in the world affects how we perceive and remember them. What are the consequences of attending to an object in mind? In particular, how does reporting the features of a recently seen object guide visual learning? In three experiments, observers were presented with abstract shapes in a particular color, orientation, and location. After viewing each object, observers were cued to report one feature from visual short-term memory (VSTM). In a subsequent test, observers were cued to report features of the same objects from visual long-term memory (VLTM). We tested whether reporting a feature from VSTM: (1) enhances VLTM for just that feature (practice-benefit hypothesis), (2) enhances VLTM for all features (object-based hypothesis), or (3) simultaneously enhances VLTM for that feature and suppresses VLTM for unreported features (feature-competition hypothesis). The results provided support for the feature-competition hypothesis, whereby the representation of an object in VLTM was biased towards features reported from VSTM and away from unreported features (Experiment 1). This bias could not be explained by the amount of sensory exposure or response learning (Experiment 2) and was amplified by the reporting of multiple features (Experiment 3). Taken together, these results suggest that selective internal attention induces competitive dynamics among features during visual learning, flexibly tuning object representations to align with prior mnemonic goals. Copyright © 2013 Elsevier B.V. All rights reserved.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

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Zhao, Baojun; Zhao, Boya; Tang, Linbo; Han, Yuqi; Wang, Wenzheng

2018-03-04

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

Object detection based on improved color and scale invariant features

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Chen, Mengyang; Men, Aidong; Fan, Peng; Yang, Bo

2009-10-01

A novel object detection method which combines color and scale invariant features is presented in this paper. The detection system mainly adopts the widely used framework of SIFT (Scale Invariant Feature Transform), which consists of both a keypoint detector and descriptor. Although SIFT has some impressive advantages, it is not only computationally expensive, but also vulnerable to color images. To overcome these drawbacks, we employ the local color kernel histograms and Haar Wavelet Responses to enhance the descriptor's distinctiveness and computational efficiency. Extensive experimental evaluations show that the method has better robustness and lower computation costs.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

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Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

Science.gov (United States)

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

[Origin and morphological features of small supernumerary marker chromosomes in Turner syndrome].

Science.gov (United States)

Liu, Nan; Tong, Tong; Chen, Yue; Chen, Yanling; Cai, Chunquan

2018-02-10

OBJECTIVE To explore the origin and morphological features of small supernumerary marker chromosomes (sSMCs) in Turner syndrome. METHODS For 5 cases of Turner syndrome with a sSMC identified by conventional G-banding, dual-color fluorescence in situ hybridization (FISH) was applied to explore their origin and morphological features. RESULTS Among the 5 cases, 3 have derived from the X chromosome, which included 2 ring chromosomes and 1 centric minute. For the 2 sSMCs derived from the Y chromosome, 1 was ring or isodicentric chromosome, while the other was an isodicentric chromosome. CONCLUSION The sSMCs found in Turner syndrome have almost all derived from sex chromosomes. The majority of sSMCs derived from the X chromosome will form ring chromosomes, while a minority will form centric minute. While most sSMC derived from Y chromosome may exist as isodicentric chromosomes, and a small number may exist as rings. For Turner syndrome patients with sSMCs, dual-color FISH may be used to delineate their origins to facilitate genetic counseling and selection of clinical regime.

Determination of morphological features and molecular interactions ...

African Journals Online (AJOL)

This research focused on identifying the morphological features and molecular interactions of the Nigerian Bentonitic clays using Scanning Electron Microscope (SEM) characterisation technique. The SEM microstructure images indicated that the bentonite samples are generally moderately dispersive to dispersive with ...

Incidence and morphological features of thyroid papillary microcarcinoma in Graves’ disease

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Kovačević Božidar

2017-01-01

Full Text Available Introduction/Objective. Association of Graves’ disease (GD and thyroid cancer is reported in a wide range from 0% to 33.7%. Papillary thyroid carcinoma (PTC is the most commonly diagnosed malignancy in GD, namely its variant – papillary thyroid microcarcinoma (PTMC. The increasingly frequent PTMC disclose favorable biological behavior with low mortality and recurrence rates. The aim of this work is to report our experience on the frequency and morphological features of PTMC in surgically treated patients with GD. Methods. Over a period of three years, total or near-total thyroidectomy was performed in 129 patients with GD. Results. Incidental PTMC was diagnosed in 24 (18.7% patients with GD. The mean tumor diameter was 3.03 ± 2.17 mm. The average age of patients in the GD with PTMC group was 48.50 ± 13.07 years, while in the GD without PTMC group it was 41 ± 13.12 years, and it proved to be statistically significant ( p = 0.045. Most of the PTMC were unifocal (83%, and the most common morphological features of PTMC were intraparenchymal localization (62.5%, follicular morphology (66.7%, and infiltrative growth pattern (62.5%. Extrathyroidal extension, lymphatic invasion and multifocality of PTMC were more commonly related with subcapsular localized PTMC. The presence of at least one nodule in the GD with PTMC group was 58.3%, while in the GD without PTMC group it was 26.7%, and it was statistically significant (p = 0.003. Conclusion. Our results showed a high incidence of PTMC (18.7% in patients with GD. Clinically, the most important morphological characteristics of PTMC were related with its subcapsular localization.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection

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Baojun Zhao

2018-03-01

Full Text Available With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP.

Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells.

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Hiroto Sasaki

Full Text Available Precise quantification of cellular potential of stem cells, such as human bone marrow-derived mesenchymal stem cells (hBMSCs, is important for achieving stable and effective outcomes in clinical stem cell therapy. Here, we report a method for image-based prediction of the multiple differentiation potentials of hBMSCs. This method has four major advantages: (1 the cells used for potential prediction are fully intact, and therefore directly usable for clinical applications; (2 predictions of potentials are generated before differentiation cultures are initiated; (3 prediction of multiple potentials can be provided simultaneously for each sample; and (4 predictions of potentials yield quantitative values that correlate strongly with the experimental data. Our results show that the collapse of hBMSC differentiation potentials, triggered by in vitro expansion, can be quantitatively predicted far in advance by predicting multiple potentials, multi-lineage differentiation potentials (osteogenic, adipogenic, and chondrogenic and population doubling potential using morphological features apparent during the first 4 days of expansion culture. In order to understand how such morphological features can be effective for advance predictions, we measured gene-expression profiles of the same early undifferentiated cells. Both senescence-related genes (p16 and p21 and cytoskeleton-related genes (PTK2, CD146, and CD49 already correlated to the decrease of potentials at this stage. To objectively compare the performance of morphology and gene expression for such early prediction, we tested a range of models using various combinations of features. Such comparison of predictive performances revealed that morphological features performed better overall than gene-expression profiles, balancing the predictive accuracy with the effort required for model construction. This benchmark list of various prediction models not only identifies the best morphological feature

Morphological features in the Bay of Bengal

Digital Repository Service at National Institute of Oceanography (India)

Sarma, K.V.L.N.S.; Ramana, M.V.; Subrahmanyam, V.; Krishna, K.S.; Ramprasad, T.; Desa, M.

history of the Fan. After India's soft collision with the Eurasian plate, these events may have played a critical role in shaping various morphological features since late Eocene in the Bay of Bengal. The present 12 kHz Echo sounder data collected along...

Selection of morphological features of pollen grains for chosen tree taxa

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Agnieszka Kubik-Komar

2018-05-01

Full Text Available The basis of aerobiological studies is to monitor airborne pollen concentrations and pollen season timing. This task is performed by appropriately trained staff and is difficult and time consuming. The goal of this research is to select morphological characteristics of grains that are the most discriminative for distinguishing between birch, hazel and alder taxa and are easy to determine automatically from microscope images. This selection is based on the split attributes of the J4.8 classification trees built for different subsets of features. Determining the discriminative features by this method, we provide specific rules for distinguishing between individual taxa, at the same time obtaining a high percentage of correct classification. The most discriminative among the 13 morphological characteristics studied are the following: number of pores, maximum axis, minimum axis, axes difference, maximum oncus width, and number of lateral pores. The classification result of the tree based on this subset is better than the one built on the whole feature set and it is almost 94%. Therefore, selection of attributes before tree building is recommended. The classification results for the features easiest to obtain from the image, i.e. maximum axis, minimum axis, axes difference, and number of lateral pores, are only 2.09 pp lower than those obtained for the complete set, but 3.23 pp lower than the results obtained for the selected most discriminating attributes only.

Feature-fused SSD: fast detection for small objects

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Cao, Guimei; Xie, Xuemei; Yang, Wenzhe; Liao, Quan; Shi, Guangming; Wu, Jinjian

2018-04-01

Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.

Histological image classification using biologically interpretable shape-based features

International Nuclear Information System (INIS)

Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

2013-01-01

Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

Object-based target templates guide attention during visual search.

Science.gov (United States)

Berggren, Nick; Eimer, Martin

2018-05-03

During visual search, attention is believed to be controlled in a strictly feature-based fashion, without any guidance by object-based target representations. To challenge this received view, we measured electrophysiological markers of attentional selection (N2pc component) and working memory (sustained posterior contralateral negativity; SPCN) in search tasks where two possible targets were defined by feature conjunctions (e.g., blue circles and green squares). Critically, some search displays also contained nontargets with two target features (incorrect conjunction objects, e.g., blue squares). Because feature-based guidance cannot distinguish these objects from targets, any selective bias for targets will reflect object-based attentional control. In Experiment 1, where search displays always contained only one object with target-matching features, targets and incorrect conjunction objects elicited identical N2pc and SPCN components, demonstrating that attentional guidance was entirely feature-based. In Experiment 2, where targets and incorrect conjunction objects could appear in the same display, clear evidence for object-based attentional control was found. The target N2pc became larger than the N2pc to incorrect conjunction objects from 250 ms poststimulus, and only targets elicited SPCN components. This demonstrates that after an initial feature-based guidance phase, object-based templates are activated when they are required to distinguish target and nontarget objects. These templates modulate visual processing and control access to working memory, and their activation may coincide with the start of feature integration processes. Results also suggest that while multiple feature templates can be activated concurrently, only a single object-based target template can guide attention at any given time. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Surface feature congruency effects in the object-reviewing paradigm are dependent on task memory demands.

Science.gov (United States)

Kimchi, Ruth; Pirkner, Yossef

2014-08-01

Perception of object continuity depends on establishing correspondence between objects viewed across disruptions in visual information. The role of spatiotemporal information in guiding object continuity is well documented; the role of surface features, however, is controversial. Some researchers have shown an object-specific preview benefit (OSPB)-a standard index of object continuity-only when correspondence could be based on an object's spatiotemporal information, whereas others have found color-based OSPB, suggesting that surface features can also guide object continuity. This study shows that surface feature-based OSPB is dependent on the task memory demands. When the task involved letters and matching just one target letter to the preview ones, no color congruency effect was found under spatiotemporal discontinuity and spatiotemporal ambiguity (Experiments 1-3), indicating that the absence of feature-based OSPB cannot be accounted for by salient spatiotemporal discontinuity. When the task involved complex shapes and matching two target shapes to the preview ones, color-based OSPB was obtained. Critically, however, when a visual working memory task was performed concurrently with the matching task, the presence of a nonspatial (but not a spatial) working memory load eliminated the color-based OSPB (Experiments 4 and 5). These results suggest that the surface feature congruency effects that are observed in the object-reviewing paradigm (with the matching task) reflect memory-based strategies that participants use to solve a memory-demanding task; therefore, they are not reliable measures of online object continuity and cannot be taken as evidence for the role of surface features in establishing object correspondence.

A FRAMEWORK OF CHANGE DETECTION BASED ON COMBINED MORPHOLOGICA FEATURES AND MULTI-INDEX CLASSIFICATION

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S. Li

2017-09-01

Full Text Available Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI, the differential water index (NDWI are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

Science.gov (United States)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

Involuntary top-down control by search-irrelevant features: Visual working memory biases attention in an object-based manner.

Science.gov (United States)

Foerster, Rebecca M; Schneider, Werner X

2018-03-01

Many everyday tasks involve successive visual-search episodes with changing targets. Converging evidence suggests that these targets are retained in visual working memory (VWM) and bias attention from there. It is unknown whether all or only search-relevant features of a VWM template bias attention during search. Bias signals might be configured exclusively to task-relevant features so that only search-relevant features bias attention. Alternatively, VWM might maintain objects in the form of bound features. Then, all template features will bias attention in an object-based manner, so that biasing effects are ranked by feature relevance. Here, we investigated whether search-irrelevant VWM template features bias attention. Participants had to saccade to a target opposite a distractor. A colored cue depicted the target prior to each search trial. The target was predefined only by its identity, while its color was irrelevant. When target and cue matched not only in identity (search-relevant) but also in color (search-irrelevant), saccades went more often and faster directly to the target than without any color match (Experiment 1). When introducing a cue-distractor color match (Experiment 2), direct target saccades were most likely when target and cue matched in the search-irrelevant color and least likely in case of a cue-distractor color match. When cue and target were never colored the same (Experiment 3), cue-colored distractors still captured the eyes more often than different-colored distractors despite color being search-irrelevant. As participants were informed about the misleading color, the result argues against a strategical and voluntary usage of color. Instead, search-irrelevant features biased attention obligatorily arguing for involuntary top-down control by object-based VWM templates. Copyright © 2017 Elsevier B.V. All rights reserved.

Morphological features of leaves in the genus Echinacea Moench under introduction

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Valentyna O. Menshova

2015-05-01

Full Text Available The comparative morphological characteristics of leaves of the genus Echinacea representatives (E. pallida (Nutt. Nutt and E. tennesseensis (Beadle Small introduced in the O.V. Fomin Botanical Garden are given. The established morphological features allow determining the adaptive capacities of species of the genus Echinacea ex situ. Despite of taxonomical belonging, the leaves of basal formation are most developed. Studied features could be applied during the implementation of these plants in pharmaceutical industry. Also these features could be useful during further investigations of adaptive possibilities of Echinacea species in ex situ conditions.

Voting based object boundary reconstruction

Science.gov (United States)

Tian, Qi; Zhang, Like; Ma, Jingsheng

2005-07-01

A voting-based object boundary reconstruction approach is proposed in this paper. Morphological technique was adopted in many applications for video object extraction to reconstruct the missing pixels. However, when the missing areas become large, the morphological processing cannot bring us good results. Recently, Tensor voting has attracted people"s attention, and it can be used for boundary estimation on curves or irregular trajectories. However, the complexity of saliency tensor creation limits its applications in real-time systems. An alternative approach based on tensor voting is introduced in this paper. Rather than creating saliency tensors, we use a "2-pass" method for orientation estimation. For the first pass, Sobel d*etector is applied on a coarse boundary image to get the gradient map. In the second pass, each pixel puts decreasing weights based on its gradient information, and the direction with maximum weights sum is selected as the correct orientation of the pixel. After the orientation map is obtained, pixels begin linking edges or intersections along their direction. The approach is applied to various video surveillance clips under different conditions, and the experimental results demonstrate significant improvement on the final extracted objects accuracy.

Automated Feature Extraction of Foredune Morphology from Terrestrial Lidar Data

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Spore, N.; Brodie, K. L.; Swann, C.

2014-12-01

Foredune morphology is often described in storm impact prediction models using the elevation of the dune crest and dune toe and compared with maximum runup elevations to categorize the storm impact and predicted responses. However, these parameters do not account for other foredune features that may make them more or less erodible, such as alongshore variations in morphology, vegetation coverage, or compaction. The goal of this work is to identify other descriptive features that can be extracted from terrestrial lidar data that may affect the rate of dune erosion under wave attack. Daily, mobile-terrestrial lidar surveys were conducted during a 6-day nor'easter (Hs = 4 m in 6 m water depth) along 20km of coastline near Duck, North Carolina which encompassed a variety of foredune forms in close proximity to each other. This abstract will focus on the tools developed for the automated extraction of the morphological features from terrestrial lidar data, while the response of the dune will be presented by Brodie and Spore as an accompanying abstract. Raw point cloud data can be dense and is often under-utilized due to time and personnel constraints required for analysis, since many algorithms are not fully automated. In our approach, the point cloud is first projected into a local coordinate system aligned with the coastline, and then bare earth points are interpolated onto a rectilinear 0.5 m grid creating a high resolution digital elevation model. The surface is analyzed by identifying features along each cross-shore transect. Surface curvature is used to identify the position of the dune toe, and then beach and berm morphology is extracted shoreward of the dune toe, and foredune morphology is extracted landward of the dune toe. Changes in, and magnitudes of, cross-shore slope, curvature, and surface roughness are used to describe the foredune face and each cross-shore transect is then classified using its pre-storm morphology for storm-response analysis.

Three dimensional pattern recognition using feature-based indexing and rule-based search

Science.gov (United States)

Lee, Jae-Kyu

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells. This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene. Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage. Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size

Robust object tracking combining color and scale invariant features

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Zhang, Shengping; Yao, Hongxun; Gao, Peipei

2010-07-01

Object tracking plays a very important role in many computer vision applications. However its performance will significantly deteriorate due to some challenges in complex scene, such as pose and illumination changes, clustering background and so on. In this paper, we propose a robust object tracking algorithm which exploits both global color and local scale invariant (SIFT) features in a particle filter framework. Due to the expensive computation cost of SIFT features, the proposed tracker adopts a speed-up variation of SIFT, SURF, to extract local features. Specially, the proposed method first finds matching points between the target model and target candidate, than the weight of the corresponding particle based on scale invariant features is computed as the the proportion of matching points of that particle to matching points of all particles, finally the weight of the particle is obtained by combining weights of color and SURF features with a probabilistic way. The experimental results on a variety of challenging videos verify that the proposed method is robust to pose and illumination changes and is significantly superior to the standard particle filter tracker and the mean shift tracker.

An Integrated Account of Generalization across Objects and Features

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Kemp, Charles; Shafto, Patrick; Tenenbaum, Joshua B.

2012-01-01

Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single…

Automatic processing of unattended object features by functional connectivity

Directory of Open Access Journals (Sweden)

Katja Martina Mayer

2013-05-01

Full Text Available Observers can selectively attend to object features that are relevant for a task. However, unattended task-irrelevant features may still be processed and possibly integrated with the attended features. This study investigated the neural mechanisms for processing both task-relevant (attended and task-irrelevant (unattended object features. The Garner paradigm was adapted for functional magnetic resonance imaging (fMRI to test whether specific brain areas process the conjunction of features or whether multiple interacting areas are involved in this form of feature integration. Observers attended to shape, colour, or non-rigid motion of novel objects while unattended features changed from trial to trial (change blocks or remained constant (no-change blocks during a given block. This block manipulation allowed us to measure the extent to which unattended features affected neural responses which would reflect the extent to which multiple object features are automatically processed. We did not find Garner interference at the behavioural level. However, we designed the experiment to equate performance across block types so that any fMRI results could not be due solely to differences in task difficulty between change and no-change blocks. Attention to specific features localised several areas known to be involved in object processing. No area showed larger responses on change blocks compared to no-change blocks. However, psychophysiological interaction analyses revealed that several functionally-localised areas showed significant positive interactions with areas in occipito-temporal and frontal areas that depended on block type. Overall, these findings suggest that both regional responses and functional connectivity are crucial for processing multi-featured objects.

Acoustic features of objects matched by an echolocating bottlenose dolphin.

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Delong, Caroline M; Au, Whitlow W L; Lemonds, David W; Harley, Heidi E; Roitblat, Herbert L

2006-03-01

The focus of this study was to investigate how dolphins use acoustic features in returning echolocation signals to discriminate among objects. An echolocating dolphin performed a match-to-sample task with objects that varied in size, shape, material, and texture. After the task was completed, the features of the object echoes were measured (e.g., target strength, peak frequency). The dolphin's error patterns were examined in conjunction with the between-object variation in acoustic features to identify the acoustic features that the dolphin used to discriminate among the objects. The present study explored two hypotheses regarding the way dolphins use acoustic information in echoes: (1) use of a single feature, or (2) use of a linear combination of multiple features. The results suggested that dolphins do not use a single feature across all object sets or a linear combination of six echo features. Five features appeared to be important to the dolphin on four or more sets: the echo spectrum shape, the pattern of changes in target strength and number of highlights as a function of object orientation, and peak and center frequency. These data suggest that dolphins use multiple features and integrate information across echoes from a range of object orientations.

Identifying relevant feature-action associations for grasping unmodelled objects

DEFF Research Database (Denmark)

Thomsen, Mikkel Tang; Kraft, Dirk; Krüger, Norbert

2015-01-01

content. The method is provided with a large and structured set of visual features, motivated by the visual hierarchy in primates and finds relevant feature action associations automatically. We apply our method in a simulated environment on three different object sets for the case of grasp affordance...... learning. For box objects, we achieve a 0.90 success probability, 0.80 for round objects and up to 0.75 for open objects, when presented with novel objects. In this work, we in particular demonstrate the effect of choosing appropriate feature representations. We demonstrate a significant performance...

Storage of features, conjunctions and objects in visual working memory.

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Vogel, E K; Woodman, G F; Luck, S J

2001-02-01

Working memory can be divided into separate subsystems for verbal and visual information. Although the verbal system has been well characterized, the storage capacity of visual working memory has not yet been established for simple features or for conjunctions of features. The authors demonstrate that it is possible to retain information about only 3-4 colors or orientations in visual working memory at one time. Observers are also able to retain both the color and the orientation of 3-4 objects, indicating that visual working memory stores integrated objects rather than individual features. Indeed, objects defined by a conjunction of four features can be retained in working memory just as well as single-feature objects, allowing many individual features to be retained when distributed across a small number of objects. Thus, the capacity of visual working memory must be understood in terms of integrated objects rather than individual features.

Joint Tensor Feature Analysis For Visual Object Recognition.

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Wong, Wai Keung; Lai, Zhihui; Xu, Yong; Wen, Jiajun; Ho, Chu Po

2015-11-01

Tensor-based object recognition has been widely studied in the past several years. This paper focuses on the issue of joint feature selection from the tensor data and proposes a novel method called joint tensor feature analysis (JTFA) for tensor feature extraction and recognition. In order to obtain a set of jointly sparse projections for tensor feature extraction, we define the modified within-class tensor scatter value and the modified between-class tensor scatter value for regression. The k-mode optimization technique and the L(2,1)-norm jointly sparse regression are combined together to compute the optimal solutions. The convergent analysis, computational complexity analysis and the essence of the proposed method/model are also presented. It is interesting to show that the proposed method is very similar to singular value decomposition on the scatter matrix but with sparsity constraint on the right singular value matrix or eigen-decomposition on the scatter matrix with sparse manner. Experimental results on some tensor datasets indicate that JTFA outperforms some well-known tensor feature extraction and selection algorithms.

A configural effect in visual short-term memory for features from different parts of an object.

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Delvenne, Jean-François; Bruyer, Raymond

2006-09-01

Previous studies have shown that change detection performance is improved when the visual display holds features (e.g., a colour and an orientation) that are grouped into different parts of the same object compared to when they are all spatially separated (Xu, 2002a, 2002b). These findings indicate that visual short-term memory (VSTM) encoding can be "object based". Recently, however, it has been demonstrated that changing the orientation of an item could affect the spatial configuration of the display (Jiang, Chun, & Olson, 2004), which may have an important influence on change detection. The perceptual grouping of features into an object obviously reduces the amount of distinct spatial relations in a display and hence the complexity of the spatial configuration. In the present study, we ask whether the object-based encoding benefit observed in previous studies may reflect the use of configural coding rather than the outcome of a true object-based effect. The results show that when configural cues are removed, the object-based encoding benefit remains for features (i.e., colour and orientation) from different parts of an object, but is significantly reduced. These findings support the view that memory for features from different parts of an object can benefit from object-based encoding, but the use of configural coding significantly helps enlarge this effect.

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

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Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

A study of gross morphological and histological syringeal features of ...

African Journals Online (AJOL)

A study of gross morphological and histological syringeal features of true francolins (Galliformes: Francolinus, Scleroptila, Peliperdix and Dendroperdix spp.) and spurfowls ( Pternistis spp.) in a phylogenetic context.

Curvature histogram features for retrieval of images of smooth 3D objects

International Nuclear Information System (INIS)

Zhdanov, I; Scherbakov, O; Potapov, A; Peterson, M

2014-01-01

We consider image features on the base of histograms of oriented gradients (HOG) with addition of contour curvature histogram (HOG-CH), and also compare it with results of known scale-invariant feature transform (SIFT) approach in application to retrieval of images of smooth 3D objects.

A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.

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Saito, Akira; Numata, Yasushi; Hamada, Takuya; Horisawa, Tomoyoshi; Cosatto, Eric; Graf, Hans-Peter; Kuroda, Masahiko; Yamamoto, Yoichiro

2016-01-01

Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image

Infants' Developing Sensitivity to Object Function: Attention to Features and Feature Correlations

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Baumgartner, Heidi A.; Oakes, Lisa M.

2011-01-01

When learning object function, infants must detect relations among features--for example, that squeezing is associated with squeaking or that objects with wheels roll. Previously, Perone and Oakes (2006) found 10-month-old infants were sensitive to relations between object appearances and actions, but not to relations between appearances and…

Morphological features of the neonatal brain support development of subsequent cognitive, language, and motor abilities.

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Spann, Marisa N; Bansal, Ravi; Rosen, Tove S; Peterson, Bradley S

2014-09-01

Knowledge of the role of brain maturation in the development of cognitive abilities derives primarily from studies of school-age children to adults. Little is known about the morphological features of the neonatal brain that support the subsequent development of abilities in early childhood, when maturation of the brain and these abilities are the most dynamic. The goal of our study was to determine whether brain morphology during the neonatal period supports early cognitive development through 2 years of age. We correlated morphological features of the cerebral surface assessed using deformation-based measures (surface distances) of high-resolution MRI scans for 33 healthy neonates, scanned between the first to sixth week of postmenstrual life, with subsequent measures of their motor, language, and cognitive abilities at ages 6, 12, 18, and 24 months. We found that morphological features of the cerebral surface of the frontal, mesial prefrontal, temporal, and occipital regions correlated with subsequent motor scores, posterior parietal regions correlated with subsequent language scores, and temporal and occipital regions correlated with subsequent cognitive scores. Measures of the anterior and middle portions of the cingulate gyrus correlated with scores across all three domains of ability. Most of the significant findings were inverse correlations located bilaterally in the brain. The inverse correlations may suggest either that a more protracted morphological maturation or smaller local volumes of neonatal brain tissue supports better performance on measures of subsequent motor, language, and cognitive abilities throughout the first 2 years of postnatal life. The correlations of morphological measures of the cingulate with measures of performance across all domains of ability suggest that the cingulate supports a broad range of skills in infancy and early childhood, similar to its functions in older children and adults. Copyright © 2014 Wiley Periodicals, Inc.

Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects.

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Taylor, Kirsten I; Devereux, Barry J; Acres, Kadia; Randall, Billi; Tyler, Lorraine K

2012-03-01

Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system. Copyright © 2011 Elsevier B.V. All rights reserved.

Learning object location predictors with boosting and grammar-guided feature extraction

Energy Technology Data Exchange (ETDEWEB)

Eads, Damian Ryan [Los Alamos National Laboratory; Rosten, Edward [UNIV OF CAMBRIDGE; Helmbold, David [UC/SANTA CRUZ

2009-01-01

The authors present BEAMER: a new spatially exploitative approach to learning object detectors which shows excellent results when applied to the task of detecting objects in greyscale aerial imagery in the presence of ambiguous and noisy data. There are four main contributions used to produce these results. First, they introduce a grammar-guided feature extraction system, enabling the exploration of a richer feature space while constraining the features to a useful subset. This is specified with a rule-based generative grammer crafted by a human expert. Second, they learn a classifier on this data using a newly proposed variant of AdaBoost which takes into account the spatially correlated nature of the data. Third, they perform another round of training to optimize the method of converting the pixel classifications generated by boosting into a high quality set of (x,y) locations. lastly, they carefully define three common problems in object detection and define two evaluation criteria that are tightly matched to these problems. Major strengths of this approach are: (1) a way of randomly searching a broad feature space, (2) its performance when evaluated on well-matched evaluation criteria, and (3) its use of the location prediction domain to learn object detectors as well as to generate detections that perform well on several tasks: object counting, tracking, and target detection. They demonstrate the efficacy of BEAMER with a comprehensive experimental evaluation on a challenging data set.

Bindings in working memory: The role of object-based attention.

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Gao, Zaifeng; Wu, Fan; Qiu, Fangfang; He, Kaifeng; Yang, Yue; Shen, Mowei

2017-02-01

Over the past decade, it has been debated whether retaining bindings in working memory (WM) requires more attention than retaining constituent features, focusing on domain-general attention and space-based attention. Recently, we proposed that retaining bindings in WM needs more object-based attention than retaining constituent features (Shen, Huang, & Gao, 2015, Journal of Experimental Psychology: Human Perception and Performance, doi: 10.1037/xhp0000018 ). However, only unitized visual bindings were examined; to establish the role of object-based attention in retaining bindings in WM, more emperical evidence is required. We tested 4 new bindings that had been suggested requiring no more attention than the constituent features in the WM maintenance phase: The two constituent features of binding were stored in different WM modules (cross-module binding, Experiment 1), from auditory and visual modalities (cross-modal binding, Experiment 2), or temporally (cross-time binding, Experiments 3) or spatially (cross-space binding, Experiments 4-6) separated. In the critical condition, we added a secondary object feature-report task during the delay interval of the change-detection task, such that the secondary task competed for object-based attention with the to-be-memorized stimuli. If more object-based attention is required for retaining bindings than for retaining constituent features, the secondary task should impair the binding performance to a larger degree relative to the performance of constituent features. Indeed, Experiments 1-6 consistently revealed a significantly larger impairment for bindings than for the constituent features, suggesting that object-based attention plays a pivotal role in retaining bindings in WM.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

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Wang, Gang; Wang, Yalin

2017-02-15

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Adaptive Colour Feature Identification in Image for Object Tracking

Directory of Open Access Journals (Sweden)

Feng Su

2012-01-01

Full Text Available Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

Improved Object Proposals with Geometrical Features for Autonomous Driving

Directory of Open Access Journals (Sweden)

Yiliu Feng

2017-01-01

Full Text Available This paper aims at generating high-quality object proposals for object detection in autonomous driving. Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene. We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods. In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework. Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly. Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes.

An object-based visual attention model for robotic applications.

Science.gov (United States)

Yu, Yuanlong; Mann, George K I; Gosine, Raymond G

2010-10-01

By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.

Combining heterogenous features for 3D hand-held object recognition

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Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Integrative and distinctive coding of visual and conceptual object features in the ventral visual stream.

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Martin, Chris B; Douglas, Danielle; Newsome, Rachel N; Man, Louisa Ly; Barense, Morgan D

2018-02-02

A significant body of research in cognitive neuroscience is aimed at understanding how object concepts are represented in the human brain. However, it remains unknown whether and where the visual and abstract conceptual features that define an object concept are integrated. We addressed this issue by comparing the neural pattern similarities among object-evoked fMRI responses with behavior-based models that independently captured the visual and conceptual similarities among these stimuli. Our results revealed evidence for distinctive coding of visual features in lateral occipital cortex, and conceptual features in the temporal pole and parahippocampal cortex. By contrast, we found evidence for integrative coding of visual and conceptual object features in perirhinal cortex. The neuroanatomical specificity of this effect was highlighted by results from a searchlight analysis. Taken together, our findings suggest that perirhinal cortex uniquely supports the representation of fully specified object concepts through the integration of their visual and conceptual features. © 2018, Martin et al.

Integrative and distinctive coding of visual and conceptual object features in the ventral visual stream

Science.gov (United States)

Douglas, Danielle; Newsome, Rachel N; Man, Louisa LY

2018-01-01

A significant body of research in cognitive neuroscience is aimed at understanding how object concepts are represented in the human brain. However, it remains unknown whether and where the visual and abstract conceptual features that define an object concept are integrated. We addressed this issue by comparing the neural pattern similarities among object-evoked fMRI responses with behavior-based models that independently captured the visual and conceptual similarities among these stimuli. Our results revealed evidence for distinctive coding of visual features in lateral occipital cortex, and conceptual features in the temporal pole and parahippocampal cortex. By contrast, we found evidence for integrative coding of visual and conceptual object features in perirhinal cortex. The neuroanatomical specificity of this effect was highlighted by results from a searchlight analysis. Taken together, our findings suggest that perirhinal cortex uniquely supports the representation of fully specified object concepts through the integration of their visual and conceptual features. PMID:29393853

Assessment of the stability of morphological ECG features and their potential for person verification/identification

Directory of Open Access Journals (Sweden)

Matveev Mikhail

2017-01-01

Full Text Available This study investigates the potential of a set of ECG morphological features for person verification/identification. The measurements are done over 145 pairs of ECG recordings from healthy subjects, acquired 5 years apart (T1, T2 = T1+5 years. Time, amplitude, area and slope descriptors of the QRS-T pattern are analysed in 4 ECG leads, forming quasi-orthogonal lead system (II&III, V1, V5. The correspondence between feature values in T1 and T2 is verified via factor analysis by principal components extraction method; correlation analysis applied over the measurements in T1 and T2; synthesis of regression equations for prediction of features’ values in T2 based on T1 measurements; and cluster analysis for assessment of the correspondence between measured and predicted feature values. Thus, 11 amplitude descriptors of the QRS complex are highlighted as stable, i.e. keeping their strong correlation (≥0.7 within a certain factor, weak correlation (<0.3 with the features from the remaining factors and presenting high correlation in the two measurement periods that is a sign for their person verification/identification potential. The observed coincidence between feature values measured in T2 and predicted via the designed regression models (r=0.93 suggests about the confidence of person identification via the proposed morphological features.

[The peculiar morphological features of the imprints of straight and wavy head hair dirtied with blood].

Science.gov (United States)

Leonova, E N; Nagornov, M N; Prokhorenko, A S

2018-01-01

The objective of the present study was to elucidate the specific morphological features of the imprints of blood-soaked straight and wavy head hair. The contact imprints of straight and wavy head hair dirtied with blood were obtained experimentally. The imprints of straight hair were shown to exhibit the elements in the form of the rectilinear and bow-shaped slightly bent stripes. The imprints of wavy hair were shaped as the arches, waves, circles, and a large number of various small elements, such as dashes and commas.

Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

Directory of Open Access Journals (Sweden)

Lei Shi

2018-01-01

Full Text Available In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA and tabu search (TS is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy.

Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

Science.gov (United States)

Shi, Lei; Wan, Youchuan; Gao, Xianjun

2018-01-01

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. PMID:29581721

Determination of feature generation methods for PTZ camera object tracking

Science.gov (United States)

Doyle, Daniel D.; Black, Jonathan T.

2012-06-01

Object detection and tracking using computer vision (CV) techniques have been widely applied to sensor fusion applications. Many papers continue to be written that speed up performance and increase learning of artificially intelligent systems through improved algorithms, workload distribution, and information fusion. Military application of real-time tracking systems is becoming more and more complex with an ever increasing need of fusion and CV techniques to actively track and control dynamic systems. Examples include the use of metrology systems for tracking and measuring micro air vehicles (MAVs) and autonomous navigation systems for controlling MAVs. This paper seeks to contribute to the determination of select tracking algorithms that best track a moving object using a pan/tilt/zoom (PTZ) camera applicable to both of the examples presented. The select feature generation algorithms compared in this paper are the trained Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the Mixture of Gaussians (MoG) background subtraction method, the Lucas- Kanade optical flow method (2000) and the Farneback optical flow method (2003). The matching algorithm used in this paper for the trained feature generation algorithms is the Fast Library for Approximate Nearest Neighbors (FLANN). The BSD licensed OpenCV library is used extensively to demonstrate the viability of each algorithm and its performance. Initial testing is performed on a sequence of images using a stationary camera. Further testing is performed on a sequence of images such that the PTZ camera is moving in order to capture the moving object. Comparisons are made based upon accuracy, speed and memory.

A model based on feature objects aided strategy to evaluate the methane generation from food waste by anaerobic digestion.

Science.gov (United States)

Yu, Meijuan; Zhao, Mingxing; Huang, Zhenxing; Xi, Kezhong; Shi, Wansheng; Ruan, Wenquan

2018-02-01

A model based on feature objects (FOs) aided strategy was used to evaluate the methane generation from food waste by anaerobic digestion. The kinetics of feature objects was tested by the modified Gompertz model and the first-order kinetic model, and the first-order kinetic hydrolysis constants were used to estimate the reaction rate of homemade and actual food waste. The results showed that the methane yields of four feature objects were significantly different. The anaerobic digestion of homemade food waste and actual food waste had various methane yields and kinetic constants due to the different contents of FOs in food waste. Combining the kinetic equations with the multiple linear regression equation could well express the methane yield of food waste, as the R 2 of food waste was more than 0.9. The predictive methane yields of the two actual food waste were 528.22 mL g -1  TS and 545.29 mL g -1  TS with the model, while the experimental values were 527.47 mL g -1  TS and 522.1 mL g -1  TS, respectively. The relative error between the experimental cumulative methane yields and the predicted cumulative methane yields were both less than 5%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Non-sky polarization-based dehazing algorithm for non-specular objects using polarization difference and global scene feature.

Science.gov (United States)

Qu, Yufu; Zou, Zhaofan

2017-10-16

Photographic images taken in foggy or hazy weather (hazy images) exhibit poor visibility and detail because of scattering and attenuation of light caused by suspended particles, and therefore, image dehazing has attracted considerable research attention. The current polarization-based dehazing algorithms strongly rely on the presence of a "sky area", and thus, the selection of model parameters is susceptible to external interference of high-brightness objects and strong light sources. In addition, the noise of the restored image is large. In order to solve these problems, we propose a polarization-based dehazing algorithm that does not rely on the sky area ("non-sky"). First, a linear polarizer is used to collect three polarized images. The maximum- and minimum-intensity images are then obtained by calculation, assuming the polarization of light emanating from objects is negligible in most scenarios involving non-specular objects. Subsequently, the polarization difference of the two images is used to determine a sky area and calculate the infinite atmospheric light value. Next, using the global features of the image, and based on the assumption that the airlight and object radiance are irrelevant, the degree of polarization of the airlight (DPA) is calculated by solving for the optimal solution of the correlation coefficient equation between airlight and object radiance; the optimal solution is obtained by setting the right-hand side of the equation to zero. Then, the hazy image is subjected to dehazing. Subsequently, a filtering denoising algorithm, which combines the polarization difference information and block-matching and 3D (BM3D) filtering, is designed to filter the image smoothly. Our experimental results show that the proposed polarization-based dehazing algorithm does not depend on whether the image includes a sky area and does not require complex models. Moreover, the dehazing image except specular object scenarios is superior to those obtained by Tarel

Morphological features of the species of the genus Chlamydomonas s.l. (Chlorophyta from various molecular clades

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Maria N. Pavlovska

2012-03-01

Full Text Available The morphology of 78 authentic strains from 5 clades into culture condition was investigated. The complex of phenotype features was established. Such features as: type of mucilage and their origin, mucilage collapse under methylene blue, saving papilla and stigma in not motile stage, extracellular matrix formation inside cell wall, the way of sporangium break, pyrenoid and stigma habit before cell division, cell shape, chloroplast morphology. Diagnostic features for determination of taxa on clades level are discussed.

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

Science.gov (United States)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures

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Yun Ren

2018-01-01

Full Text Available Modern object detectors always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors. The deeper and wider convolutional architectures are adopted as the feature extractor at present. However, many notable object detection systems such as Fast/Faster RCNN only consider simple fully connected layers as the feature classifier. In this paper, we declare that it is beneficial for the detection performance to elaboratively design deep convolutional networks (ConvNets of various depths for feature classification, especially using the fully convolutional architectures. In addition, this paper also demonstrates how to employ the fully convolutional architectures in the Fast/Faster RCNN. Experimental results show that a classifier based on convolutional layer is more effective for object detection than that based on fully connected layer and that the better detection performance can be achieved by employing deeper ConvNets as the feature classifier.

Tracking Location and Features of Objects within Visual Working Memory

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Michael Patterson

2012-10-01

Full Text Available Four studies examined how color or shape features can be accessed to retrieve the memory of an object's location. In each trial, 6 colored dots (Experiments 1 and 2 or 6 black shapes (Experiments 3 and 4 were displayed in randomly selected locations for 1.5 s. An auditory cue for either the shape or the color to-be-remembered was presented either simultaneously, immediately, or 2 s later. Non-informative cues appeared in some trials to serve as a control condition. After a 4 s delay, 5/6 objects were re-presented, and participants indicated the location of the missing object either by moving the mouse (Experiments 1 and 3, or by typing coordinates using a grid (Experiments 2 and 4. Compared to the control condition, cues presented simultaneously or immediately after stimuli improved location accuracy in all experiments. However, cues presented after 2 s only improved accuracy in Experiment 1. These results suggest that location information may not be addressable within visual working memory using shape features. In Experiment 1, but not Experiments 2–4, cues significantly improved accuracy when they indicated the missing object could be any of the three identical objects. In Experiments 2–4, location accuracy was highly impaired when the missing object came from a group of identical rather than uniquely identifiable objects. This indicates that when items with similar features are presented, location accuracy may be reduced. In summary, both feature type and response mode can influence the accuracy and accessibility of visual working memory for object location.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

Science.gov (United States)

Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

2015-07-01

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exploiting Higher Order and Multi-modal Features for 3D Object Detection

DEFF Research Database (Denmark)

Kiforenko, Lilita

that describe object visual appearance such as shape, colour, texture etc. This thesis focuses on robust object detection and pose estimation of rigid objects using 3D information. The thesis main contributions are novel feature descriptors together with object detection and pose estimation algorithms....... The initial work introduces a feature descriptor that uses edge categorisation in combination with a local multi-modal histogram descriptor in order to detect objects with little or no texture or surface variation. The comparison is performed with a state-of-the-art method, which is outperformed...... of the methods work well for one type of objects in a specific scenario, in another scenario or with different objects they might fail, therefore more robust solutions are required. The typical problem solution is the design of robust feature descriptors, where feature descriptors contain information...

Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells.

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Fumiko Matsuoka

Full Text Available Human bone marrow mesenchymal stem cells (hBMSCs are widely used cell source for clinical bone regeneration. Achieving the greatest therapeutic effect is dependent on the osteogenic differentiation potential of the stem cells to be implanted. However, there are still no practical methods to characterize such potential non-invasively or previously. Monitoring cellular morphology is a practical and non-invasive approach for evaluating osteogenic potential. Unfortunately, such image-based approaches had been historically qualitative and requiring experienced interpretation. By combining the non-invasive attributes of microscopy with the latest technology allowing higher throughput and quantitative imaging metrics, we studied the applicability of morphometric features to quantitatively predict cellular osteogenic potential. We applied computational machine learning, combining cell morphology features with their corresponding biochemical osteogenic assay results, to develop prediction model of osteogenic differentiation. Using a dataset of 9,990 images automatically acquired by BioStation CT during osteogenic differentiation culture of hBMSCs, 666 morphometric features were extracted as parameters. Two commonly used osteogenic markers, alkaline phosphatase (ALP activity and calcium deposition were measured experimentally, and used as the true biological differentiation status to validate the prediction accuracy. Using time-course morphological features throughout differentiation culture, the prediction results highly correlated with the experimentally defined differentiation marker values (R>0.89 for both marker predictions. The clinical applicability of our morphology-based prediction was further examined with two scenarios: one using only historical cell images and the other using both historical images together with the patient's own cell images to predict a new patient's cellular potential. The prediction accuracy was found to be greatly enhanced

Morphological operation based dense houses extraction from DSM

Science.gov (United States)

Li, Y.; Zhu, L.; Tachibana, K.; Shimamura, H.

2014-08-01

This paper presents a method of reshaping and extraction of markers and masks of the dense houses from the DSM based on mathematical morphology (MM). Houses in a digital surface model (DSM) are almost joined together in high-density housing areas, and most segmentation methods cannot completely separate them. We propose to label the markers of the buildings firstly and segment them into masks by watershed then. To avoid detecting more than one marker for a house or no marker at all due to its higher neighbour, the DSM is morphologically reshaped. It is carried out by a MM operation using the certain disk shape SE of the similar size to the houses. The sizes of the houses need to be estimated before reshaping. A granulometry generated by opening-by-reconstruction to the NDSM is proposed to detect the scales of the off-terrain objects. It is a histogram of the global volume of the top hats of the convex objects in the continuous scales. The obvious step change in the profile means that there are many objects of similar sizes occur at this scale. In reshaping procedure, the slices of the object are derived by morphological filtering at the detected continuous scales and reconstructed in pile as the dome. The markers are detected on the basis of the domes.

Human listening studies reveal insights into object features extracted by echolocating dolphins

Science.gov (United States)

Delong, Caroline M.; Au, Whitlow W. L.; Roitblat, Herbert L.

2004-05-01

Echolocating dolphins extract object feature information from the acoustic parameters of object echoes. However, little is known about which object features are salient to dolphins or how they extract those features. To gain insight into how dolphins might be extracting feature information, human listeners were presented with echoes from objects used in a dolphin echoic-visual cross-modal matching task. Human participants performed a task similar to the one the dolphin had performed; however, echoic samples consisting of 23-echo trains were presented via headphones. The participants listened to the echoic sample and then visually selected the correct object from among three alternatives. The participants performed as well as or better than the dolphin (M=88.0% correct), and reported using a combination of acoustic cues to extract object features (e.g., loudness, pitch, timbre). Participants frequently reported using the pattern of aural changes in the echoes across the echo train to identify the shape and structure of the objects (e.g., peaks in loudness or pitch). It is likely that dolphins also attend to the pattern of changes across echoes as objects are echolocated from different angles.

Average combination difference morphological filters for fault feature extraction of bearing

Science.gov (United States)

Lv, Jingxiang; Yu, Jianbo

2018-02-01

In order to extract impulse components from vibration signals with much noise and harmonics, a new morphological filter called average combination difference morphological filter (ACDIF) is proposed in this paper. ACDIF constructs firstly several new combination difference (CDIF) operators, and then integrates the best two CDIFs as the final morphological filter. This design scheme enables ACIDF to extract positive and negative impacts existing in vibration signals to enhance accuracy of bearing fault diagnosis. The length of structure element (SE) that affects the performance of ACDIF is determined adaptively by a new indicator called Teager energy kurtosis (TEK). TEK further improves the effectiveness of ACDIF for fault feature extraction. Experimental results on the simulation and bearing vibration signals demonstrate that ACDIF can effectively suppress noise and extract periodic impulses from bearing vibration signals.

Morphological computed tomography features of surgically resectable pulmonary squamous cell carcinomas: Impact on prognosis and comparison with adenocarcinomas

Energy Technology Data Exchange (ETDEWEB)

Koenigkam Santos, Marcel, E-mail: marcelk46@yahoo.com.br [Department of Diagnostic and Interventional Radiology, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg (Germany); German Cancer Research Center (Deutsches Krebsforschungszentrum – DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg (Germany); Department of Radiology, University Hospital of the School of Medicine of Ribeirao Preto, University of Sao Paulo, Av. Bandeirantes 3900, Campus Universitario Monte Alegre, 14048 900 Ribeirao Preto, SP (Brazil); Muley, Thomas [Chest Clinic (Thoraxklinik) at University of Heidelberg, Amalienstr. 5, 69126 Heidelberg (Germany); Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Im Neuenheimer Feld 350, 69120 Heidelberg (Germany); Warth, Arne [Institute of Pathology, Heidelberg University, Im Neuenheimer Feld 224, 69120 Heidelberg (Germany); Paula, Wagner Diniz de [Department of Diagnostic and Interventional Radiology, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg (Germany); Department of Radiology, University of Brasilia, Brasilia (Brazil); Lederlin, Mathieu [Department of Diagnostic and Interventional Radiology, University of Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg (Germany); Department of Thoracic and Cardiovascular Imaging, University of Bordeaux, Bordeaux (France); Schnabel, Philipp Albert [Institute of Pathology, Heidelberg University, Im Neuenheimer Feld 224, 69120 Heidelberg (Germany); Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Im Neuenheimer Feld 350, 69120 Heidelberg (Germany); Schlemmer, Heinz-Peter [German Cancer Research Center (Deutsches Krebsforschungszentrum – DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg (Germany); and others

2014-07-15

Objective: To characterize the morphological computed tomography (CT) features of pulmonary squamous cell carcinomas (SQCC) submitted to therapeutic resection; to correlate these features with patients’ outcomes; and to compare with pulmonary adenocarcinomas (ADC). Materials and methods: Two chest radiologists retrospectively evaluated CT exams of 123 patients with SQCC resected between 2002 and 2008. Tumors’ size, location (central vs. peripheral), shape, margins, attenuation, enhancement, presence of calcification, cavitation, internal air bronchograms and pleural tags were assigned by consensus. Prevalence of features was compared with patients’ survival data and a previously studied population of ADC surgically resected at the same time period. Results: Cavitation correlated negatively with overall (hazard ratio = 3.04), disease-specific (HR = 3.67) and disease-free survival (HR = 2.69), independent from age, gender, tumor pathological stage, size, and location. In relation to ADC, SQCC presented different shape, margins, attenuation, enhancement, with more cavitation, rare internal air bronchograms, and less pleural tags. Differences were also significant when comparing only the peripheral type of tumors. Conclusions: Cavitation at CT was an independent and negative predictive factor for SQCC. Different CT morphological features were described for SQCC and ADC. Image evaluation of lung lesions should go beyond measuring and addressing adjacent structures invasion. Adequate imaging characterization not only helps to differentiate benign versus malignant disease and to determine malignancy staging, it may also imply the histologic subtype and improve the prognostic assessment of lung cancer patients.

Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

OpenAIRE

Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

2015-01-01

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

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Jarosław Rudy

2015-01-01

Full Text Available In this paper the job shop scheduling problem (JSP with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO and genetic algorithm (GA for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery

Science.gov (United States)

Gao, Junfeng; Liao, Wenzhi; Nuyttens, David; Lootens, Peter; Vangeyte, Jürgen; Pižurica, Aleksandra; He, Yong; Pieters, Jan G.

2018-05-01

The developments in the use of unmanned aerial vehicles (UAVs) and advanced imaging sensors provide new opportunities for ultra-high resolution (e.g., less than a 10 cm ground sampling distance (GSD)) crop field monitoring and mapping in precision agriculture applications. In this study, we developed a strategy for inter- and intra-row weed detection in early season maize fields from aerial visual imagery. More specifically, the Hough transform algorithm (HT) was applied to the orthomosaicked images for inter-row weed detection. A semi-automatic Object-Based Image Analysis (OBIA) procedure was developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. Furthermore, the two binary weed masks generated from HT and OBIA were fused for accurate binary weed image. The developed RF classifier was evaluated by 5-fold cross validation, and it obtained an overall accuracy of 0.945, and Kappa value of 0.912. Finally, the relationship of detected weeds and their ground truth densities was quantified by a fitted linear model with a coefficient of determination of 0.895 and a root mean square error of 0.026. Besides, the importance of input features was evaluated, and it was found that the ratio of vegetation length and width was the most significant feature for the classification model. Overall, our approach can yield a satisfactory weed map, and we expect that the obtained accurate and timely weed map from UAV imagery will be applicable to realize site-specific weed management (SSWM) in early season crop fields for reducing spraying non-selective herbicides and costs.

SIFT based algorithm for point feature tracking

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Adrian BURLACU

2007-12-01

Full Text Available In this paper a tracking algorithm for SIFT features in image sequences is developed. For each point feature extracted using SIFT algorithm a descriptor is computed using information from its neighborhood. Using an algorithm based on minimizing the distance between two descriptors tracking point features throughout image sequences is engaged. Experimental results, obtained from image sequences that capture scaling of different geometrical type object, reveal the performances of the tracking algorithm.

Emergent features and perceptual objects: re-examining fundamental principles in analogical display design.

Science.gov (United States)

Holt, Jerred; Bennett, Kevin B; Flach, John M

2015-01-01

Two sets of design principles for analogical visual displays, based on the concepts of emergent features and perceptual objects, are described. An interpretation of previous empirical findings for three displays (bar graph, polar graphic, alphanumeric) is provided from both perspectives. A fourth display (configural coordinate) was designed using principles of ecological interface design (i.e. direct perception). An experiment was conducted to evaluate performance (accuracy and latency of state identification) with these four displays. Numerous significant effects were obtained and a clear rank ordering of performance emerged (from best to worst): configural coordinate, bar graph, alphanumeric and polar graphic. These findings are consistent with principles of design based on emergent features; they are inconsistent with principles based on perceptual objects. Some limitations of the configural coordinate display are discussed and a redesign is provided. Practitioner Summary: Principles of ecological interface design, which emphasise the quality of very specific mappings between domain, display and observer constraints, are described; these principles are applicable to the design of all analogical graphical displays.

Development of an algorithm for heartbeats detection and classification in Holter records based on temporal and morphological features

International Nuclear Information System (INIS)

García, A; Romano, H; Laciar, E; Correa, R

2011-01-01

In this work a detection and classification algorithm for heartbeats analysis in Holter records was developed. First, a QRS complexes detector was implemented and their temporal and morphological characteristics were extracted. A vector was built with these features; this vector is the input of the classification module, based on discriminant analysis. The beats were classified in three groups: Premature Ventricular Contraction beat (PVC), Atrial Premature Contraction beat (APC) and Normal Beat (NB). These beat categories represent the most important groups of commercial Holter systems. The developed algorithms were evaluated in 76 ECG records of two validated open-access databases 'arrhythmias MIT BIH database' and M IT BIH supraventricular arrhythmias database . A total of 166343 beats were detected and analyzed, where the QRS detection algorithm provides a sensitivity of 99.69 % and a positive predictive value of 99.84 %. The classification stage gives sensitivities of 97.17% for NB, 97.67% for PCV and 92.78% for APC.

Morphologic Features Suggestive of Endometriosis in Nondiagnostic Peritoneal Biopsies.

Science.gov (United States)

Harrison, Beth T; Mittal, Khush

2015-11-01

Endometriosis is a common disorder that causes significant morbidity from dysmenorrhea, pelvic pain, and subfertility. Establishment of a definitive diagnosis has important therapeutic implications; however, only approximately 50% of biopsies of laparoscopically suspicious areas provide a diagnosis of endometriosis. Histologic criteria for diagnosis require the presence of endometrial glands or endometrial-type stroma. We hypothesize that other frequently present, but nondiagnostic, histologic features of endometriosis suggest its presence in patients with nondiagnostic peritoneal biopsies. We performed a retrospective clinicopathologic study of morphologic and immunohistochemical features that may improve the histologic diagnosis of endometriosis on laparoscopic peritoneal biopsies. We compared diagnostic (n=88) and nondiagnostic (n=54) peritoneal biopsies from pathologically confirmed endometriosis cases with negative peritoneal biopsies (n=84) from early-stage gynecologic cancer cases. Statistical analysis utilized the Fisher exact test. Multiple morphologic features were significantly increased in nondiagnostic biopsies from patients with endometriosis in comparison with those from negative controls, including foamy macrophages (P=0.0001) and submesothelial stromal clusters (SSCs) (P=0.0008). SSCs ranged from subtle aggregates of spindle cells to nodules of whorled spindle cells with small vessels and extravasated red blood cells resembling stromal endometriosis. Immunohistochemical studies confirmed that ER and CD10-positive SSCs were present in a greater proportion of both nondiagnostic and diagnostic peritoneal biopsies and at a greater number of lesions per biopsy. The overall histologic detection rate of peritoneal biopsies for endometriosis was 62.0%, and inclusion of SSCs with or without foamy macrophages in the diagnostic criteria appreciably increased this rate to between 72.5% and 76.8%. We describe SSCs, which appear to be an early or less developed

Phylogeny of kemenyan (Styrax sp.) from North Sumatra based on morphological characters

Science.gov (United States)

Susilowati, A.; Kholibrina, C. R.; Rachmat, H. H.; Munthe, M. A.

2018-02-01

Kemenyan is the most famous local tree species from North Sumatra. Kemenyan is known as rosin producer that very valuable for pharmacheutical, cosmetic, food preservatives and vernis. Based on its history, there were only two species of kemenyan those were kemenyan durame and toba, but in its the natural distribution we also found others species showing different characteristics with previously known ones. The objectives of this research were:The objectives of this research were: (1). To determine the morphological diversity of kemenyan in North Sumatra and (2). To determine phylogeny clustering based on the morphological characters. Data was collected from direct observation and morphological characterization, based on purposive sampling technique to those samples trees atPakpak Bharat, North Sumatra. Morphological characters were examined using descriptive analysis, phenotypic variability using standard deviation, and cluster analysis. The result showed that there was a difference between 4 species kemenyen (batak, minyak, durame and toba) according to 75 observed characters including flower, fruits, leaf, stem, bark, crown type, wood and the resin. Analysis and both quantitative and qualitative characters kemenyan clustered into two groups. In which, kemenyan toba separated with other clusters.

Mid-level perceptual features distinguish objects of different real-world sizes.

Science.gov (United States)

Long, Bria; Konkle, Talia; Cohen, Michael A; Alvarez, George A

2016-01-01

Understanding how perceptual and conceptual representations are connected is a fundamental goal of cognitive science. Here, we focus on a broad conceptual distinction that constrains how we interact with objects--real-world size. Although there appear to be clear perceptual correlates for basic-level categories (apples look like other apples, oranges look like other oranges), the perceptual correlates of broader categorical distinctions are largely unexplored, i.e., do small objects look like other small objects? Because there are many kinds of small objects (e.g., cups, keys), there may be no reliable perceptual features that distinguish them from big objects (e.g., cars, tables). Contrary to this intuition, we demonstrated that big and small objects have reliable perceptual differences that can be extracted by early stages of visual processing. In a series of visual search studies, participants found target objects faster when the distractor objects differed in real-world size. These results held when we broadly sampled big and small objects, when we controlled for low-level features and image statistics, and when we reduced objects to texforms--unrecognizable textures that loosely preserve an object's form. However, this effect was absent when we used more basic textures. These results demonstrate that big and small objects have reliably different mid-level perceptual features, and suggest that early perceptual information about broad-category membership may influence downstream object perception, recognition, and categorization processes. (c) 2015 APA, all rights reserved).

Early Contact Stage of Apoptosis: Its Morphological Features and Function

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Etheri Mikadze

2006-01-01

Full Text Available Apoptosis has been a biological phenomenon of intense interest for 20 years, but the earlier morphological features of apoptosis have not been determined hitherto. Using the methods of semi- and ultrathin sections, the livers of intact embryos and young rats have been studied under the effect of cycloheximide to determine morphological features of an early stage of apoptosis. It is discovered that both in hepatoblasts and hepatocytes, apoptosis, besides the well-known stages, also includes an early contact stage, distinguishing features of which are agglutination of bound ribosomes (breaking of translation, elimination of the nucleolus, reduction of free polysomes (and in hepatocytes, reduction of cisterns of rough endoplasmic reticulum, formation of cytoplasmic excrescences, and cell shape changes. The early stage of apoptosis is characterized by close contact with neighboring cells. At a certain phase of the contact stage of apoptosis, the nucleolus reappears in the nucleus and the number of free polysomes in the cytoplasm increases, which suggests the renewal of synthesis of new RNA and proteins. Close contact of differentiating and mitotic hepatoblasts with apoptotic cells indicates a certain functional relationship between these cells that is realized not only by micropinocytosis, but through gap junctions as well. We assume that the apoptotic cell, besides proteolytic products, can contain newly synthesized, low-molecular substances, the relocation of which from apoptotic to neighboring cells may contribute to both functional activity and proliferation of adjacent hepatoblasts and, therefore, the function of apoptosis may not be limited only to the elimination of harmful, damaged, and unwanted cells.

Feature-based tolerancing for intelligent inspection process definition

International Nuclear Information System (INIS)

Brown, C.W.

1993-07-01

This paper describes a feature-based tolerancing capability that complements a geometric solid model with an explicit representation of conventional and geometric tolerances. This capability is focused on supporting an intelligent inspection process definition system. The feature-based tolerance model's benefits include advancing complete product definition initiatives (e.g., STEP -- Standard for Exchange of Product model dam), suppling computer-integrated manufacturing applications (e.g., generative process planning and automated part programming) with product definition information, and assisting in the solution of measurement performance issues. A feature-based tolerance information model was developed based upon the notion of a feature's toleranceable aspects and describes an object-oriented scheme for representing and relating tolerance features, tolerances, and datum reference frames. For easy incorporation, the tolerance feature entities are interconnected with STEP solid model entities. This schema will explicitly represent the tolerance specification for mechanical products, support advanced dimensional measurement applications, and assist in tolerance-related methods divergence issues

Storage and binding of object features in visual working memory

OpenAIRE

Bays, Paul M; Wu, Emma Y; Husain, Masud

2010-01-01

An influential conception of visual working memory is of a small number of discrete memory “slots”, each storing an integrated representation of a single visual object, including all its component features. When a scene contains more objects than there are slots, visual attention controls which objects gain access to memory.

Category-based attentional guidance can operate in parallel for multiple target objects.

Science.gov (United States)

Jenkins, Michael; Grubert, Anna; Eimer, Martin

2018-04-30

The question whether the control of attention during visual search is always feature-based or can also be based on the category of objects remains unresolved. Here, we employed the N2pc component as an on-line marker for target selection processes to compare the efficiency of feature-based and category-based attentional guidance. Two successive displays containing pairs of real-world objects (line drawings of kitchen or clothing items) were separated by a 10 ms SOA. In Experiment 1, target objects were defined by their category. In Experiment 2, one specific visual object served as target (exemplar-based search). On different trials, targets appeared either in one or in both displays, and participants had to report the number of targets (one or two). Target N2pc components were larger and emerged earlier during exemplar-based search than during category-based search, demonstrating the superior efficiency of feature-based attentional guidance. On trials where target objects appeared in both displays, both targets elicited N2pc components that overlapped in time, suggesting that attention was allocated in parallel to these target objects. Critically, this was the case not only in the exemplar-based task, but also when targets were defined by their category. These results demonstrate that attention can be guided by object categories, and that this type of category-based attentional control can operate concurrently for multiple target objects. Copyright © 2018 Elsevier B.V. All rights reserved.

Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.

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Saba, Luca; Jain, Pankaj K; Suri, Harman S; Ikeda, Nobutaka; Araki, Tadashi; Singh, Bikesh K; Nicolaides, Andrew; Shafique, Shoaib; Gupta, Ajay; Laird, John R; Suri, Jasjit S

2017-06-01

Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.

Cranial base morphology and temporal bone pneumatization in Asian Homo erectus.

Science.gov (United States)

Balzeau, Antoine; Grimaud-Hervé, Dominique

2006-10-01

The external morphological features of the temporal bone are used frequently to determine taxonomic affinities of fossils of the genus Homo. Temporal bone pneumatization has been widely studied in great apes and in early hominids. However, this feature is rarely examined in the later hominids, particularly in Asian Homo erectus. We provide a comparative morphological and quantitative analysis of Asian Homo erectus from the sites of Ngandong, Sambungmacan, and Zhoukoudian, and of Neandertals and anatomically modern Homo sapiens in order to discuss causes and modalities of temporal bone pneumatization during hominid evolution. The evolution of temporal bone pneumatization in the genus Homo is more complex than previously described. Indeed, the Zhoukoudian fossils have a unique pattern of temporal bone pneumatization, whereas Ngandong and Sambungmacan fossils, as well as the Neandertals, more closely resemble the modern human pattern. Moreover, these Chinese fossils are characterized by a wide midvault and a relatively narrow occipital bone. Our results support the point of view that cell development does not play an active role in determining cranial base morphology. Instead, pneumatization is related to available space and to temporal bone morphology, and its development is related to correlated morphology and the relative disposition of the bones and cerebral lobes. Because variation in pneumatization is extensive within the same species, the phyletic implications of pneumatization are limited in the taxa considered here.

Role of early visual cortex in trans-saccadic memory of object features.

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Malik, Pankhuri; Dessing, Joost C; Crawford, J Douglas

2015-08-01

Early visual cortex (EVC) participates in visual feature memory and the updating of remembered locations across saccades, but its role in the trans-saccadic integration of object features is unknown. We hypothesized that if EVC is involved in updating object features relative to gaze, feature memory should be disrupted when saccades remap an object representation into a simultaneously perturbed EVC site. To test this, we applied transcranial magnetic stimulation (TMS) over functional magnetic resonance imaging-localized EVC clusters corresponding to the bottom left/right visual quadrants (VQs). During experiments, these VQs were probed psychophysically by briefly presenting a central object (Gabor patch) while subjects fixated gaze to the right or left (and above). After a short memory interval, participants were required to detect the relative change in orientation of a re-presented test object at the same spatial location. Participants either sustained fixation during the memory interval (fixation task) or made a horizontal saccade that either maintained or reversed the VQ of the object (saccade task). Three TMS pulses (coinciding with the pre-, peri-, and postsaccade intervals) were applied to the left or right EVC. This had no effect when (a) fixation was maintained, (b) saccades kept the object in the same VQ, or (c) the EVC quadrant corresponding to the first object was stimulated. However, as predicted, TMS reduced performance when saccades (especially larger saccades) crossed the remembered object location and brought it into the VQ corresponding to the TMS site. This suppression effect was statistically significant for leftward saccades and followed a weaker trend for rightward saccades. These causal results are consistent with the idea that EVC is involved in the gaze-centered updating of object features for trans-saccadic memory and perception.

Assessing the accuracy of perceptions of intelligence based on heritable facial features

OpenAIRE

Lee, Anthony J.; Hibbs, Courtney; Wright, Margaret J.; Martin, Nicholas G.; Keller, Matthew C.; Zietsch, Brendan P.

2017-01-01

Perceptions of intelligence based on facial features can have a profound impact on many social situations, but findings have been mixed as to whether these judgements are accurate. Even if such perceptions were accurate, the underlying mechanism is unclear. Several possibilities have been proposed, including evolutionary explanations where certain morphological facial features are associated with fitness-related traits (including cognitive development), or that intelligence judgements are ove...

Features of the choice of object of management and object of research in socially-educational systems

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O G Fedorov

2009-12-01

Full Text Available In work features of modeling and research of socially-educational systems are analyzed, principles of a choice of objects of management and objects of research, and also definition of factors of the importance of subsystems are considered.

Study of the morphology of corrosion features of natural graphite oxidised by dry and humid air

International Nuclear Information System (INIS)

Senevat, Jean

1965-12-01

The author reports a study which aimed at highlighting the morphology differences between corrosion features which affect flakes of natural graphite oxidised by dry air and by humid air. The study is based on observations made by optical and transmission electronic microscopy, this last one being performed on replicates. As the so-called 'Hennig' replicates did not result in a sufficient resolution of corrosion feature details, another method has been developed. Three classes of samples (in relationship with the rate of impurities present in samples) have been studied. Flakes have thus been sorted and each flake has then been oxidised at different wear rates. This highlights the influence of damages created by impurities in the lattice [fr

Infectious mononucleosis mimicking lymphoma: distinguishing morphological and immunophenotypic features.

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Louissaint, Abner; Ferry, Judith A; Soupir, Chad P; Hasserjian, Robert P; Harris, Nancy L; Zukerberg, Lawrence R

2012-08-01

The diagnosis of infectious mononucleosis (acute Epstein-Barr virus (EBV) infection) is usually made on the basis of clinical and laboratory findings. However, an atypical clinical presentation occasionally results in a lymph node or tonsillar biopsy. The morphological features of EBV-infected lymphoid tissue can easily mimic lymphoma. Furthermore, the immunophenotype of the immunoblasts has not been well characterized. To assess the morphological spectrum of acute EBV infection and the utility of immunohistochemistry in diagnosing difficult cases that resemble lymphoma, we reviewed 18 cases of acute EBV infection submitted in consultation to our institution with an initial diagnosis of/or suspicion for lymphoma. Patients included nine male and nine female individuals with a median age of 18 years (range 9-69). Biopsies were obtained from lymph nodes (3/18) or Waldeyer's ring (15/18). Infectious mononucleosis was confirmed by monospot or serological assays in 72% of cases (13/18). All cases featured architectural distortion by a polymorphous infiltrate with an immunoblastic proliferation, sometimes forming sheets. Reed-Sternberg-like cells were present in 8/18 (44%) of the cases. Infiltrates were often accompanied by necrosis (10/18) and mucosal ulceration (6/15). The majority of immunoblasts in all cases were CD20+ B cells with a post-germinal center immunophenotype (strongly positive for MUM1/IRF4 (18/18), CD10- (18/18 negative) and BCL-6- (16/18 negative; 2/18 faint BCL-6 expression in mononucleosis, and warrants additional consideration before a diagnosis of lymphoma is made.

Application of textural features to the objective diagnosis of Alzheimer-type dementia

International Nuclear Information System (INIS)

Kodama, Naoki; Shimada, Tetsuo; Kaeriyama, Tomoharu; Kaneko, Tomoyuki; Fukumoto, Ichiro; Kobayashi, Yoshio

2003-01-01

In this study, patients with Alzheimer-type dementia were compared with healthy elderly individuals by means of 13 textural features to evaluate the application of these features to the objective diagnosis of the dementia. A statistically significant difference was found in 8 of the 13 textural features between dementia patients and healthy controls. Discriminant analysis using the eight features demonstrated a sensitivity of 91.2% and a specificity of 86.4%, with an overall accuracy of 89.1%. Multiple discriminant analysis using the eight features by dementia stage showed an overall accuracy of 78.2% for discrimination of four stages. These results indicate that quantitative textural feature measurements can be used as an objective diagnostic technique for Alzheimer-type dementia. (author)

THE MORPHOLOGICAL PYRAMID AND ITS APPLICATIONS TO REMOTE SENSING: MULTIRESOLUTION DATA ANALYSIS AND FEATURES EXTRACTION

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Laporterie Florence

2011-05-01

Full Text Available In remote sensing, sensors are more and more numerous, and their spatial resolution is higher and higher. Thus, the availability of a quick and accurate characterisation of the increasing amount of data is now a quite important issue. This paper deals with an approach combining a pyramidal algorithm and mathematical morphology to study the physiographic characteristics of terrestrial ecosystems. Our pyramidal strategy involves first morphological filters, then extraction at each level of resolution of well-known landscapes features. The approach is applied to a digitised aerial photograph representing an heterogeneous landscape of orchards and forests along the Garonne river (France. This example, simulating very high spatial resolution imagery, highlights the influence of the parameters of the pyramid according to the spatial properties of the studied patterns. It is shown that, the morphological pyramid approach is a promising attempt for multi-level features extraction by modelling geometrical relevant parameters.

Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

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Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-01-01

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

Representation and Metrics Extraction from Feature Basis: An Object Oriented Approach

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Fausto Neri da Silva Vanin

2010-10-01

Full Text Available This tutorial presents an object oriented approach to data reading and metrics extraction from feature basis. Structural issues about basis are discussed first, then the Object Oriented Programming (OOP is aplied to modeling the main elements in this context. The model implementation is then discussed using C++ as programing language. To validate the proposed model, we apply on some feature basis from the University of Carolina, Irvine Machine Learning Database.

Model-based recognition of 3-D objects by geometric hashing technique

International Nuclear Information System (INIS)

Severcan, M.; Uzunalioglu, H.

1992-09-01

A model-based object recognition system is developed for recognition of polyhedral objects. The system consists of feature extraction, modelling and matching stages. Linear features are used for object descriptions. Lines are obtained from edges using rotation transform. For modelling and recognition process, geometric hashing method is utilized. Each object is modelled using 2-D views taken from the viewpoints on the viewing sphere. A hidden line elimination algorithm is used to find these views from the wire frame model of the objects. The recognition experiments yielded satisfactory results. (author). 8 refs, 5 figs

Feature-based RNN target recognition

Science.gov (United States)

Bakircioglu, Hakan; Gelenbe, Erol

1998-09-01

Detection and recognition of target signatures in sensory data obtained by synthetic aperture radar (SAR), forward- looking infrared, or laser radar, have received considerable attention in the literature. In this paper, we propose a feature based target classification methodology to detect and classify targets in cluttered SAR images, that makes use of selective signature data from sensory data, together with a neural network technique which uses a set of trained networks based on the Random Neural Network (RNN) model (Gelenbe 89, 90, 91, 93) which is trained to act as a matched filter. We propose and investigate radial features of target shapes that are invariant to rotation, translation, and scale, to characterize target and clutter signatures. These features are then used to train a set of learning RNNs which can be used to detect targets within clutter with high accuracy, and to classify the targets or man-made objects from natural clutter. Experimental data from SAR imagery is used to illustrate and validate the proposed method, and to calculate Receiver Operating Characteristics which illustrate the performance of the proposed algorithm.

Working memory units are all in your head: Factors that influence whether features or objects are the favored units.

Science.gov (United States)

Vergauwe, Evie; Cowan, Nelson

2015-09-01

We compared two contrasting hypotheses of how multifeatured objects are stored in visual working memory (vWM); as integrated objects or as independent features. A new procedure was devised to examine vWM representations of several concurrently held objects and their features and our main measure was reaction time (RT), allowing an examination of the real-time search through features and/or objects in an array in vWM. Response speeds to probes with color, shape, or both were studied as a function of the number of memorized colored shapes. Four testing groups were created by varying the instructions and the way in which probes with both color and shape were presented. The instructions explicitly either encouraged or discouraged the use of binding information and the task-relevance of binding information was further suggested by presenting probes with both color and shapes as either integrated objects or independent features. Our results show that the unit used for retrieval from vWM depends on the testing situation. Search was fully object-based only when all factors support that basis of search, in which case retrieving 2 features took no longer than retrieving a single feature. Otherwise, retrieving 2 features took longer than retrieving a single feature. Additional analyses of change detection latency suggested that, even though different testing situations can result in a stronger emphasis on either the feature dimension or the object dimension, neither one disappears from the representation and both concurrently affect change detection performance. (c) 2015 APA, all rights reserved).

An Innovative SIFT-Based Method for Rigid Video Object Recognition

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Jie Yu

2014-01-01

Full Text Available This paper presents an innovative SIFT-based method for rigid video object recognition (hereafter called RVO-SIFT. Just like what happens in the vision system of human being, this method makes the object recognition and feature updating process organically unify together, using both trajectory and feature matching, and thereby it can learn new features not only in the training stage but also in the recognition stage, which can improve greatly the completeness of the video object’s features automatically and, in turn, increases the ratio of correct recognition drastically. The experimental results on real video sequences demonstrate its surprising robustness and efficiency.

Filtering Airborne LIDAR Data by AN Improved Morphological Method Based on Multi-Gradient Analysis

Science.gov (United States)

Li, Y.

2013-05-01

The technology of airborne Light Detection And Ranging (LIDAR) is capable of acquiring dense and accurate 3D geospatial data. Although many related efforts have been made by a lot of researchers in the last few years, LIDAR data filtering is still a challenging task, especially for area with high relief or hybrid geographic features. In order to address the bare-ground extraction from LIDAR point clouds of complex landscapes, a novel morphological filtering algorithm is proposed based on multi-gradient analysis in terms of the characteristic of LIDAR data distribution in this paper. Firstly, point clouds are organized by an index mesh. Then, the multigradient of each point is calculated using the morphological method. And, objects are removed gradually by choosing some points to carry on an improved opening operation constrained by multi-gradient iteratively. 15 sample data provided by ISPRS Working Group III/3 are employed to test the filtering algorithm proposed. These sample data include those environments that may lead to filtering difficulty. Experimental results show that filtering algorithm proposed by this paper is of high adaptability to various scenes including urban and rural areas. Omission error, commission error and total error can be simultaneously controlled in a relatively small interval. This algorithm can efficiently remove object points while preserves ground points to a great degree.

Remembering perceptual features unequally bound in object and episodic tokens: Neural mechanisms and their electrophysiological correlates.

Science.gov (United States)

Zimmer, Hubert D; Ecker, Ullrich K H

2010-06-01

We present a neurocognitive model of long-term object memory. We propose that perceptual priming and episodic recognition are phenomena based on three distinct kinds of representations. We label these representations types and tokens. Types are prototypical representations needed for object identification. The network of non-arbitrary features necessary for object categorization is sharpened in the course of repeated identification, an effect that we call type trace and which causes perceptual priming. Tokens, on the other hand, support episodic recognition. Perirhinal structures are proposed to bind intrinsic within-object features into an object token that can be thought of as a consolidated perceptual object file. Hippocampal structures integrate object- with contextual information in an episodic token. The reinstatement of an object token is assumed to generate a feeling of familiarity, whereas recollection occurs when the reinstatement of an episodic token occurs. Retrieval mode and retrieval orientation dynamically modulate access to these representations. In this review, we apply the model to recent empirical research (behavioral, fMRI, and ERP data) including a series of studies from our own lab. We put specific emphasis on the effects that sensory features and their study-test match have on familiarity. The type-token approach fits the data and additionally provides a framework for the analysis of concepts like unitization and associative reinstatement. Copyright (c) 2010. Published by Elsevier Ltd.

Top-down attention based on object representation and incremental memory for knowledge building and inference.

Science.gov (United States)

Kim, Bumhwi; Ban, Sang-Woo; Lee, Minho

2013-10-01

Humans can efficiently perceive arbitrary visual objects based on an incremental learning mechanism with selective attention. This paper proposes a new task specific top-down attention model to locate a target object based on its form and color representation along with a bottom-up saliency based on relativity of primitive visual features and some memory modules. In the proposed model top-down bias signals corresponding to the target form and color features are generated, which draw the preferential attention to the desired object by the proposed selective attention model in concomitance with the bottom-up saliency process. The object form and color representation and memory modules have an incremental learning mechanism together with a proper object feature representation scheme. The proposed model includes a Growing Fuzzy Topology Adaptive Resonance Theory (GFTART) network which plays two important roles in object color and form biased attention; one is to incrementally learn and memorize color and form features of various objects, and the other is to generate a top-down bias signal to localize a target object by focusing on the candidate local areas. Moreover, the GFTART network can be utilized for knowledge inference which enables the perception of new unknown objects on the basis of the object form and color features stored in the memory during training. Experimental results show that the proposed model is successful in focusing on the specified target objects, in addition to the incremental representation and memorization of various objects in natural scenes. In addition, the proposed model properly infers new unknown objects based on the form and color features of previously trained objects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Assessment of features for automatic CTG analysis based on expert annotation.

Science.gov (United States)

Chudácek, Vacláv; Spilka, Jirí; Lhotská, Lenka; Janku, Petr; Koucký, Michal; Huptych, Michal; Bursa, Miroslav

2011-01-01

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

Clinico-morphological features of the female genital tract: review of the literature

Directory of Open Access Journals (Sweden)

S. O. Nikogosyan

2012-01-01

Full Text Available In the review are provided data of the world literature on clinical and morphological features of neuroendocrine tumors of the female sexual sphere. Questions of pathogenesis and stages of development of new growths in embryogenesis considered. Historical references are given. Besides, in article questions of diagnostics and treatment of these tumors are taken up.

Object-based target templates guide attention during visual search

OpenAIRE

Berggren, Nick; Eimer, Martin

2018-01-01

During visual search, attention is believed to be controlled in a strictly feature-based fashion, without any guidance by object-based target representations. To challenge this received view, we measured electrophysiological markers of attentional selection (N2pc component) and working memory (SPCN) in search tasks where two possible targets were defined by feature conjunctions (e.g., blue circles and green squares). Critically, some search displays also contained nontargets with two target f...

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data.

Science.gov (United States)

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-12-22

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.

Video rate morphological processor based on a redundant number representation

Science.gov (United States)

Kuczborski, Wojciech; Attikiouzel, Yianni; Crebbin, Gregory A.

1992-03-01

This paper presents a video rate morphological processor for automated visual inspection of printed circuit boards, integrated circuit masks, and other complex objects. Inspection algorithms are based on gray-scale mathematical morphology. Hardware complexity of the known methods of real-time implementation of gray-scale morphology--the umbra transform and the threshold decomposition--has prompted us to propose a novel technique which applied an arithmetic system without carrying propagation. After considering several arithmetic systems, a redundant number representation has been selected for implementation. Two options are analyzed here. The first is a pure signed digit number representation (SDNR) with the base of 4. The second option is a combination of the base-2 SDNR (to represent gray levels of images) and the conventional twos complement code (to represent gray levels of structuring elements). Operation principle of the morphological processor is based on the concept of the digit level systolic array. Individual processing units and small memory elements create a pipeline. The memory elements store current image windows (kernels). All operation primitives of processing units apply a unified direction of digit processing: most significant digit first (MSDF). The implementation technology is based on the field programmable gate arrays by Xilinx. This paper justified the rationality of a new approach to logic design, which is the decomposition of Boolean functions instead of Boolean minimization.

Shape-based hand recognition approach using the morphological pattern spectrum

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Ramirez-Cortes, Juan Manuel; Gomez-Gil, Pilar; Sanchez-Perez, Gabriel; Prieto-Castro, Cesar

2009-01-01

We propose the use of the morphological pattern spectrum, or pecstrum, as the base of a biometric shape-based hand recognition system. The system receives an image of the right hand of a subject in an unconstrained pose, which is captured with a commercial flatbed scanner. According to pecstrum property of invariance to translation and rotation, the system does not require the use of pegs for a fixed hand position, which simplifies the image acquisition process. This novel feature-extraction method is tested using a Euclidean distance classifier for identification and verification cases, obtaining 97% correct identification, and an equal error rate (EER) of 0.0285 (2.85%) for the verification mode. The obtained results indicate that the pattern spectrum represents a good feature-extraction alternative for low- and medium-level hand-shape-based biometric applications.

An object-based approach for tree species extraction from digital orthophoto maps

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Jamil, Akhtar; Bayram, Bulent

2018-05-01

Tree segmentation is an active and ongoing research area in the field of photogrammetry and remote sensing. It is more challenging due to both intra-class and inter-class similarities among various tree species. In this study, we exploited various statistical features for extraction of hazelnut trees from 1 : 5000 scaled digital orthophoto maps. Initially, the non-vegetation areas were eliminated using traditional normalized difference vegetation index (NDVI) followed by application of mean shift segmentation for transforming the pixels into meaningful homogeneous objects. In order to eliminate false positives, morphological opening and closing was employed on candidate objects. A number of heuristics were also derived to eliminate unwanted effects such as shadow and bounding box aspect ratios, before passing them into the classification stage. Finally, a knowledge based decision tree was constructed to distinguish the hazelnut trees from rest of objects which include manmade objects and other type of vegetation. We evaluated the proposed methodology on 10 sample orthophoto maps obtained from Giresun province in Turkey. The manually digitized hazelnut tree boundaries were taken as reference data for accuracy assessment. Both manually digitized and segmented tree borders were converted into binary images and the differences were calculated. According to the obtained results, the proposed methodology obtained an overall accuracy of more than 85 % for all sample images.

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

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Hoshang Kolivand

Full Text Available In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

An expert botanical feature extraction technique based on phenetic features for identifying plant species

Science.gov (United States)

Fern, Bong Mei; Rahim, Mohd Shafry Mohd; Sulong, Ghazali; Baker, Thar; Tully, David

2018-01-01

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost. PMID:29420568

Conditioning 3D object-based models to dense well data

Science.gov (United States)

Wang, Yimin C.; Pyrcz, Michael J.; Catuneanu, Octavian; Boisvert, Jeff B.

2018-06-01

Object-based stochastic simulation models are used to generate categorical variable models with a realistic representation of complicated reservoir heterogeneity. A limitation of object-based modeling is the difficulty of conditioning to dense data. One method to achieve data conditioning is to apply optimization techniques. Optimization algorithms can utilize an objective function measuring the conditioning level of each object while also considering the geological realism of the object. Here, an objective function is optimized with implicit filtering which considers constraints on object parameters. Thousands of objects conditioned to data are generated and stored in a database. A set of objects are selected with linear integer programming to generate the final realization and honor all well data, proportions and other desirable geological features. Although any parameterizable object can be considered, objects from fluvial reservoirs are used to illustrate the ability to simultaneously condition multiple types of geologic features. Channels, levees, crevasse splays and oxbow lakes are parameterized based on location, path, orientation and profile shapes. Functions mimicking natural river sinuosity are used for the centerline model. Channel stacking pattern constraints are also included to enhance the geological realism of object interactions. Spatial layout correlations between different types of objects are modeled. Three case studies demonstrate the flexibility of the proposed optimization-simulation method. These examples include multiple channels with high sinuosity, as well as fragmented channels affected by limited preservation. In all cases the proposed method reproduces input parameters for the object geometries and matches the dense well constraints. The proposed methodology expands the applicability of object-based simulation to complex and heterogeneous geological environments with dense sampling.

Effect of Feature Dimensionality on Object-based Land Cover ...

African Journals Online (AJOL)

Myburgh, G, Mnr

features, it has not been demonstrated with land cover mapping in an ... classifiers were chosen for benchmarking as the latter is the most commonly .... Additional open-source libraries were acquired to complete the implementation of the.

Comparative Visual Analysis of Structure-Performance Relations in Complex Bulk-Heterojunction Morphologies

KAUST Repository

Aboulhassan, A.

2017-07-04

The structure of Bulk-Heterojunction (BHJ) materials, the main component of organic photovoltaic solar cells, is very complex, and the relationship between structure and performance is still largely an open question. Overall, there is a wide spectrum of fabrication configurations resulting in different BHJ morphologies and correspondingly different performances. Current state-of-the-art methods for assessing the performance of BHJ morphologies are either based on global quantification of morphological features or simply on visual inspection of the morphology based on experimental imaging. This makes finding optimal BHJ structures very challenging. Moreover, finding the optimal fabrication parameters to get an optimal structure is still an open question. In this paper, we propose a visual analysis framework to help answer these questions through comparative visualization and parameter space exploration for local morphology features. With our approach, we enable scientists to explore multivariate correlations between local features and performance indicators of BHJ morphologies. Our framework is built on shape-based clustering of local cubical regions of the morphology that we call patches. This enables correlating the features of clusters with intuition-based performance indicators computed from geometrical and topological features of charge paths.

Comparative Visual Analysis of Structure-Performance Relations in Complex Bulk-Heterojunction Morphologies

KAUST Repository

Aboulhassan, A.; Sicat, R.; Baum, D.; Wodo, O.; Hadwiger, Markus

2017-01-01

The structure of Bulk-Heterojunction (BHJ) materials, the main component of organic photovoltaic solar cells, is very complex, and the relationship between structure and performance is still largely an open question. Overall, there is a wide spectrum of fabrication configurations resulting in different BHJ morphologies and correspondingly different performances. Current state-of-the-art methods for assessing the performance of BHJ morphologies are either based on global quantification of morphological features or simply on visual inspection of the morphology based on experimental imaging. This makes finding optimal BHJ structures very challenging. Moreover, finding the optimal fabrication parameters to get an optimal structure is still an open question. In this paper, we propose a visual analysis framework to help answer these questions through comparative visualization and parameter space exploration for local morphology features. With our approach, we enable scientists to explore multivariate correlations between local features and performance indicators of BHJ morphologies. Our framework is built on shape-based clustering of local cubical regions of the morphology that we call patches. This enables correlating the features of clusters with intuition-based performance indicators computed from geometrical and topological features of charge paths.

Surface and morphological features of laser-irradiated silicon under vacuum, nitrogen and ethanol

Energy Technology Data Exchange (ETDEWEB)

Hayat, Asma, E-mail: asmahayat@gcu.edu.pk; Bashir, Shazia; Akram, Mahreen; Mahmood, Khaliq; Iqbal, Muhammad Hassan

2015-12-01

Highlights: • Laser irradiation effects on Si surface have been explored. • An Excimer Laser was used as a source. • SEM analysis was performed to explore surface morphology. • Raman spectroscopy analysis was carried out to find crystallographical alterations. - Abstract: Laser-induced surface and structural modification of silicon (Si) has been investigated under three different environments of vacuum, nitrogen (100 Torr) and ethanol. The interaction of 1000 pulses of KrF (λ ≈ 248 nm, τ ≈ 18 ns, repetition rate ≈ 30 Hz) Excimer laser at two different fluences of 2.8 J/cm{sup 2} and 4 J/cm{sup 2} resulted in formation of various kinds of features such as laser induced periodic surface structures (LIPSS), spikes, columns, cones and cracks. Surface morphology has been observed by Scanning Electron Microscope (SEM). Whereas, structural modification of irradiated targets is explored by Raman spectroscopy. SEM analysis exhibits a non-uniform distribution of micro-scale pillars and spikes at the central ablated regime of silicon irradiated at low laser fluence of 2.8 J/cm{sup 2} under vacuum. Whereas cones, pits, cavities and ripples like features are seen at the boundaries. At higher fluence of 4 J/cm{sup 2}, laser induced periodic structures as well as micro-columns are observed. In the case of ablation in nitrogen environment, melting, splashing, self-organized granular structures and cracks along with redeposition are observed at lower fluence. Such types of small scaled structures in nitrogen are attributed to confinement and shielding effects of nitrogen plasma. Whereas, a crater with multiple ablative layers is formed in the case of ablation at higher fluence. Significantly different surface morphology of Si is observed in the case of ablation in ethanol. It reveals the formation of cavities along with small scale pores and less redeposition. These results reveal that the growth of surface and morphological features of irradiated Si are strongly

Surface and morphological features of laser-irradiated silicon under vacuum, nitrogen and ethanol

International Nuclear Information System (INIS)

Hayat, Asma; Bashir, Shazia; Akram, Mahreen; Mahmood, Khaliq; Iqbal, Muhammad Hassan

2015-01-01

Highlights: • Laser irradiation effects on Si surface have been explored. • An Excimer Laser was used as a source. • SEM analysis was performed to explore surface morphology. • Raman spectroscopy analysis was carried out to find crystallographical alterations. - Abstract: Laser-induced surface and structural modification of silicon (Si) has been investigated under three different environments of vacuum, nitrogen (100 Torr) and ethanol. The interaction of 1000 pulses of KrF (λ ≈ 248 nm, τ ≈ 18 ns, repetition rate ≈ 30 Hz) Excimer laser at two different fluences of 2.8 J/cm 2 and 4 J/cm 2 resulted in formation of various kinds of features such as laser induced periodic surface structures (LIPSS), spikes, columns, cones and cracks. Surface morphology has been observed by Scanning Electron Microscope (SEM). Whereas, structural modification of irradiated targets is explored by Raman spectroscopy. SEM analysis exhibits a non-uniform distribution of micro-scale pillars and spikes at the central ablated regime of silicon irradiated at low laser fluence of 2.8 J/cm 2 under vacuum. Whereas cones, pits, cavities and ripples like features are seen at the boundaries. At higher fluence of 4 J/cm 2 , laser induced periodic structures as well as micro-columns are observed. In the case of ablation in nitrogen environment, melting, splashing, self-organized granular structures and cracks along with redeposition are observed at lower fluence. Such types of small scaled structures in nitrogen are attributed to confinement and shielding effects of nitrogen plasma. Whereas, a crater with multiple ablative layers is formed in the case of ablation at higher fluence. Significantly different surface morphology of Si is observed in the case of ablation in ethanol. It reveals the formation of cavities along with small scale pores and less redeposition. These results reveal that the growth of surface and morphological features of irradiated Si are strongly dependent upon the

Errors induced in the measurement and azimuth directions of morphological features imaged on oblique Lunar Orbiter photographs

Science.gov (United States)

Siegal, B. S.

1974-01-01

Many quantitative lunar studies, e.g., the morphology and dimensions of craters, crater density and distribution, have been performed using oblique Lunar Orbiter photographs. If the inherent change in scale and azimuth direction of features imaged on these photographs are not corrected, the measurement can be in considerable error and the resulting statistical inferences may be invalid. The magnitude of this error is dependent upon: the depression angle of the camera, the flight height of the spacecraft, the focal length of the camera, and the position and orientation of the object on the ground. The errors introduced by using unrectified oblique photographs as though they were vertical photographs are examined for several Lunar Orbiter high resolution NASA LRC Enhancement photographic prints taken at various depression angles.

The modulation of inhibition of return by object-internal structure: implications for theories of object-based attentional selection.

Science.gov (United States)

Reppa, Irene; Leek, E Charles

2003-06-01

Recently, Vecera, Behrmann, and McGoldrick (2000), using a divided-attention task, reported that targets are detected more accurately when they occur on the same structural part of an object, suggesting that attention can be directed toward object-internal features. We present converging evidence using the object-based inhibition of return (IOR) paradigm as an implicit measure of selection. The results show that IOR is attenuated when cues and targets appear on the same part of an object relative to when they are separated by a part boundary. These findings suggest that object-based mechanisms of selection can operate over shape representations that make explicit information about object-internal structure.

Representing Objects using Global 3D Relational Features for Recognition Tasks

DEFF Research Database (Denmark)

Mustafa, Wail

2015-01-01

representations. For representing objects, we derive global descriptors encoding shape using viewpoint-invariant features obtained from multiple sensors observing the scene. Objects are also described using color independently. This allows for combining color and shape when it is required for the task. For more...... robust color description, color calibration is performed. The framework was used in three recognition tasks: object instance recognition, object category recognition, and object spatial relationship recognition. For the object instance recognition task, we present a system that utilizes color and scale...

A Secure and Robust Object-Based Video Authentication System

Directory of Open Access Journals (Sweden)

He Dajun

2004-01-01

Full Text Available An object-based video authentication system, which combines watermarking, error correction coding (ECC, and digital signature techniques, is presented for protecting the authenticity between video objects and their associated backgrounds. In this system, a set of angular radial transformation (ART coefficients is selected as the feature to represent the video object and the background, respectively. ECC and cryptographic hashing are applied to those selected coefficients to generate the robust authentication watermark. This content-based, semifragile watermark is then embedded into the objects frame by frame before MPEG4 coding. In watermark embedding and extraction, groups of discrete Fourier transform (DFT coefficients are randomly selected, and their energy relationships are employed to hide and extract the watermark. The experimental results demonstrate that our system is robust to MPEG4 compression, object segmentation errors, and some common object-based video processing such as object translation, rotation, and scaling while securely preventing malicious object modifications. The proposed solution can be further incorporated into public key infrastructure (PKI.

The fate of task-irrelevant visual motion: perceptual load versus feature-based attention.

Science.gov (United States)

Taya, Shuichiro; Adams, Wendy J; Graf, Erich W; Lavie, Nilli

2009-11-18

We tested contrasting predictions derived from perceptual load theory and from recent feature-based selection accounts. Observers viewed moving, colored stimuli and performed low or high load tasks associated with one stimulus feature, either color or motion. The resultant motion aftereffect (MAE) was used to evaluate attentional allocation. We found that task-irrelevant visual features received less attention than co-localized task-relevant features of the same objects. Moreover, when color and motion features were co-localized yet perceived to belong to two distinct surfaces, feature-based selection was further increased at the expense of object-based co-selection. Load theory predicts that the MAE for task-irrelevant motion would be reduced with a higher load color task. However, this was not seen for co-localized features; perceptual load only modulated the MAE for task-irrelevant motion when this was spatially separated from the attended color location. Our results suggest that perceptual load effects are mediated by spatial selection and do not generalize to the feature domain. Feature-based selection operates to suppress processing of task-irrelevant, co-localized features, irrespective of perceptual load.

Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories

OpenAIRE

Takahiro Soshi; Norio Fujimaki; Atsushi Matsumoto; Aya S. Ihara

2017-01-01

Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, and fish), along with 195 verbal features. Healthy adults participated in memory-based feature-animal m...

A Method to Measure the Bracelet Based on Feature Energy

Science.gov (United States)

Liu, Hongmin; Li, Lu; Wang, Zhiheng; Huo, Zhanqiang

2017-12-01

To measure the bracelet automatically, a novel method based on feature energy is proposed. Firstly, the morphological method is utilized to preprocess the image, and the contour consisting of a concentric circle is extracted. Then, a feature energy function, which is relevant to the distances from one pixel to the edge points, is defined taking into account the geometric properties of the concentric circle. The input image is subsequently transformed to the feature energy distribution map (FEDM) by computing the feature energy of each pixel. The center of the concentric circle is thus located by detecting the maximum on the FEDM; meanwhile, the radii of the concentric circle are determined according to the feature energy function of the center pixel. Finally, with the use of a calibration template, the internal diameter and thickness of the bracelet are measured. The experimental results show that the proposed method can measure the true sizes of the bracelet accurately with the simplicity, directness and robustness compared to the existing methods.

Fruit Morphology as Taxonomic Features in Five Varieties of Capsicum annuum L. Solanaceae

Directory of Open Access Journals (Sweden)

Daniel Andrawus Zhigila

2014-01-01

Full Text Available Variations in the fruit morphological features of Capsicum annuum varieties were studied. Varieties studied include var. abbreviatum, var. annuum, var. accuminatum, var. grossum, and var. glabriusculum. The fruit morphology revealed attenuated fruit shape with rounded surfaces in var. glabriusculum, and cordate fruit shape with flexuous surface in var. annuum, abbreviatum and accuminatum. The fruit is a berry and may be green, yellow, or red when ripe. The fruit epidermal cell-wall patterns are polygonal in shape with straight and curved anticlinal walls in all the five varieties. The fruit of var. abbreviatum and var. grossum is trilocular, while that of var. accuminatum and annuum is bilocular, and that of var. glabriusculum is tetralocular. Capsicum annuum var. glabriusculum had the highest mean number of seeds (108.4 and var. annuum had the lowest number of seeds (41.3 per fruit. The fruit is conspicuously hollowed in var. glabriusculum, accuminatum, and annuum but inconspicuously hollowed in var. abbreviatum and var. grossum. These features are shown to be good taxonomic characters for delimiting the five varieties of Capsicum annuum.

Preattentive representation of feature conjunctions for concurrent spatially distributed auditory objects.

Science.gov (United States)

Takegata, Rika; Brattico, Elvira; Tervaniemi, Mari; Varyagina, Olga; Näätänen, Risto; Winkler, István

2005-09-01

The role of attention in conjoining features of an object has been a topic of much debate. Studies using the mismatch negativity (MMN), an index of detecting acoustic deviance, suggested that the conjunctions of auditory features are preattentively represented in the brain. These studies, however, used sequentially presented sounds and thus are not directly comparable with visual studies of feature integration. Therefore, the current study presented an array of spatially distributed sounds to determine whether the auditory features of concurrent sounds are correctly conjoined without focal attention directed to the sounds. Two types of sounds differing from each other in timbre and pitch were repeatedly presented together while subjects were engaged in a visual n-back working-memory task and ignored the sounds. Occasional reversals of the frequent pitch-timbre combinations elicited MMNs of a very similar amplitude and latency irrespective of the task load. This result suggested preattentive integration of auditory features. However, performance in a subsequent target-search task with the same stimuli indicated the occurrence of illusory conjunctions. The discrepancy between the results obtained with and without focal attention suggests that illusory conjunctions may occur during voluntary access to the preattentively encoded object representations.

Nanoscale synthesis and characterization of graphene-based objects

Directory of Open Access Journals (Sweden)

Daisuke Fujita

2011-01-01

Full Text Available Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (STM has clarified the structure and functionality of the novel nano-objects at atomic resolution. Nanobelts were detected consisting of bilayer graphene sheets with a nanoscale width and a length of several microns. Moiré patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.

Improving Remote Sensing Scene Classification by Integrating Global-Context and Local-Object Features

Directory of Open Access Journals (Sweden)

Dan Zeng

2018-05-01

Full Text Available Recently, many researchers have been dedicated to using convolutional neural networks (CNNs to extract global-context features (GCFs for remote-sensing scene classification. Commonly, accurate classification of scenes requires knowledge about both the global context and local objects. However, unlike the natural images in which the objects cover most of the image, objects in remote-sensing images are generally small and decentralized. Thus, it is hard for vanilla CNNs to focus on both global context and small local objects. To address this issue, this paper proposes a novel end-to-end CNN by integrating the GCFs and local-object-level features (LOFs. The proposed network includes two branches, the local object branch (LOB and global semantic branch (GSB, which are used to generate the LOFs and GCFs, respectively. Then, the concatenation of features extracted from the two branches allows our method to be more discriminative in scene classification. Three challenging benchmark remote-sensing datasets were extensively experimented on; the proposed approach outperformed the existing scene classification methods and achieved state-of-the-art results for all three datasets.

Rule set transferability for object-based feature extraction

NARCIS (Netherlands)

Anders, N.S.; Seijmonsbergen, Arie C.; Bouten, Willem

2015-01-01

Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability of an

OBEST: The Object-Based Event Scenario Tree Methodology

International Nuclear Information System (INIS)

WYSS, GREGORY D.; DURAN, FELICIA A.

2001-01-01

Event tree analysis and Monte Carlo-based discrete event simulation have been used in risk assessment studies for many years. This report details how features of these two methods can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology with some of the best features of each. The resultant Object-Based Event Scenarios Tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible (especially those that exhibit inconsistent or variable event ordering, which are difficult to represent in an event tree analysis). Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST method uses a recursive algorithm to solve the object model and identify all possible scenarios and their associated probabilities. Since scenario likelihoods are developed directly by the solution algorithm, they need not be computed by statistical inference based on Monte Carlo observations (as required by some discrete event simulation methods). Thus, OBEST is not only much more computationally efficient than these simulation methods, but it also discovers scenarios that have extremely low probabilities as a natural analytical result--scenarios that would likely be missed by a Monte Carlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

OpenAIRE

Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

2014-01-01

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

Morphological and anatomical features of achenes of Anemone L. (Ranunculaceae Juss. of the flora of Ukraine

Directory of Open Access Journals (Sweden)

О.М. Tsarenko

2016-06-01

Full Text Available Based on SEM and LM some morphological and anatomical features of achenes of Anemone L. of Ukrainian flora (A. narcissiflora, A. sylvestris, A. nemorosa, A. narcissiflora have been investigated. Сarpological features after which it is possible to diagnose species are detected: the dimensions and shape of the achenes and beaks, the peculiarities of pubescence (length and character of localization of the hairs, a presence of different appendages of pericarp (as ribs or wings which surround body of fruit, thickness of pericarp and peculiarities of the thickness of the cell walls of endocarp. Detailed descriptions of fruitlets have been represented. The key for determination of species not in a flourishing condition on the revealed signs have been composed. Data obtained are important for systematics and phylogeny of the genus and the family as a whole.

Morphological and genetic features of cisco (coregonidae: coregonus sp. from lake Sobachye (Putorana plateau

Directory of Open Access Journals (Sweden)

Elena A. Borovikova

2016-09-01

Full Text Available Background. Recently was revealed that cisco from Lake Sobachye (Putorana Plateau is more similar to Coregonus albula Linnaeuas than C. sardinella Valenciennes according to number of vertebrae [13]. The aim of this work was to investigate molecular genetic features of this population.  Materials and methods. For morphological analysis were used 60 specimens of cisco from Lake Sobachye. For nine specimens molecular genetic analysis was performed. The sequences of two fragments of the mitochondrial DNA (ND1 and COI were defined.  Results. The cisco of the Lake Sobachye significantly differed from riverine cisco of this region by meristic features (namely from cisco of the River Pyasina. Sequencing results showed the minimal divergence of the ND1 and COI sequences of the cisco from Lake Sobachye and vendace.  Conclusion. Morphological analysis and analysis of the mitochondrial DNA polymorphism of cisco from Lake Sobachye revealed close relationship of this population to C. albula.

Classifying Physical Morphology of Cocoa Beans Digital Images using Multiclass Ensemble Least-Squares Support Vector Machine

Science.gov (United States)

Lawi, Armin; Adhitya, Yudhi

2018-03-01

The objective of this research is to determine the quality of cocoa beans through morphology of their digital images. Samples of cocoa beans were scattered on a bright white paper under a controlled lighting condition. A compact digital camera was used to capture the images. The images were then processed to extract their morphological parameters. Classification process begins with an analysis of cocoa beans image based on morphological feature extraction. Parameters for extraction of morphological or physical feature parameters, i.e., Area, Perimeter, Major Axis Length, Minor Axis Length, Aspect Ratio, Circularity, Roundness, Ferret Diameter. The cocoa beans are classified into 4 groups, i.e.: Normal Beans, Broken Beans, Fractured Beans, and Skin Damaged Beans. The model of classification used in this paper is the Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM), a proposed improvement model of SVM using ensemble method in which the separate hyperplanes are obtained by least square approach and the multiclass procedure uses One-Against- All method. The result of our proposed model showed that the classification with morphological feature input parameters were accurately as 99.705% for the four classes, respectively.

5-YEAR SURVIVAL OF PATIENTS WITH STAGE II UTERINE CANCER DEPENDING ON MORPHOLOGIC FEATURES OF TUMOR

Directory of Open Access Journals (Sweden)

Ye. A. Mustafina

2008-01-01

Full Text Available Retrospective data of treatment results of 109 patients with rarely observed stage II uterine cancer, admitted to N.N. Blokhin Russian Cancer Research Center from 1980 to 2000 is analyzed. Correlation of overall 5-year survival rates of stage IIA and IIB uterine can- cer patients with a number of tumor morphologic features is studied. The influence of some non-elucidated morphologic features of stage IIA and IIB uterine cancer such as the degree of cellular anaplasia, the depth of tumor invasion into the uterine neck, lymho- vascular invasion into the myometrium and uterine neck, microscopic vessels density in the area of the most extensive invasion, the presence of necrotic areas in the tumor tissue on long-term treatment results are analyzed.

Computer-aided diagnosis with morphological features for breast lesion on sonograms

International Nuclear Information System (INIS)

Huang Yu-Len; Jiang Yu-Ru; Shiu Jia-Jia; Chen Dar-Ren; Moon Woo Kyung

2007-01-01

Information about shape, provided by a breast tumor contour is important to physicians in making diagnostic decisions. To avoid needless biopsy and enhance the diagnostic accuracy, a computer-aided diagnosis (CAD) system can provide a second beneficial support reference. This paper aimed to evaluate the potential role of the CAD with automatic contouring and morphologic analysis in the differential of breast tumors for ultrasound (US) images. This study evaluated 118 breast lesions. The suspicious tumor contour in the digitized US image was automatically extracted by the proposed contouring algorithm. Then 20 practical morphologic features from the extracted contour were calculated and a support vector machine (SVM) classifier identified the breast tumor as benign or malignant. The area (Az) under the receiver operating characteristic (ROC) curve for the proposed CAD system was 0.91 ± 0.03. This system differentiates benign from malignant breast tumors with relative high accuracy and is therefore clinically useful to reduce patients needed for dispensable breast biopsy. (orig.)

Object-based landslide detection in different geographic regions

Science.gov (United States)

Friedl, Barbara; Hölbling, Daniel; Eisank, Clemens; Blaschke, Thomas

2015-04-01

, SPOT-5 images are combined with digital elevation models (DEM) for developing a consistent semi-automated landslide detection approach using eCognition (Trimble) software. Suitable image objects are generated by means of multiresolution segmentation. Expert knowledge, i.e. reported facts on features (e.g. mean object slope, mean NDVI) and thresholds that are commonly chosen by professionals for digital landslide mapping, is considered during classification. The applicability of a range of features is tested and the most promising parameters, i.e. features that produce appropriate results for both regions, are selected for landslide detection. However, minor adaptations of particular thresholds are necessary due to the distinct environmental conditions of the test sites. In order to reduce the number of required adjustments to a minimum, relational features and spectral indices are primarily used for classification. The obtained results are finally compared to manually digitized reference polygons and existing landslide inventories in order to quantify the applicability of the developed object-based landslide detection approach in different geographic regions.

A recurrent neural model for proto-object based contour integration and figure-ground segregation.

Science.gov (United States)

Hu, Brian; Niebur, Ernst

2017-12-01

Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

Biases Towards Internal Features in Infants' Reasoning about Objects

Science.gov (United States)

Newman, George E.; Herrmann, Patricia; Wynn, Karen; Keil, Frank C.

2008-01-01

This paper reports the results of two sets of studies demonstrating 14-month-olds' tendency to associate an object's behavior with internal, rather than external features. In Experiment 1 infants were familiarized to two animated cats that each exhibited a different style of self-generated motion. Infants then saw a novel individual that had an…

Comparing infants' use of featural and spatiotemporal information when individuating objects in an event monitoring design

DEFF Research Database (Denmark)

Krøjgaard, Peter

. The results obtained using this design reveal that infants are more successful using spatiotemporal object information than when using featural information. However, recent studies using the less cognitively demanding event monitoring design have revealed that even younger infants are capable of object...... in the present series of experiments in which infants' use of spatiotemporal and featural information is compared directly using the less demanding event monitoring design. The results are discussed in relation to existing empirical evidence......., to what extent infants rely on spatiotemporal or featural object information when individuating objects is currently under debate. Hitherto, infants' use of spatiotemporal and featural object information has only been compared directly using the rather cognitively demanding event mapping design...

Personalised learning object based on multi-agent model and learners’ learning styles

Directory of Open Access Journals (Sweden)

Noppamas Pukkhem

2011-09-01

Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.

Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Science.gov (United States)

Ai, Dan-Ni; Han, Xian-Hua; Ruan, Xiang; Chen, Yen-Wei

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.

Joint spatial-depth feature pooling for RGB-D object classification

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2015-01-01

RGB-D camera can provide effective support with additional depth cue for many RGB-D perception tasks beyond traditional RGB information. However, current feature representations based on RGB-D camera utilize depth information only to extract local features, without considering it for the improvem......RGB-D camera can provide effective support with additional depth cue for many RGB-D perception tasks beyond traditional RGB information. However, current feature representations based on RGB-D camera utilize depth information only to extract local features, without considering...

A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS

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T. Kavzoglu

2016-06-01

Full Text Available Within the last two decades, object-based image analysis (OBIA considering objects (i.e. groups of pixels instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient. Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

Geographic Object-Based Image Analysis: Towards a new paradigm

NARCIS (Netherlands)

Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.A.|info:eu-repo/dai/nl/224281216; Queiroz Feitosa, R.; van der Meer, F.D.|info:eu-repo/dai/nl/138940908; van der Werff, H.M.A.; van Coillie, F.; Tiede, A.

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature

Perception Of "Features" And "Objects": Applications To The Design Of Instrument Panel Displays

Science.gov (United States)

Poynter, Douglas; Czarnomski, Alan J.

1988-10-01

An experiment was conducted to determine whether socalled feature displays allow for faster and more accurate processing compared to object displays. Previous psychological studies indicate that features can be processed in parallel across the visual field, whereas objects must be processed one at a time with the aid of attentional focus. Numbers and letters are examples of objects; line orientation and color are examples of features. In this experiment, subjects were asked to search displays composed of up to 16 elements for the presence of specific elements. The ability to detect, localize, and identify targets was influenced by display format. Digital errors increased with the number of elements, the number of targets, and the distance of the target from the fixation point. Line orientation errors increased only with the number of targets. Several other display types were evaluated, and each produced a pattern of errors similar to either digital or line orientation format. Results of the study were discussed in terms of Feature Integration Theory, which distinguishes between elements that are processed with parallel versus serial mechanisms.

Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes

Science.gov (United States)

Ahmadzadeh, Ezat; Jaferzadeh, Keyvan; Lee, Jieun; Moon, Inkyu

2017-07-01

We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis.

Object width modulates object-based attentional selection.

Science.gov (United States)

Nah, Joseph C; Neppi-Modona, Marco; Strother, Lars; Behrmann, Marlene; Shomstein, Sarah

2018-04-24

Visual input typically includes a myriad of objects, some of which are selected for further processing. While these objects vary in shape and size, most evidence supporting object-based guidance of attention is drawn from paradigms employing two identical objects. Importantly, object size is a readily perceived stimulus dimension, and whether it modulates the distribution of attention remains an open question. Across four experiments, the size of the objects in the display was manipulated in a modified version of the two-rectangle paradigm. In Experiment 1, two identical parallel rectangles of two sizes (thin or thick) were presented. Experiments 2-4 employed identical trapezoids (each having a thin and thick end), inverted in orientation. In the experiments, one end of an object was cued and participants performed either a T/L discrimination or a simple target-detection task. Combined results show that, in addition to the standard object-based attentional advantage, there was a further attentional benefit for processing information contained in the thick versus thin end of objects. Additionally, eye-tracking measures demonstrated increased saccade precision towards thick object ends, suggesting that Fitts's Law may play a role in object-based attentional shifts. Taken together, these results suggest that object-based attentional selection is modulated by object width.

3-D FEATURE-BASED MATCHING BY RSTG APPROACH

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J.-J. Jaw

2012-07-01

Full Text Available 3-D feature matching is the essential kernel in a fully automated feature-based LiDAR point cloud registration. After feasible procedures of feature acquisition, connecting corresponding features in different data frames is imperative to be solved. The objective addressed in this paper is developing an approach coined RSTG to retrieve corresponding counterparts of unsorted multiple 3-D features extracted from sets of LiDAR point clouds. RSTG stands for the four major processes, "Rotation alignment"; "Scale estimation"; "Translation alignment" and "Geometric check," strategically formulated towards finding out matching solution with high efficiency and leading to accomplishing the 3-D similarity transformation among all sets. The workable types of features to RSTG comprise points, lines, planes and clustered point groups. Each type of features can be employed exclusively or combined with others, if sufficiently supplied, throughout the matching scheme. The paper gives a detailed description of the matching methodology and discusses on the matching effects based on the statistical assessment which revealed that the RSTG approach reached an average matching rate of success up to 93% with around 6.6% of statistical type 1 error. Notably, statistical type 2 error, the critical indicator of matching reliability, was kept 0% throughout all the experiments.

A Novel Object Tracking Algorithm Based on Compressed Sensing and Entropy of Information

Directory of Open Access Journals (Sweden)

Ding Ma

2015-01-01

Full Text Available Object tracking has always been a hot research topic in the field of computer vision; its purpose is to track objects with specific characteristics or representation and estimate the information of objects such as their locations, sizes, and rotation angles in the current frame. Object tracking in complex scenes will usually encounter various sorts of challenges, such as location change, dimension change, illumination change, perception change, and occlusion. This paper proposed a novel object tracking algorithm based on compressed sensing and information entropy to address these challenges. First, objects are characterized by the Haar (Haar-like and ORB features. Second, the dimensions of computation space of the Haar and ORB features are effectively reduced through compressed sensing. Then the above-mentioned features are fused based on information entropy. Finally, in the particle filter framework, an object location was obtained by selecting candidate object locations in the current frame from the local context neighboring the optimal locations in the last frame. Our extensive experimental results demonstrated that this method was able to effectively address the challenges of perception change, illumination change, and large area occlusion, which made it achieve better performance than existing approaches such as MIL and CT.

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Directory of Open Access Journals (Sweden)

Hanqing Zhao

2017-05-01

Full Text Available Abstract: Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of “vegetation recovery”. This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA. A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN, accumulative curvature slope (AC, and slope of slope (SOS were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and

A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

Science.gov (United States)

Lu, Wei; Li, Zhe; Chu, Jinghui

2017-04-01

Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers. We extracted morphological, various texture, and Gabor features. To clarify the feature subsets' physical meaning, subspaces are built by combining morphological features with each kind of texture or Gabor feature. We tested our proposal using a manually segmented Region of Interest (ROI) data set, which contains 438 images of malignant tumors and 1898 images of normal tissues or benign tumors. Our proposal achieves an area under the ROC curve (AUC) value of 0.9617, which outperforms most other state-of-the-art breast MRI CADx systems. Compared with other methods, our proposal significantly reduces the false-positive classification rate. Copyright © 2017 Elsevier Ltd. All rights reserved.

An automated approach for extracting Barrier Island morphology from digital elevation models

Science.gov (United States)

Wernette, Phillipe; Houser, Chris; Bishop, Michael P.

2016-06-01

The response and recovery of a barrier island to extreme storms depends on the elevation of the dune base and crest, both of which can vary considerably alongshore and through time. Quantifying the response to and recovery from storms requires that we can first identify and differentiate the dune(s) from the beach and back-barrier, which in turn depends on accurate identification and delineation of the dune toe, crest and heel. The purpose of this paper is to introduce a multi-scale automated approach for extracting beach, dune (dune toe, dune crest and dune heel), and barrier island morphology. The automated approach introduced here extracts the shoreline and back-barrier shoreline based on elevation thresholds, and extracts the dune toe, dune crest and dune heel based on the average relative relief (RR) across multiple spatial scales of analysis. The multi-scale automated RR approach to extracting dune toe, dune crest, and dune heel based upon relative relief is more objective than traditional approaches because every pixel is analyzed across multiple computational scales and the identification of features is based on the calculated RR values. The RR approach out-performed contemporary approaches and represents a fast objective means to define important beach and dune features for predicting barrier island response to storms. The RR method also does not require that the dune toe, crest, or heel are spatially continuous, which is important because dune morphology is likely naturally variable alongshore.

Biometric morphing: a novel technique for the analysis of morphologic outcomes after facial surgery.

Science.gov (United States)

Pahuta, Markian A; Mainprize, James G; Rohlf, F James; Antonyshyn, Oleh M

2009-01-01

The results of facial surgery are intuitively judged in terms of the visible changes in facial features or proportions. However, describing these morphologic outcomes objectively remains a challenge. Biometric morphing addresses this issue by merging statistical shape analysis and image processing. This study describes the implementation of biometric morphing in describing the average morphologic result of facial surgery. The biometric morphing protocol was applied to pre- and postoperative images of the following: (1) 40 dorsal hump reduction rhinoplasties and (2) 20 unilateral enophthalmos repairs. Pre- and postoperative average images (average morphs) were generated. The average morphs provided an objective rendering of nasal and periorbital morphology, which summarized the average features and extent of deformity in a population of patients. Subtle alterations in morphology after surgery, which would otherwise be difficult to identify or demonstrate, were clearly illustrated. Biometric morphing is an effective instrument for describing average facial morphology in a population of patients.

Electromagnetic Scattering by a Morphologically Complex Object: Fundamental Concepts and Common Misconceptions

Science.gov (United States)

Mischenko, Michael I.; Travis, Larry D.; Cairns, Brian; Tishkovets, Victor P.; Dlugach, Janna M.; Rosenbush, Vera K.; Kiselev, Nikolai N.

2011-01-01

Following Keller(Proc Symp Appl Math 1962;13:227:46), we classify all theoretical treatments of electromagnetic scattering by a morphologically complex object into first- principle (or "honest" in Keller s terminology) and phenomenological (or "dishonest") categories. This helps us identify, analyze, and dispel several profound misconceptions widespread in the discipline of electromagnetic scattering by solitary particles and discrete random media. Our goal is not to call for a complete renunciation of phenomenological approaches but rather to encourage a critical and careful evaluation of their actual origin, virtues, and limitations. In other words, we do not intend to deter creative thinking in terms of phenomenological short-cuts, but we do want to raise awareness when we stray (often for practical reasons) from the fundamentals. The main results and conclusions are illustrated by numerically-exact data based on direct numerical solutions of the macroscopic Maxwell equations.

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects

Science.gov (United States)

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

Directory of Open Access Journals (Sweden)

Tingquan Deng

2016-01-01

Full Text Available There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

Human listeners provide insights into echo features used by dolphins (Tursiops truncatus) to discriminate among objects.

Science.gov (United States)

Delong, Caroline M; Au, Whitlow W L; Harley, Heidi E; Roitblat, Herbert L; Pytka, Lisa

2007-08-01

Echolocating bottlenose dolphins (Tursiops truncatus) discriminate between objects on the basis of the echoes reflected by the objects. However, it is not clear which echo features are important for object discrimination. To gain insight into the salient features, the authors had a dolphin perform a match-to-sample task and then presented human listeners with echoes from the same objects used in the dolphin's task. In 2 experiments, human listeners performed as well or better than the dolphin at discriminating objects, and they reported the salient acoustic cues. The error patterns of the humans and the dolphin were compared to determine which acoustic features were likely to have been used by the dolphin. The results indicate that the dolphin did not appear to use overall echo amplitude, but that it attended to the pattern of changes in the echoes across different object orientations. Human listeners can quickly identify salient combinations of echo features that permit object discrimination, which can be used to generate hypotheses that can be tested using dolphins as subjects.

An open, object-based modeling approach for simulating subsurface heterogeneity

Science.gov (United States)

Bennett, J.; Ross, M.; Haslauer, C. P.; Cirpka, O. A.

2017-12-01

Characterization of subsurface heterogeneity with respect to hydraulic and geochemical properties is critical in hydrogeology as their spatial distribution controls groundwater flow and solute transport. Many approaches of characterizing subsurface heterogeneity do not account for well-established geological concepts about the deposition of the aquifer materials; those that do (i.e. process-based methods) often require forcing parameters that are difficult to derive from site observations. We have developed a new method for simulating subsurface heterogeneity that honors concepts of sequence stratigraphy, resolves fine-scale heterogeneity and anisotropy of distributed parameters, and resembles observed sedimentary deposits. The method implements a multi-scale hierarchical facies modeling framework based on architectural element analysis, with larger features composed of smaller sub-units. The Hydrogeological Virtual Reality simulator (HYVR) simulates distributed parameter models using an object-based approach. Input parameters are derived from observations of stratigraphic morphology in sequence type-sections. Simulation outputs can be used for generic simulations of groundwater flow and solute transport, and for the generation of three-dimensional training images needed in applications of multiple-point geostatistics. The HYVR algorithm is flexible and easy to customize. The algorithm was written in the open-source programming language Python, and is intended to form a code base for hydrogeological researchers, as well as a platform that can be further developed to suit investigators' individual needs. This presentation will encompass the conceptual background and computational methods of the HYVR algorithm, the derivation of input parameters from site characterization, and the results of groundwater flow and solute transport simulations in different depositional settings.

Variational Level Set Method for Two-Stage Image Segmentation Based on Morphological Gradients

Directory of Open Access Journals (Sweden)

Zemin Ren

2014-01-01

Full Text Available We use variational level set method and transition region extraction techniques to achieve image segmentation task. The proposed scheme is done by two steps. We first develop a novel algorithm to extract transition region based on the morphological gradient. After this, we integrate the transition region into a variational level set framework and develop a novel geometric active contour model, which include an external energy based on transition region and fractional order edge indicator function. The external energy is used to drive the zero level set toward the desired image features, such as object boundaries. Due to this external energy, the proposed model allows for more flexible initialization. The fractional order edge indicator function is incorporated into the length regularization term to diminish the influence of noise. Moreover, internal energy is added into the proposed model to penalize the deviation of the level set function from a signed distance function. The results evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed model has been applied to both synthetic and real images with promising results.

Feature-based automatic color calibration for networked camera system

Science.gov (United States)

Yamamoto, Shoji; Taki, Keisuke; Tsumura, Norimichi; Nakaguchi, Toshiya; Miyake, Yoichi

2011-01-01

In this paper, we have developed a feature-based automatic color calibration by using an area-based detection and adaptive nonlinear regression method. Simple color matching of chartless is achieved by using the characteristic of overlapping image area with each camera. Accurate detection of common object is achieved by the area-based detection that combines MSER with SIFT. Adaptive color calibration by using the color of detected object is calculated by nonlinear regression method. This method can indicate the contribution of object's color for color calibration, and automatic selection notification for user is performed by this function. Experimental result show that the accuracy of the calibration improves gradually. It is clear that this method can endure practical use of multi-camera color calibration if an enough sample is obtained.

Discriminative kernel feature extraction and learning for object recognition and detection

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2015-01-01

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...... even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting...... codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset....

Object based implicit contextual learning: a study of eye movements.

Science.gov (United States)

van Asselen, Marieke; Sampaio, Joana; Pina, Ana; Castelo-Branco, Miguel

2011-02-01

Implicit contextual cueing refers to a top-down mechanism in which visual search is facilitated by learned contextual features. In the current study we aimed to investigate the mechanism underlying implicit contextual learning using object information as a contextual cue. Therefore, we measured eye movements during an object-based contextual cueing task. We demonstrated that visual search is facilitated by repeated object information and that this reduction in response times is associated with shorter fixation durations. This indicates that by memorizing associations between objects in our environment we can recognize objects faster, thereby facilitating visual search.

Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

Science.gov (United States)

Hernandez-Contreras, D.; Peregrina-Barreto, H.; Rangel-Magdaleno, J.; Ramirez-Cortes, J.; Renero-Carrillo, F.

2015-11-01

This paper presents a novel approach to characterize and identify patterns of temperature in thermographic images of the human foot plant in support of early diagnosis and follow-up of diabetic patients. Composed feature vectors based on 3D morphological pattern spectrum (pecstrum) and relative position, allow the system to quantitatively characterize and discriminate non-diabetic (control) and diabetic (DM) groups. Non-linear classification using neural networks is used for that purpose. A classification rate of 94.33% in average was obtained with the composed feature extraction process proposed in this paper. Performance evaluation and obtained results are presented.

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

Directory of Open Access Journals (Sweden)

Xiaolong Liu

2015-01-01

Full Text Available Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR data in an object-based image analysis (OBIA paradigm. The aims of this work were the following: (i to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI with a 1 m spatial resolution and the Canopy Height Model (CHM data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR, VHR/CHM, and minimum noise fractional transformed data (MNF/CHM were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that

Bottlenose dolphins perceive object features through echolocation.

Science.gov (United States)

Harley, Heidi E; Putman, Erika A; Roitblat, Herbert L

2003-08-07

How organisms (including people) recognize distant objects is a fundamental question. The correspondence between object characteristics (distal stimuli), like visual shape, and sensory characteristics (proximal stimuli), like retinal projection, is ambiguous. The view that sensory systems are 'designed' to 'pick up' ecologically useful information is vague about how such mechanisms might work. In echolocating dolphins, which are studied as models for object recognition sonar systems, the correspondence between echo characteristics and object characteristics is less clear. Many cognitive scientists assume that object characteristics are extracted from proximal stimuli, but evidence for this remains ambiguous. For example, a dolphin may store 'sound templates' in its brain and identify whole objects by listening for a particular sound. Alternatively, a dolphin's brain may contain algorithms, derived through natural endowments or experience or both, which allow it to identify object characteristics based on sounds. The standard method used to address this question in many species is indirect and has led to equivocal results with dolphins. Here we outline an appropriate method and test it to show that dolphins extract object characteristics directly from echoes.

Learning based particle filtering object tracking for visible-light systems.

Science.gov (United States)

Sun, Wei

2015-10-01

We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

Science.gov (United States)

Bray, Mark-Anthony; Singh, Shantanu; Han, Han; Davis, Chadwick T.; Borgeson, Blake; Hartland, Cathy; Kost-Alimova, Maria; Gustafsdottir, Sigrun M.; Gibson, Christopher C.; Carpenter, Anne E.

2016-01-01

In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, a morphological profiling assay multiplexing six fluorescent dyes imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multi-well plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Then, automated image analysis software identifies individual cells and measures ~1,500 morphological features (various measures of size, shape, texture, intensity, etc.) to produce a rich profile suitable for detecting subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes two weeks; feature extraction and data analysis take an additional 1-2 weeks. PMID:27560178

Object-based Landslide Mapping: Examples, Challenges and Opportunities

Science.gov (United States)

Hölbling, Daniel; Eisank, Clemens; Friedl, Barbara; Chang, Kang-Tsung; Tsai, Tsai-Tsung; Birkefeldt Møller Pedersen, Gro; Betts, Harley; Cigna, Francesca; Chiang, Shou-Hao; Aubrey Robson, Benjamin; Bianchini, Silvia; Füreder, Petra; Albrecht, Florian; Spiekermann, Raphael; Weinke, Elisabeth; Blaschke, Thomas; Phillips, Chris

2016-04-01

Over the last decade, object-based image analysis (OBIA) has been increasingly used for mapping landslides that occur after triggering events such as heavy rainfall. The increasing availability and quality of Earth Observation (EO) data in terms of temporal, spatial and spectral resolution allows for comprehensive mapping of landslides at multiple scales. Most often very high resolution (VHR) or high resolution (HR) optical satellite images are used in combination with a digital elevation model (DEM) and its products such as slope and curvature. Semi-automated object-based mapping makes use of various characteristics of image objects that are derived through segmentation. OBIA enables numerous spectral, spatial, contextual and textural image object properties to be applied during an analysis. This is especially useful when mapping complex natural features such as landslides and constitutes an advantage over pixel-based image analysis. However, several drawbacks in the process of object-based landslide mapping have not been overcome yet. The developed classification routines are often rather complex and limited regarding their transferability across areas and sensors. There is still more research needed to further improve present approaches and to fully exploit the capabilities of OBIA for landslide mapping. In this study several examples of object-based landslide mapping from various geographical regions with different characteristics are presented. Examples from the Austrian and Italian Alps are shown, whereby one challenge lies in the detection of small-scale landslides on steep slopes while preventing the classification of false positives with similar spectral properties (construction areas, utilized land, etc.). Further examples feature landslides mapped in Iceland, where the differentiation of landslides from other landscape-altering processes in a highly dynamic volcanic landscape poses a very distinct challenge, and in Norway, which is exposed to multiple

Possibility of object recognition using Altera's model based design approach

International Nuclear Information System (INIS)

Tickle, A J; Harvey, P K; Smith, J S; Wu, F

2009-01-01

Object recognition is an image processing task of finding a given object in a selected image or video sequence. Object recognition can be divided into two areas: one of these is decision-theoretic and deals with patterns described by quantitative descriptors, for example such as length, area, shape and texture. With this Graphical User Interface Circuitry (GUIC) methodology employed here being relatively new for object recognition systems, the aim of this work is to identify if the developed circuitry can detect certain shapes or strings within the target image. A much smaller reference image feeds the preset data for identification, tests are conducted for both binary and greyscale and the additional mathematical morphology to highlight the area within the target image with the object(s) are located is also presented. This then provides proof that basic recognition methods are valid and would allow the progression to developing decision-theoretical and learning based approaches using GUICs for use in multidisciplinary tasks.

Morphologic Features of Kapıdağ Peninsula and its Coasts (NW-Turkey using by Remote Sensing and DTM

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Cem Gazioğlu

2014-11-01

Full Text Available Although it is an inland sea, the Sea of Marmara and its surroundings have rather complex morphology due to the active tectonics of the North Anatolian Fault (NAF zone in this region. The Kapıdağ Peninsula which is located at its southern coasts also represents a complex morphology. Macro morphologic units of Kapıdağ Peninsula are N-S trending deep valley systems, mountain areas and Belkıs isthmus. The most coastal area of peninsula has terraces, coastal plains and alluvial valley floors. These unique morphologic features can explain some parameters of active tectonics of the Sea of Marmara region. In order to investigate these geomorphologic features of the Kapıdağ peninsula in detail, some land observations, satellite data, Digital Terrain Models (DTMs which have been evaluated from topographic maps with a vertical precision of ±3m and cell size of 8m, Geographic Information Systems (GIS, traditional methods and some integrated techniques such as image processing were used.

A bio-inspired method and system for visual object-based attention and segmentation

Science.gov (United States)

Huber, David J.; Khosla, Deepak

2010-04-01

This paper describes a method and system of human-like attention and object segmentation in visual scenes that (1) attends to regions in a scene in their rank of saliency in the image, (2) extracts the boundary of an attended proto-object based on feature contours, and (3) can be biased to boost the attention paid to specific features in a scene, such as those of a desired target object in static and video imagery. The purpose of the system is to identify regions of a scene of potential importance and extract the region data for processing by an object recognition and classification algorithm. The attention process can be performed in a default, bottom-up manner or a directed, top-down manner which will assign a preference to certain features over others. One can apply this system to any static scene, whether that is a still photograph or imagery captured from video. We employ algorithms that are motivated by findings in neuroscience, psychology, and cognitive science to construct a system that is novel in its modular and stepwise approach to the problems of attention and region extraction, its application of a flooding algorithm to break apart an image into smaller proto-objects based on feature density, and its ability to join smaller regions of similar features into larger proto-objects. This approach allows many complicated operations to be carried out by the system in a very short time, approaching real-time. A researcher can use this system as a robust front-end to a larger system that includes object recognition and scene understanding modules; it is engineered to function over a broad range of situations and can be applied to any scene with minimal tuning from the user.

Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

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Lei Qin

2014-05-01

Full Text Available We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.

A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

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Zhaowen Chen

2014-01-01

Full Text Available Mathematical morphology (MM is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE. Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

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Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

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Dhanoa Jaspreet Singh

2016-01-01

Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

Feature-based component model for design of embedded systems

Science.gov (United States)

Zha, Xuan Fang; Sriram, Ram D.

2004-11-01

An embedded system is a hybrid of hardware and software, which combines software's flexibility and hardware real-time performance. Embedded systems can be considered as assemblies of hardware and software components. An Open Embedded System Model (OESM) is currently being developed at NIST to provide a standard representation and exchange protocol for embedded systems and system-level design, simulation, and testing information. This paper proposes an approach to representing an embedded system feature-based model in OESM, i.e., Open Embedded System Feature Model (OESFM), addressing models of embedded system artifacts, embedded system components, embedded system features, and embedded system configuration/assembly. The approach provides an object-oriented UML (Unified Modeling Language) representation for the embedded system feature model and defines an extension to the NIST Core Product Model. The model provides a feature-based component framework allowing the designer to develop a virtual embedded system prototype through assembling virtual components. The framework not only provides a formal precise model of the embedded system prototype but also offers the possibility of designing variation of prototypes whose members are derived by changing certain virtual components with different features. A case study example is discussed to illustrate the embedded system model.

Update on Anaplastic Thyroid Carcinoma: Morphological, Molecular, and Genetic Features of the Most Aggressive Thyroid Cancer

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Moira Ragazzi

2014-01-01

Full Text Available Anaplastic thyroid carcinoma (ATC is the most aggressive form of thyroid cancer. It shows a wide spectrum of morphological presentations and the diagnosis could be challenging due to its high degree of dedifferentiation. Molecular and genetic features of ATC are widely heterogeneous as well and many efforts have been made to find a common profile in order to clarify its cancerogenetic process. A comprehensive review of the current literature is here performed, focusing on histopathological and genetic features.

Object-based Encoding in Visual Working Memory: Evidence from Memory-driven Attentional Capture

OpenAIRE

Gao, Zaifeng; Yu, Shixian; Zhu, Chengfeng; Shui, Rende; Weng, Xuchu; Li, Peng; Shen, Mowei

2016-01-01

Visual working memory (VWM) adopts a specific manner of object-based encoding (OBE) to extract perceptual information: Whenever one feature-dimension is selected for entry into VWM, the others are also extracted. Currently most studies revealing OBE probed an ?irrelevant-change distracting effect?, where changes of irrelevant-features dramatically affected the performance of the target feature. However, the existence of irrelevant-feature change may affect participants? processing manner, lea...

Morphological features of Camarosporium pini – the fungus associated to health state degradation in Austrian and Ponderosa pine

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Ivanová Helena

2017-06-01

Full Text Available The subject of this study is escalated occurrence of the pathogenic fungus Camarosporium pini in the needle tissue of symptomatic trees P. nigra and P. ponderosa var. jeffreyi growing in urbanized settings and parks. C. pini induces severe infections and initiates a blight and premature loss of second-year foliage in pine trees. The fungus was identified microscopically and on base of morphological keys. The affected needles displayed a distinct bluish-grey necrotic band in the centre. On the surface of infected needles, there were formed pycnidia producing brown, oval conidia with three transversal and one or two vertical walls. Disease symptoms, some important characteristics in pure culture, and distinctive morphological features of C. pini associated to the health state degradation in Austrian and Ponderosa pine are described and compared. Cumulative effects of these stressful biotic and various abiotic factors may explain the current situation concerning the decline in the P. nigra and P. ponderosa var. jeffreyi in Slovakia.

Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis

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Sachit Rajbhandari

2017-11-01

Full Text Available In Geographic Object-based Image Analysis (GEOBIA, identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation vocabulary for characterising domain-specific classes. This study proposes an ontological framework that conceptualises domain knowledge in order to support the application of rule-based classifications. The proposed ontological framework is tested with a landslide case study. The Web Ontology Language (OWL is used to construct an ontology in the landslide domain. The segmented image objects with extracted features are incorporated into the ontology as instances. The classification rules are written in Semantic Web Rule Language (SWRL and executed using a semantic reasoner to assign instances to appropriate landslide classes. Machine learning techniques are used to predict new threshold values for feature attributes in the rules. Our framework is compared with published work on landslide detection where ontology was not used for the image classification. Our results demonstrate that a classification derived from the ontological framework accords with non-ontological methods. This study benchmarks the ontological method providing an alternative approach for image classification in the case study of landslides.

Invariant object recognition based on the generalized discrete radon transform

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Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses

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Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-01

Background Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. Objective The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Methods Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Results Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however

Object-based Encoding in Visual Working Memory: Evidence from Memory-driven Attentional Capture.

Science.gov (United States)

Gao, Zaifeng; Yu, Shixian; Zhu, Chengfeng; Shui, Rende; Weng, Xuchu; Li, Peng; Shen, Mowei

2016-03-09

Visual working memory (VWM) adopts a specific manner of object-based encoding (OBE) to extract perceptual information: Whenever one feature-dimension is selected for entry into VWM, the others are also extracted. Currently most studies revealing OBE probed an 'irrelevant-change distracting effect', where changes of irrelevant-features dramatically affected the performance of the target feature. However, the existence of irrelevant-feature change may affect participants' processing manner, leading to a false-positive result. The current study conducted a strict examination of OBE in VWM, by probing whether irrelevant-features guided the deployment of attention in visual search. The participants memorized an object's colour yet ignored shape and concurrently performed a visual-search task. They searched for a target line among distractor lines, each embedded within a different object. One object in the search display could match the shape, colour, or both dimensions of the memory item, but this object never contained the target line. Relative to a neutral baseline, where there was no match between the memory and search displays, search time was significantly prolonged in all match conditions, regardless of whether the memory item was displayed for 100 or 1000 ms. These results suggest that task-irrelevant shape was extracted into VWM, supporting OBE in VWM.

Activity in human visual and parietal cortex reveals object-based attention in working memory.

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Peters, Benjamin; Kaiser, Jochen; Rahm, Benjamin; Bledowski, Christoph

2015-02-25

Visual attention enables observers to select behaviorally relevant information based on spatial locations, features, or objects. Attentional selection is not limited to physically present visual information, but can also operate on internal representations maintained in working memory (WM) in service of higher-order cognition. However, only little is known about whether attention to WM contents follows the same principles as attention to sensory stimuli. To address this question, we investigated in humans whether the typically observed effects of object-based attention in perception are also evident for object-based attentional selection of internal object representations in WM. In full accordance with effects in visual perception, the key behavioral and neuronal characteristics of object-based attention were observed in WM. Specifically, we found that reaction times were shorter when shifting attention to memory positions located on the currently attended object compared with equidistant positions on a different object. Furthermore, functional magnetic resonance imaging and multivariate pattern analysis of visuotopic activity in visual (areas V1-V4) and parietal cortex revealed that directing attention to one position of an object held in WM also enhanced brain activation for other positions on the same object, suggesting that attentional selection in WM activates the entire object. This study demonstrated that all characteristic features of object-based attention are present in WM and thus follows the same principles as in perception. Copyright © 2015 the authors 0270-6474/15/353360-10$15.00/0.

Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories

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Takahiro Soshi

2017-09-01

Full Text Available Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, and fish, along with 195 verbal features. Healthy adults participated in memory-based feature-animal matching verification tests. Analyses included a hierarchical clustering analysis, support vector machine, and independent component analysis to specify features effective for classifications. Quantitative and qualitative comparisons for significant features were conducted between super-ordinate and sub-ordinate levels. The number of significant features was larger for super-ordinate than sub-ordinate levels. Qualitatively, the proportion of biological features was larger than cultural/affective features in both the levels, while the proportion of affective features increased at the sub-ordinate level. To summarize, the two types of features differentially function to establish category representations.

CoMIC: Good features for detection and matching at object boundaries

OpenAIRE

Ravindran, Swarna Kamlam; Mittal, Anurag

2014-01-01

Feature or interest points typically use information aggregation in 2D patches which does not remain stable at object boundaries when there is object motion against a significantly varying background. Level or iso-intensity curves are much more stable under such conditions, especially the longer ones. In this paper, we identify stable portions on long iso-curves and detect corners on them. Further, the iso-curve associated with a corner is used to discard portions from the background and impr...

[Morphological features of stromal-vascular component of the thymus of stillborn children and children under one year of life from mothers that do not follow a healthy lifestyle

OpenAIRE

Gorianikova I.N.

2015-01-01

Background. Morphofunctional state of the thymus of child in most cases is directly dependent on the mother health and her lifestyle. Objective. The purpose of the research was to reveal the morphological features of stromal-vascular component of the thymus of stillborn children and children under one year of life born from women who conducted a sedentary lifestyle, smoked, drank alcohol and ate the foods containing tartrazine. Methods. The material of the study was 67 thymuses of stillborn c...

An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

KAUST Repository

Soufan, Othman

2012-09-01

Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.

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Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.

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Bo Liu

Full Text Available Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1 using directional mathematical morphology to enhance the contrast between roads and non-roads; (2 using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Science.gov (United States)

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

Perceptual grouping and attention in visual search for features and for objects.

Science.gov (United States)

Treisman, A

1982-04-01

This article explores the effects of perceptual grouping on search for targets defined by separate features or by conjunction of features. Treisman and Gelade proposed a feature-integration theory of attention, which claims that in the absence of prior knowledge, the separable features of objects are correctly combined only when focused attention is directed to each item in turn. If items are preattentively grouped, however, attention may be directed to groups rather than to single items whenever no recombination of features within a group could generate an illusory target. This prediction is confirmed: In search for conjunctions, subjects appear to scan serially between groups rather than items. The scanning rate shows little effect of the spatial density of distractors, suggesting that it reflects serial fixations of attention rather than eye movements. Search for features, on the other hand, appears to independent of perceptual grouping, suggesting that features are detected preattentively. A conjunction target can be camouflaged at the preattentive level by placing it at the boundary between two adjacent groups, each of which shares one of its features. This suggests that preattentive grouping creates separate feature maps within each separable dimension rather than one global configuration.

Single-Grasp Object Classification and Feature Extraction with Simple Robot Hands and Tactile Sensors.

Science.gov (United States)

Spiers, Adam J; Liarokapis, Minas V; Calli, Berk; Dollar, Aaron M

2016-01-01

Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays, and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a 'haptic glance'). In this work, we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (eight per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp-release, or force modulation and works for arbitrary object start positions and orientations. Due to these factors, the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.

Extracting Spatiotemporal Objects from Raster Data to Represent Physical Features and Analyze Related Processes

Science.gov (United States)

Zollweg, J. A.

2017-10-01

Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don't see millions of cubes of atmosphere; we see a thunderstorm `object'. Temporally, we don't see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain's perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA's High-Resolution Rapid Refresh v2 (HRRRv2) data stream.

Time course of spatial and feature selective attention for partly-occluded objects.

Science.gov (United States)

Kasai, Tetsuko; Takeya, Ryuji

2012-07-01

Attention selects objects/groups as the most fundamental units, and this may be achieved by an attention-spreading mechanism. Previous event-related potential (ERP) studies have found that attention-spreading is reflected by a decrease in the N1 spatial attention effect. The present study tested whether the electrophysiological attention effect is associated with the perception of object unity or amodal completion through the use of partly-occluded objects. ERPs were recorded in 14 participants who were required to pay attention to their left or right visual field and to press a button for a target shape in the attended field. Bilateral stimuli were presented rapidly, and were separated, connected, or connected behind an occluder. Behavioral performance in the connected and occluded conditions was worse than that in the separated condition, indicating that attention spread over perceptual object representations after amodal completion. Consistently, the late N1 spatial attention effect (180-220 ms post-stimulus) and the early phase (230-280 ms) of feature selection effects (target N2) at contralateral sites decreased, equally for the occluded and connected conditions, while the attention effect in the early N1 latency (140-180 ms) shifted most positively for the occluded condition. These results suggest that perceptual organization processes for object recognition transiently modulate spatial and feature selection processes in the visual cortex. Copyright © 2012 Elsevier Ltd. All rights reserved.

ENHANCED NITROGEN IN MORPHOLOGICALLY DISTURBED BLUE COMPACT GALAXIES AT 0.20 < z < 0.35: PROBING GALAXY MERGING FEATURES

Energy Technology Data Exchange (ETDEWEB)

Chung, Jiwon; Rey, Soo-Chang; Yeom, Bum-Suk; Yi, Wonhyeong [Department of Astronomy and Space Science, Chungnam National University, Daejeon 305-764 (Korea, Republic of); Sung, Eon-Chang; Kyeong, Jaemann [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of); Humphrey, Andrew, E-mail: jiwon@cnu.ac.kr, E-mail: screy@cnu.ac.kr [Centro de Astrofisica da Universidade do Porto, Rua das Estrelas, 4150-762, Porto (Portugal)

2013-04-10

We present a study of correlations between the elemental abundances and galaxy morphologies of 91 blue compact galaxies (BCGs) at z = 0.20-0.35 with Sloan Digital Sky Survey (SDSS) DR7 data. We classify the morphologies of the galaxies as either ''disturbed'' or ''undisturbed'' by visual inspection of the SDSS images, and using the Gini coefficient and M{sub 20}. We derive oxygen and nitrogen abundances using the T{sub e} method. We find that a substantial fraction of BCGs with disturbed morphologies, indicative of merger remnants, show relatively high N/O and low O/H abundance ratios. The majority of the disturbed BCGs exhibit higher N/O values at a given O/H value compared to the morphologically undisturbed galaxies, implying more efficient nitrogen enrichment in disturbed BCGs. We detect Wolf-Rayet (WR) features in only a handful of the disturbed BCGs, which appears to contradict the idea that WR stars are responsible for high nitrogen abundance. Combining these results with Galaxy Evolution Explorer GR6 ultraviolet (UV) data, we find that the majority of the disturbed BCGs show systematically lower values of the H{alpha} to near-UV star formation rate ratio. The equivalent width of the H{beta} emission line is also systematically lower in the disturbed BCGs. Based on these results, we infer that disturbed BCGs have undergone star formation over relatively longer timescales, resulting in a more continuous enrichment of nitrogen. We suggest that this correlation between morphology and chemical abundances in BCGs is due to a difference in their recent star formation histories.

Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on ROI analysis?

Science.gov (United States)

Dostal, P.; Krasula, L.; Klima, M.

2012-06-01

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.

A Solution to the Square-Rectangle Problem Within the Framework of Object Morphology

Directory of Open Access Journals (Sweden)

Zbyněk Šlajchrt

2016-06-01

Full Text Available The square-rectangle problem is often cited as an illustration of pitfalls arising when using object-oriented programming (OOP. A number of solutions have been proposed, however, according to the author, none of them solve the problem satisfactorily, mainly because they tackle the problem from within the current OOP paradigm. This paper presents another solution stemming from object morphology (OM, a new object-oriented paradigm developed to model mutable phenomena. In the framework of OM the problem can be solved directly under the basic OM principle that an object may mutate not only with regard to its state, but also with regard to its type. The main contrast between the presented and the other solutions is that constraint violations caused by changes in an object’s state are no longer necessarily considered errors; instead, they may be interpreted as triggers initiating a mutation of the object’s type. The solution is demonstrated using Morpheus, a proof-of-concept implementation of OM in Scala.

Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Directory of Open Access Journals (Sweden)

Pachet François

2009-01-01

Full Text Available We present a feature generation system designed to create audio features for supervised classification tasks. The main contribution to feature generation studies is the notion of analytical features (AFs, a construct designed to support the representation of knowledge about audio signal processing. We describe the most important aspects of AFs, in particular their dimensional type system, on which are based pattern-based random generators, heuristics, and rewriting rules. We show how AFs generalize or improve previous approaches used in feature generation. We report on several projects using AFs for difficult audio classification tasks, demonstrating their advantage over standard audio features. More generally, we propose analytical features as a paradigm to bring raw signals into the world of symbolic computation.

Carcinoid of the ampulla of Vater: Morphologic features and clinical implications

Institute of Scientific and Technical Information of China (English)

George A Poultsides; Wayne AI Frederick

2006-01-01

Carcinoids involving the ampulla of Vater are rare lesions that may produce painless jaundice. The published data indicate that these tumors, in contrast to their midgut counterparts, metastasize in approximately half of cases irrespective of primary tumor size. Therefore,radical excision in the form of pancreaticoduodenectomy is recommended regardless of tumor size. As with other gastrointestinal carcinoid tumors, biological treatment with octreotide analogues can be applied to symptomatic patients. Tumor-targeted radioactive therapy is a newly emerging treatment option. We here report case of a carcinoid tumor of the ampulla of Vater presenting as painless jaundice in a 65-year old man and review the relevant literature, giving special attention to the morphologic features, clinical characteristics, and treatment modalities associated with this disease process.

Automatic extraction of corpus callosum from midsagittal head MR image and examination of Alzheimer-type dementia objective diagnostic system in feature analysis

International Nuclear Information System (INIS)

Kaneko, Tomoyuki; Kodama, Naoki; Kaeriyama, Tomoharu; Fukumoto, Ichiro

2004-01-01

We studied the objective diagnosis of Alzheimer-type dementia based on changes in the corpus callosum. We examined midsagittal head MR images of 40 Alzheimer-type dementia patients (15 men and 25 women; mean age, 75.4±5.5 years) and 31 healthy elderly persons (10 men and 21 women; mean age, 73.4±7.5 years), 71 subjects altogether. First, the corpus callosum was automatically extracted from midsagittal head MR images. Next, Alzheimer-type dementia was compared with the healthy elderly individuals using the features of shape factor and six features of Co-occurrence Matrix from the corpus callosum. Automatic extraction of the corpus callosum succeeded in 64 of 71 individuals, for an extraction rate of 90.1%. A statistically significant difference was found in 7 of the 9 features between Alzheimer-type dementia patients and the healthy elderly adults. Discriminant analysis using the 7 features demonstrated a sensitivity rate of 82.4%, specificity of 89.3%, and overall accuracy of 85.5%. These results indicated the possibility of an objective diagnostic system for Alzheimer-type dementia using feature analysis based on change in the corpus callosum. (author)

Communication target object recognition for D2D connection with feature size limit

Science.gov (United States)

Ok, Jiheon; Kim, Soochang; Kim, Young-hoon; Lee, Chulhee

2015-03-01

Recently, a new concept of device-to-device (D2D) communication, which is called "point-and-link communication" has attracted great attentions due to its intuitive and simple operation. This approach enables user to communicate with target devices without any pre-identification information such as SSIDs, MAC addresses by selecting the target image displayed on the user's own device. In this paper, we present an efficient object matching algorithm that can be applied to look(point)-and-link communications for mobile services. Due to the limited channel bandwidth and low computational power of mobile terminals, the matching algorithm should satisfy low-complexity, low-memory and realtime requirements. To meet these requirements, we propose fast and robust feature extraction by considering the descriptor size and processing time. The proposed algorithm utilizes a HSV color histogram, SIFT (Scale Invariant Feature Transform) features and object aspect ratios. To reduce the descriptor size under 300 bytes, a limited number of SIFT key points were chosen as feature points and histograms were binarized while maintaining required performance. Experimental results show the robustness and the efficiency of the proposed algorithm.

Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer

International Nuclear Information System (INIS)

Montemurro, Filippo; Redana, Stefania; Aglietta, Massimo; Martincich, Laura; Bertotto, Ilaria; Cellini, Lisa; Sarotto, Ivana; Ponzone, Riccardo; Sismondi, Piero; Regge, Daniele

2007-01-01

We studied whether dynamic contrast-enhanced MRI (DCE-MRI) could identify histopathological characteristics of breast cancer. Seventy-five patients with breast cancer underwent DCE-MRI followed by core biopsy. DCE-MRI findings were evaluated following the scoring system published by Fischer in 1999. In this scoring system, five DCE-MRI features, three morphological (shape, margins, enhancement kinetic) and two functional (initial peak of signal intensity (SI) increase and behavior of signal intensity curve), are defined by 14 parameters. Each parameter is assigned points ranging from 0 to 1 or 0 to 2, with higher points for those that are more likely to be associated with malignancy. The sum of all the points defines the degree of suspicion of malignancy, with a score 0 representing the lowest and 8 the highest degree of suspicion. Associations between DCE-MRI features and tumor histopathological characteristics assessed on core biopsies (histological type, grading, estrogen and progesterone receptor status, Ki67 and HER2 status) were studied by contingency tables and logistic regression analysis. We found a significant inverse association between the Fischer's score and HER2-overexpression (odds ratio-OR 0.608, p = 0.02). Based on our results, we suggest that lesions with intermediate-low suspicious DCE-MRI parameters may represent a subset of tumor with poor histopathological characteristics. (orig.)

Morphology of a Wetland Stream

Science.gov (United States)

Jurmu; Andrle

1997-11-01

/ Little attention has been paid to wetland stream morphology in the geomorphological and environmental literature, and in the recently expanding wetland reconstruction field, stream design has been based primarily on stream morphologies typical of nonwetland alluvial environments. Field investigation of a wetland reach of Roaring Brook, Stafford, Connecticut, USA, revealed several significant differences between the morphology of this stream and the typical morphology of nonwetland alluvial streams. Six morphological features of the study reach were examined: bankfull flow, meanders, pools and riffles, thalweg location, straight reaches, and cross-sectional shape. It was found that bankfull flow definitions originating from streams in nonwetland environments did not apply. Unusual features observed in the wetland reach include tight bends and a large axial wavelength to width ratio. A lengthy straight reach exists that exceeds what is typically found in nonwetland alluvial streams. The lack of convex bank point bars in the bends, a greater channel width at riffle locations, an unusual thalweg location, and small form ratios (a deep and narrow channel) were also differences identified. Further study is needed on wetland streams of various regions to determine if differences in morphology between alluvial and wetland environments can be applied in order to improve future designs of wetland channels.KEY WORDS: Stream morphology; Wetland restoration; Wetland creation; Bankfull; Pools and riffles; Meanders; Thalweg

[Improvement of rosacea treatment based on the morphological and functional features of the skin].

Science.gov (United States)

Tsiskarishvili, N V; Katsitadze, A G; Tsiskarishvili, Ts I

2013-10-01

Rosacea - a widespread disease sometimes aleak with severe complications, mainly affecting the skin. Irrational and inadequate treatment leads to chronicity of diseases and psychosocial disadaptation of patients. Lately, a clear upward trend in the number of patients in whom in the process of complex treatment manifestations (with the varying degrees of severity) of impaired barrier function of the skin are observed and they need the protection and restoration of the damaged stratum corneum. In patients with rosacea in order to study the function of the facial skin's horny layer we used the skin analyzer BIA (bioimpedance analysis, which in duration of 6 seconds determines the moisture content, oiliness and the softness of the skin) and significant deviations from the norm (decrease in moisture content, fatness and increased roughness) was revealed. These changes were most clearly pronounced in patients with steroid rosacea. To restore the skin barrier the drug "Episofit A" (Laboratory of Evolutionary Dermatology, France) has been used (1-2 times a day for 6 weeks). Evaluation of treatment efficacy was conducted every 2 weeks by means of a scale from 0 to 5 for parameters of dryness, erythema, peeling and expression of subjective feelings. In accordance with received results, using of Episofit A emulsion, especially on the baсkground of long-term treatment with topical steroids, had a pronounced therapeutic effect. Thus, treatment of patients with consideration of morphological and functional features of facial skin, helps to improve the results traditional therapy, and the drug is highly effective means of the new direction in skin care - corneotherapy aimed to reconstruct and protect damaged stratum corneum.

Proto-object categorisation and local gist vision using low-level spatial features.

Science.gov (United States)

Martins, Jaime A; Rodrigues, J M F; du Buf, J M H

2015-09-01

Object categorisation is a research area with significant challenges, especially in conditions with bad lighting, occlusions, different poses and similar objects. This makes systems that rely on precise information unable to perform efficiently, like a robotic arm that needs to know which objects it can reach. We propose a biologically inspired object detection and categorisation framework that relies on robust low-level object shape. Using only edge conspicuity and disparity features for scene figure-ground segregation and object categorisation, a trained neural network classifier can quickly categorise broad object families and consequently bootstrap a low-level scene gist system. We argue that similar processing is possibly located in the parietal pathway leading to the LIP cortex and, via areas V5/MT and MST, providing useful information to the superior colliculus for eye and head control. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

In vitro fertilisation when normal sperm morphology is less than ...

African Journals Online (AJOL)

The outcome of in vitro fertilisation and embryo transfer in 90 couples where the husband's normal sperm morphology was less than 15% were analysed. Based on the percentage of morphologically normal spermatozoa the patients were divided into three groups: group A - normal morphological features 0 - 5%; group B - 6 ...

Common and differential electrophysiological mechanisms underlying semantic object memory retrieval probed by features presented in different stimulus types.

Science.gov (United States)

Chiang, Hsueh-Sheng; Eroh, Justin; Spence, Jeffrey S; Motes, Michael A; Maguire, Mandy J; Krawczyk, Daniel C; Brier, Matthew R; Hart, John; Kraut, Michael A

2016-08-01

How the brain combines the neural representations of features that comprise an object in order to activate a coherent object memory is poorly understood, especially when the features are presented in different modalities (visual vs. auditory) and domains (verbal vs. nonverbal). We examined this question using three versions of a modified Semantic Object Retrieval Test, where object memory was probed by a feature presented as a written word, a spoken word, or a picture, followed by a second feature always presented as a visual word. Participants indicated whether each feature pair elicited retrieval of the memory of a particular object. Sixteen subjects completed one of the three versions (N=48 in total) while their EEG were recorded simultaneously. We analyzed EEG data in four separate frequency bands (delta: 1-4Hz, theta: 4-7Hz; alpha: 8-12Hz; beta: 13-19Hz) using a multivariate data-driven approach. We found that alpha power time-locked to response was modulated by both cross-modality (visual vs. auditory) and cross-domain (verbal vs. nonverbal) probing of semantic object memory. In addition, retrieval trials showed greater changes in all frequency bands compared to non-retrieval trials across all stimulus types in both response-locked and stimulus-locked analyses, suggesting dissociable neural subcomponents involved in binding object features to retrieve a memory. We conclude that these findings support both modality/domain-dependent and modality/domain-independent mechanisms during semantic object memory retrieval. Copyright © 2016 Elsevier B.V. All rights reserved.

Morphological and physiological features of Arthroderma benhamiae anamorphs isolated in northern Germany.

Science.gov (United States)

Brasch, Jochen; Wodarg, Svea

2015-02-01

The anamorph of Arthroderma benhamiae is an upcoming zoophilic dermatophyte that only in recent years has gained importance as a cause of tinea in humans. Its identification by conventional methods can cause problems. In this study we have subjected seven genetically confirmed strains of A. benhamiae anamorphs from northern Germany recently identified in our laboratory to a comprehensive assessment. Their macroscopic and microscopic morphology was checked on various agars and enzyme release stimulated by substrates with keratin, hair perforation and other physiological characteristics were tested. All strains were related to the previously described yellow phenotype of the A. benhamiae anamorph and showed a high resemblance among themselves. Coherent features were their uniform thallus morphology on Sabouraud glucose agar with yellow pigmentation, the formation of circuit-like hyphal structures and hyphal connections that had not been described previously, a lack of conidia, thiamine dependence, the spectrum of released enzymes and a good growth on human stratum corneum. With exception of the latter two these criteria are suggested for the identification of this anamorph phenotype that should be evaluated by future observations. Different phenotypes of the A. benhamiae anamorph may prevail in other geographic regions. © 2014 Blackwell Verlag GmbH.

EXTRACTING SPATIOTEMPORAL OBJECTS FROM RASTER DATA TO REPRESENT PHYSICAL FEATURES AND ANALYZE RELATED PROCESSES

Directory of Open Access Journals (Sweden)

J. A. Zollweg

2017-10-01

Full Text Available Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don’t see millions of cubes of atmosphere; we see a thunderstorm ‘object’. Temporally, we don’t see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain’s perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA’s High-Resolution Rapid Refresh v2 (HRRRv2 data stream.

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

Science.gov (United States)

Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

2013-01-01

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

Music preferences based on audio features, and its relation to personality

OpenAIRE

Dunn, Greg

2009-01-01

Recent studies have summarized reported music preferences by genre into four broadly defined categories, which relate to various personality characteristics. Other research has indicated that genre classification is ambiguous and inconsistent. This ambiguity suggests that research relating personality to music preferences based on genre could benefit from a more objective definition of music. This problem is addressed by investigating how music preferences linked to objective audio features r...

Real-time estimation of optical flow based on optimized haar wavelet features

DEFF Research Database (Denmark)

Salmen, Jan; Caup, Lukas; Igel, Christian

2011-01-01

-objective optimization. In this work, we build on a popular algorithm developed for realtime applications. It is originally based on the Census transform and benefits from this encoding for table-based matching and tracking of interest points. We propose to use the more universal Haar wavelet features instead...

Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition

Science.gov (United States)

Khayat, Omid; Afarideh, Hossein

2013-04-01

Track counting algorithms as one of the fundamental principles of nuclear science have been emphasized in the recent years. Accurate measurement of nuclear tracks on solid-state nuclear track detectors is the aim of track counting systems. Commonly track counting systems comprise a hardware system for the task of imaging and software for analysing the track images. In this paper, a track recognition algorithm based on 12 defined textual and shape-based features and a neuro-fuzzy classifier is proposed. Features are defined so as to discern the tracks from the background and small objects. Then, according to the defined features, tracks are detected using a trained neuro-fuzzy system. Features and the classifier are finally validated via 100 Alpha track images and 40 training samples. It is shown that principle textual and shape-based features concomitantly yield a high rate of track detection compared with the single-feature based methods.

Validation of Underwater Sensor Package Using Feature Based SLAM

Directory of Open Access Journals (Sweden)

Christopher Cain

2016-03-01

Full Text Available Robotic vehicles working in new, unexplored environments must be able to locate themselves in the environment while constructing a picture of the objects in the environment that could act as obstacles that would prevent the vehicles from completing their desired tasks. In enclosed environments, underwater range sensors based off of acoustics suffer performance issues due to reflections. Additionally, their relatively high cost make them less than ideal for usage on low cost vehicles designed to be used underwater. In this paper we propose a sensor package composed of a downward facing camera, which is used to perform feature tracking based visual odometry, and a custom vision-based two dimensional rangefinder that can be used on low cost underwater unmanned vehicles. In order to examine the performance of this sensor package in a SLAM framework, experimental tests are performed using an unmanned ground vehicle and two feature based SLAM algorithms, the extended Kalman filter based approach and the Rao-Blackwellized, particle filter based approach, to validate the sensor package.

Validation of Underwater Sensor Package Using Feature Based SLAM

Science.gov (United States)

Cain, Christopher; Leonessa, Alexander

2016-01-01

Robotic vehicles working in new, unexplored environments must be able to locate themselves in the environment while constructing a picture of the objects in the environment that could act as obstacles that would prevent the vehicles from completing their desired tasks. In enclosed environments, underwater range sensors based off of acoustics suffer performance issues due to reflections. Additionally, their relatively high cost make them less than ideal for usage on low cost vehicles designed to be used underwater. In this paper we propose a sensor package composed of a downward facing camera, which is used to perform feature tracking based visual odometry, and a custom vision-based two dimensional rangefinder that can be used on low cost underwater unmanned vehicles. In order to examine the performance of this sensor package in a SLAM framework, experimental tests are performed using an unmanned ground vehicle and two feature based SLAM algorithms, the extended Kalman filter based approach and the Rao-Blackwellized, particle filter based approach, to validate the sensor package. PMID:26999142

Generation of 3D Virtual Geographic Environment Based on Laser Scanning Technique

Institute of Scientific and Technical Information of China (English)

DU Jie; CHEN Xiaoyong; FumioYamazaki

2003-01-01

This paper demonstrates an experiment on the generation of 3D virtual geographic environment on the basis of experimental flight laser scanning data by a set of algorithms and methods that were developed to automatically interpret range images for extracting geo-spatial features and then to reconstruct geo-objects. The algorithms and methods for the interpretation and modeling of laser scanner data include triangulated-irregular-network (TIN)-based range image interpolation ; mathematical-morphology(MM)-based range image filtering,feature extraction and range image segmentation, feature generalization and optimization, 3D objects reconstruction and modeling; computergraphics (CG)-based visualization and animation of geographic virtual reality environment.

Mobile object retrieval in server-based image databases

Science.gov (United States)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

The concepts on which a morphology of the vascular plants should be based

NARCIS (Netherlands)

Bremekamp, C.E.B.

1956-01-01

It can hardly be denied that the expression “General Plant Morphology”, which is so often met with in botanical textbooks, has little or no meaning. A general morphology of the Plant Kingdom would have to occupy itself with those morphological features that are common to all groups of plants, which

Stream/Bounce Event Perception Reveals a Temporal Limit of Motion Correspondence Based on Surface Feature over Space and Time

Directory of Open Access Journals (Sweden)

Yousuke Kawachi

2011-06-01

Full Text Available We examined how stream/bounce event perception is affected by motion correspondence based on the surface features of moving objects passing behind an occlusion. In the stream/bounce display two identical objects moving across each other in a two-dimensional display can be perceived as either streaming through or bouncing off each other at coincidence. Here, surface features such as colour (Experiments 1 and 2 or luminance (Experiment 3 were switched between the two objects at coincidence. The moment of coincidence was invisible to observers due to an occluder. Additionally, the presentation of the moving objects was manipulated in duration after the feature switch at coincidence. The results revealed that a postcoincidence duration of approximately 200 ms was required for the visual system to stabilize judgments of stream/bounce events by determining motion correspondence between the objects across the occlusion on the basis of the surface feature. The critical duration was similar across motion speeds of objects and types of surface features. Moreover, controls (Experiments 4a–4c showed that cognitive bias based on feature (colour/luminance congruency across the occlusion could not fully account for the effects of surface features on the stream/bounce judgments. We discuss the roles of motion correspondence, visual feature processing, and attentive tracking in the stream/bounce judgments.

Real time object localization based on histogram of s-RGB

Science.gov (United States)

Mudjirahardjo, Panca; Suyono, Hadi; Setyawan, Raden Arief

2017-09-01

Object localization is the first task in pattern detection and recognition. This task is very important due to it reduces the searching time to the interest object. In this paper we introduce our novel method of object localization based on color feature. Our novel method is a histogram of s-RGB. This histogram is used in the training phase to determine the color dominant in the initial Region of Interest (ROI). Then this information is used to label the interest object. To reduce noise and localize the interest object, we apply the row and column density function of pixels. The comparison result with some processes, our system gives a best result and takes a short computation time of 26.56 ms, in the video rate of 15 frames per second (fps).

Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes.

Science.gov (United States)

Yebes, J Javier; Bergasa, Luis M; García-Garrido, Miguel Ángel

2015-04-20

Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.

Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes

Directory of Open Access Journals (Sweden)

J. Javier Yebes

2015-04-01

Full Text Available Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles. In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity, while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.

Different cortical mechanisms for spatial vs. feature-based attentional selection in visual working memory

Directory of Open Access Journals (Sweden)

Anna Heuer

2016-08-01

Full Text Available The limited capacity of visual working memory necessitates attentional mechanisms that selectively update and maintain only the most task-relevant content. Psychophysical experiments have shown that the retroactive selection of memory content can be based on visual properties such as location or shape, but the neural basis for such differential selection is unknown. For example, it is not known if there are different cortical modules specialized for spatial versus feature-based mnemonic attention, in the same way that has been demonstrated for attention to perceptual input. Here, we used transcranial magnetic stimulation (TMS to identify areas in human parietal and occipital cortex involved in the selection of objects from memory based on cues to their location (spatial information or their shape (featural information. We found that TMS over the supramarginal gyrus (SMG selectively facilitated spatial selection, whereas TMS over the lateral occipital cortex selectively enhanced feature-based selection for remembered objects in the contralateral visual field. Thus, different cortical regions are responsible for spatial vs. feature-based selection of working memory representations. Since the same regions are involved in attention to external events, these new findings indicate overlapping mechanisms for attentional control over perceptual input and mnemonic representations.

Real-Time FPGA-Based Object Tracker with Automatic Pan-Tilt Features for Smart Video Surveillance Systems

Directory of Open Access Journals (Sweden)

Sanjay Singh

2017-05-01

Full Text Available The design of smart video surveillance systems is an active research field among the computer vision community because of their ability to perform automatic scene analysis by selecting and tracking the objects of interest. In this paper, we present the design and implementation of an FPGA-based standalone working prototype system for real-time tracking of an object of interest in live video streams for such systems. In addition to real-time tracking of the object of interest, the implemented system is also capable of providing purposive automatic camera movement (pan-tilt in the direction determined by movement of the tracked object. The complete system, including camera interface, DDR2 external memory interface controller, designed object tracking VLSI architecture, camera movement controller and display interface, has been implemented on the Xilinx ML510 (Virtex-5 FX130T FPGA Board. Our proposed, designed and implemented system robustly tracks the target object present in the scene in real time for standard PAL (720 × 576 resolution color video and automatically controls camera movement in the direction determined by the movement of the tracked object.

Morphologic Features of Extrahepatic Manifestations of Hepatitis C Virus Infection

Directory of Open Access Journals (Sweden)

Huaibin M. Ko

2012-01-01

Full Text Available Cirrhosis and hepatocellular carcinoma are the prototypic complications of chronic hepatitis C virus infection in the liver. However, hepatitis C virus also affects a variety of other organs that may lead to significant morbidity and mortality. Extrahepatic manifestations of hepatitis C infection include a multitude of disease processes affecting the small vessels, skin, kidneys, salivary gland, eyes, thyroid, and immunologic system. The majority of these conditions are thought to be immune mediated. The most documented of these entities is mixed cryoglobulinemia. Morphologically, immune complex depositions can be identified in small vessels and glomerular capillary walls, leading to leukoclastic vasculitis in the skin and membranoproliferative glomerulonephritis in the kidney. Other HCV-associated entities include porphyria cutanea tarda, lichen planus, necrolytic acral erythema, membranous glomerulonephritis, diabetic nephropathy, B-cell non-Hodgkin lymphomas, insulin resistance, sialadenitis, sicca syndrome, and autoimmune thyroiditis. This paper highlights the histomorphologic features of these processes, which are typically characterized by chronic inflammation, immune complex deposition, and immunoproliferative disease in the affected organ.

Morphological features in a Xhosa schizophrenia population

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Muller Jacqueline E

2006-10-01

Full Text Available Abstract Background Demonstrating an association between physical malformation and schizophrenia could be considered supportive of a neurodevelopmental origin of schizophrenia and may offer insights into a critical period for the development of this illness. The aim of our study was to investigate whether differences in the presence of minor physical anomalies could be demonstrated between schizophrenia sufferers and normal controls in a Xhosa population with a view to identifying a means of subtyping schizophrenia for use in future genetic studies. Methods Sixty-three subjects with schizophrenia (21 sibling pairs, 1 sibship of four and a group of probands with an affected non-participating sibling (n = 17, 81 normal controls (37 singletons and 22 sibling pairs of Xhosa ethnicity were recruited. Each participant was then examined for minor physical anomalies using the Modified Waldrop scale. The relationship between each of the morphological features and the presence of an affected sib was examined using the Chi-squared test, followed by an intra-pair concordance analysis in the sibling pairs. Results Gap between first and second toes was significantly more common in the affected sib pair group when compared to the non-affected sib pair group (p = 0.019 and non-affected singleton control group (p = 0.013. Concordance analysis also revealed increased concordance for this item in the affected sib pair group. Conclusion These findings offer an intriguing possibility that in the Xhosa population, affected sib pair status may be linked to a neurodevelopmental insult during a specific period of the fetal developmental.

Connection-based and object-based grouping in multiple-object tracking: A developmental study.

Science.gov (United States)

Van der Hallen, Ruth; Reusens, Julie; Evers, Kris; de-Wit, Lee; Wagemans, Johan

2018-03-30

Developmental research on Gestalt laws has previously revealed that, even as young as infancy, we are bound to group visual elements into unitary structures in accordance with a variety of organizational principles. Here, we focus on the developmental trajectory of both connection-based and object-based grouping, and investigate their impact on object formation in participants, aged 9-21 years old (N = 113), using a multiple-object tracking paradigm. Results reveal a main effect of both age and grouping type, indicating that 9- to 21-year-olds are sensitive to both connection-based and object-based grouping interference, and tracking ability increases with age. In addition to its importance for typical development, these results provide an informative baseline to understand clinical aberrations in this regard. Statement of contribution What is already known on this subject? The origin of the Gestalt principles is still an ongoing debate: Are they innate, learned over time, or both? Developmental research has revealed how each Gestalt principle has its own trajectory and unique relationship to visual experience. Both connectedness and object-based grouping play an important role in object formation during childhood. What does this study add? The study identifies how sensitivity to connectedness and object-based grouping evolves in individuals, aged 9-21 years old. Using multiple-object tracking, results reveal that the ability to track multiple objects increases with age. These results provide an informative baseline to understand clinical aberrations in different types of grouping. © 2018 The Authors. British Journal of Developmental Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

Object Persistence: A Framework Based On Design Patterns

OpenAIRE

Kienzle, Jörg; Romanovsky, Alexander

2000-01-01

The poster presents a framework for providing object persistence in object-oriented programming languages without modifying the run-time system or the language itself. The framework does not rely on any kind of special programming language features. It only uses basic object-oriented programming techniques, and is therefore implementable in any object-oriented programming language.

Shadow detection of moving objects based on multisource information in Internet of things

Science.gov (United States)

Ma, Zhen; Zhang, De-gan; Chen, Jie; Hou, Yue-xian

2017-05-01

Moving object detection is an important part in intelligent video surveillance under the banner of Internet of things. The detection of moving target's shadow is also an important step in moving object detection. On the accuracy of shadow detection will affect the detection results of the object directly. Based on the variety of shadow detection method, we find that only using one feature can't make the result of detection accurately. Then we present a new method for shadow detection which contains colour information, the invariance of optical and texture feature. Through the comprehensive analysis of the detecting results of three kinds of information, the shadow was effectively determined. It gets ideal effect in the experiment when combining advantages of various methods.

Feature-based Detection and Discrimination at DuPont's Lake Success Business Park, Connecticut

National Research Council Canada - National Science Library

Keiswetter, Dean A

2007-01-01

The objective of this demonstration was to determine if laser-positioned, high-density EM61 data acquired in a moving survey mode could support feature-based discrimination decisions for a canopied...

Method for evaluation of human induced pluripotent stem cell quality using image analysis based on the biological morphology of cells.

Science.gov (United States)

Wakui, Takashi; Matsumoto, Tsuyoshi; Matsubara, Kenta; Kawasaki, Tomoyuki; Yamaguchi, Hiroshi; Akutsu, Hidenori

2017-10-01

We propose an image analysis method for quality evaluation of human pluripotent stem cells based on biologically interpretable features. It is important to maintain the undifferentiated state of induced pluripotent stem cells (iPSCs) while culturing the cells during propagation. Cell culture experts visually select good quality cells exhibiting the morphological features characteristic of undifferentiated cells. Experts have empirically determined that these features comprise prominent and abundant nucleoli, less intercellular spacing, and fewer differentiating cellular nuclei. We quantified these features based on experts' visual inspection of phase contrast images of iPSCs and found that these features are effective for evaluating iPSC quality. We then developed an iPSC quality evaluation method using an image analysis technique. The method allowed accurate classification, equivalent to visual inspection by experts, of three iPSC cell lines.

Kimpul (Xanthosoma spp. characterization based on morphological characteristic and isozymic analysis

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SAJIDAN

2009-11-01

Full Text Available Nurmiyati, Sugiyarto, Sajidan. 2009. Kimpul (Xanthosoma spp. characterization based on morphological characteristic and isozymic analysis. Nusantara Bioscience 1: 138-145. This research is aimed: (i to know the variety of kimpul (Xanthosoma spp. based on morphological characteristics and isozymes analysis; (ii to know the correlation between its genetic space based on morphological characteristics and its genetic resemblance based on isozymes-banding pattern. This research results were analyzed and described by descriptive qualitative methods. Morphological observation was carried out in sub-District of Galur, Lendah and Girimulyo, Kulonprogo District, Yogyakarta. Morphological data of the kimpul plant was explored descriptively and then made dendogram. Data of isozymic banding pattern were analyzed quantitatively based on the appearance of the band on the gel, and qualitatively based on the thickness of the band formed, and then made dendogram. The correlation, between its genetic distance based on morphological characteristics and its genetic resemblance based on isozymes-banding pattern, were then analyzed grounded on coefficient correlation between product-moment and goodness of it criteria based on correlation. The results pointed out that morphologically, on eight observed samples which were consist of four different types (species, each Xanthosoma from different locations did not indicate obvious differences. Esterase was formed four different banding-patterns, Glutamate Oxaloacetate Transaminase indicated eight different banding-patterns, and Peroxidase indicated seven different banding-patterns. Correlation between morphological data and data from EST and GOT isozymic banding pattern were very good (0.967918 and 0.937113, While, the correlations between morphological data and POD isozymes were good (0.892721.

Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

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M.F.M. Yunoh

2015-06-01

Full Text Available This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses.

Science.gov (United States)

Syrowatka, Ania; Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-26

Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however, some features performed better than

Significance of MPEG-7 textural features for improved mass detection in mammography.

Science.gov (United States)

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

Are objects the same as groups? ERP correlates of spatial attentional guidance by irrelevant feature similarity.

Science.gov (United States)

Kasai, Tetsuko; Moriya, Hiroki; Hirano, Shingo

2011-07-05

It has been proposed that the most fundamental units of attentional selection are "objects" that are grouped according to Gestalt factors such as similarity or connectedness. Previous studies using event-related potentials (ERPs) have shown that object-based attention is associated with modulations of the visual-evoked N1 component, which reflects an early cortical mechanism that is shared with spatial attention. However, these studies only examined the case of perceptually continuous objects. The present study examined the case of separate objects that are grouped according to feature similarity (color, shape) by indexing lateralized potentials at posterior sites in a sustained-attention task that involved bilateral stimulus arrays. A behavioral object effect was found only for task-relevant shape similarity. Electrophysiological results indicated that attention was guided to the task-irrelevant side of the visual field due to achromatic-color similarity in N1 (155-205 ms post-stimulus) and early N2 (210-260 ms) and due to shape similarity in early N2 and late N2 (280-400 ms) latency ranges. These results are discussed in terms of selection mechanisms and object/group representations. Copyright © 2011 Elsevier B.V. All rights reserved.

Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

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Yi An

2013-01-01

Full Text Available Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from [0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations.

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Miha Amon

Full Text Available Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping, thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB. As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3 classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The

Feature selection model based on clustering and ranking in pipeline for microarray data

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Barnali Sahu

2017-01-01

Full Text Available Most of the available feature selection techniques in the literature are classifier bound. It means a group of features tied to the performance of a specific classifier as applied in wrapper and hybrid approach. Our objective in this study is to select a set of generic features not tied to any classifier based on the proposed framework. This framework uses attribute clustering and feature ranking techniques in pipeline in order to remove redundant features. On each uncovered cluster, signal-to-noise ratio, t-statistics and significance analysis of microarray are independently applied to select the top ranked features. Both filter and evolutionary wrapper approaches have been considered for feature selection and the data set with selected features are given to ensemble of predefined statistically different classifiers. The class labels of the test data are determined using majority voting technique. Moreover, with the aforesaid objectives, this paper focuses on obtaining a stable result out of various classification models. Further, a comparative analysis has been performed to study the classification accuracy and computational time of the current approach and evolutionary wrapper techniques. It gives a better insight into the features and further enhancing the classification accuracy with less computational time.

Tolerance-Based Feature Transforms

NARCIS (Netherlands)

Reniers, Dennie; Telea, Alexandru

2007-01-01

Tolerance-based feature transforms (TFTs) assign to each pixel in an image not only the nearest feature pixels on the boundary (origins), but all origins from the minimum distance up to a user-defined tolerance. In this paper, we compare four simple-to-implement methods for computing TFTs on binary

Feature Level Two -Dimensional Arrays Based Fusion in the Personal Authentication system using Physiological Biometric traits

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Jerusalin Carol .J

Full Text Available ABSTRACT The fingerprint, knuckle print and the retina are used to authenticate a person accurately because of the permanence in the features. These three biometric traits are fused for better security. The fingerprint and knuckle print images are pre-processed by morphological techniques and the features are extracted from the normalized image using gabor filter. The retinal image is converted to gray image and pre-processing is done using top hat and bottom hat filtering. Blood vessels are segmented and the features are extracted by locating the optic disk as the centre point. The extracted features from the fingerprint, knuckle print and the retina are fused together as one template and stored in the data base for authentication purpose, thus reducing the space and time complexity.

Object-based connectedness facilitates matching

NARCIS (Netherlands)

Koning, A.R.; Lier, R.J. van

2003-01-01

In two matching tasks, participants had to match two images of object pairs. Image-based (113) connectedness refers to connectedness between the objects in an image. Object-based (OB) connectedness refers to connectedness between the interpreted objects. In Experiment 1, a monocular depth cue

Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

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Xianju Li

2015-07-01

Full Text Available For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA of LiDar data, while few studies have examined (semi- automated methods and object-based image analysis (OBIA. Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS, were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1 inclusion of holes encircled by the landslide body; (2 removal of isolated segments, and (3 delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1 the filter features of surface roughness were first applied for calculating object features, and proved useful; (2 FS improved classification accuracy and reduced features; (3 the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4 compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5 based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

AUTOMATED FEATURE BASED TLS DATA REGISTRATION FOR 3D BUILDING MODELING

OpenAIRE

K. Kitamura; N. Kochi; S. Kaneko

2012-01-01

In this paper we present a novel method for the registration of point cloud data obtained using terrestrial laser scanner (TLS). The final goal of our investigation is the automated reconstruction of CAD drawings and the 3D modeling of objects surveyed by TLS. Because objects are scanned from multiple positions, individual point cloud need to be registered to the same coordinate system. We propose in this paper an automated feature based registration procedure. Our proposed method does not re...

A review of supervised object-based land-cover image classification

Science.gov (United States)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial

A simple fracture energy prediction method for fiber network based on its morphological features extracted by X-ray tomography

International Nuclear Information System (INIS)

Huang, Xiang; Wang, Qinghui; Zhou, Wei; Li, Jingrong

2013-01-01

The fracture behavior of a novel porous metal fiber sintered sheet (PMFSS) was predicted using a semi-empirical method combining the knowledge of its morphological characteristics and micro-mechanical responses. The morphological characteristics were systematically summarized based on the analysis of the topologically identical skeleton representation extracted from the X-ray tomography images. The analytical model firstly proposed by Tan et al. [1] was further modified according to the experimental observations from both tensile tests of single fibers and sintered fiber sheets, which built the coupling of single fiber segment and fiber network in terms of fracture energy using a simple prediction method. The efficacy of the prediction model was verified by comparing the predicted results to the experimental measurements. The prediction error that arose at high porosity was analyzed through fiber orientation distribution. Moreover, the tensile fracture process evolving from single fiber segments at micro-scale to the global mechanical performance was investigated

MORPHOLOGICAL HIT-OR-MISS TRANSFORM BASED APPROACH FOR BUILDING DAMAGE ESTIMATION FROM VHR AIRBORNE IMAGERY IN 2011 PACIFIC COAST OF TOHOKU EARTHQUAKE AND TSUNAMI

Directory of Open Access Journals (Sweden)

C. D. K. Parape

2012-08-01

Full Text Available The very high resolution (VHR airborne images offer the opportunity to recognize features such as road, vegetation, buildings and other kind of infrastructures. The advantage of remote sensing and its applications made it possible to extract damaged, undamaged building and vulnerability assessment of wide urban areas due to a natural disaster. In this paper, we focus on an automatic building detection method which is helpful to optimizing, recognizing, rescuing, recovery and management tasks in the event of a disaster. Objective of this study is to develop techniques for tsunami damaged building extraction, based on very high resolution (VHR airborne images acquired before and after the 2011 East coastline of Japan among Tohoku area and to carry out a damage assessment of building and vulnerable area mapping. This paper presents a methodology and results of evaluating damaged buildings detection algorithm using an object recognition task based on Mathematical Morphological (MM operators for Very High Resolution (VHR remotely sensed airborne images. The proposed approach involves several advanced morphological operators among which an adaptive hit-or-miss transform with varying size and shape of the structuring elements. VHR airborne images consisting of pre and post 2011 Pacific coast of Tohoku earthquake and Tsunami site of the Ishinomaki, Miyagi area in Japan were used. The extracted results of building were compared with ground truth data giving 76% and 88% in accuracy before and after the Tsunami event.

Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

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Qian Zhang

2015-12-01

Full Text Available New aerial sensors and platforms (e.g., unmanned aerial vehicles (UAVs are capable of providing ultra-high resolution remote sensing data (less than a 30-cm ground sampling distance (GSD. This type of data is an important source for interpreting sub-building level objects; however, it has not yet been explored. The large-scale differences of urban objects, the high spectral variability and the large perspective effect bring difficulties to the design of descriptive features. Therefore, features representing the spatial information of the objects are essential for dealing with the spectral ambiguity. In this paper, we proposed a dual morphology top-hat profile (DMTHP using both morphology reconstruction and erosion with different granularities. Due to the high dimensional feature space, we have proposed an adaptive scale selection procedure to reduce the feature dimension according to the training samples. The DMTHP is extracted from both images and Digital Surface Models (DSM to obtain complimentary information. The random forest classifier is used to classify the features hierarchically. Quantitative experimental results on aerial images with 9-cm and UAV images with 5-cm GSD are performed. Under our experiments, improvements of 10% and 2% in overall accuracy are obtained in comparison with the well-known differential morphological profile (DMP feature, and superior performance is observed over other tested features. Large format data with 20,000 × 20,000 pixels are used to perform a qualitative experiment using the proposed method, which shows its promising potential. The experiments also demonstrate that the DSM information has greatly enhanced the classification accuracy. In the best case in our experiment, it gives rise to a classification accuracy from 63.93% (spectral information only to 94.48% (the proposed method.

Smart learning objects for smart education in computer science theory, methodology and robot-based implementation

CERN Document Server

Stuikys, Vytautas

2015-01-01

This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the latest research on the meta-programming-based generative learning objects (the latter with advanced features are treated as SLOs) and the use of educational robots in teaching CS topics. The introduced methodology includes the overall processes to develop SLO and smart educational environment (SEE) and integrates both into the real education setting to provide teaching in CS using constructivist a

Eye-tracking study of inanimate objects

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Ković Vanja

2009-01-01

Full Text Available Unlike the animate objects, where participants were consistent in their looking patterns, for inanimates it was difficult to identify both consistent areas of fixations and a consistent order of fixations. Furthermore, in comparison to animate objects, in animates received significantly shorter total looking time, shorter longest looks and a smaller number of overall fixations. However, as with animates, looking patterns did not systematically differ between the naming and non-naming conditions. These results suggested that animacy, but not labelling, impacts on looking behavior in this paradigm. In the light of feature-based accounts of semantic memory organization, one could interpret these findings as suggesting that processing of the animate objects is based on the saliency/diagnosticity of their visual features (which is then reflected through participants eye-movements towards those features, whereas processing of the inanimate objects is based more on functional features (which cannot be easily captured by looking behavior in such a paradigm.

[The morphological characteristic of the skin lesions inflicted by plastic knives with four cutting edges].

Science.gov (United States)

Leonov, S V; Finkel'shtein, V T

2015-01-01

The objective of the present work was to study the morphological features of the skin lesions inflicted by the blades of the Fgx Boot Blade I knives having four cutting edges. The study revealed the signs that can be used to distinguish between morphological characteristics of the stab and lacerated wounds having the primary and secondary incisions made by the four-edge blade.

CT features of vasculitides based on the 2012 international chapel hill consensus conference revised classification

International Nuclear Information System (INIS)

Hur, Jee Hye; Chun, Eun Ju; Kim, Hae Young; Kim, Jeong Jae; Lee, Kyung Won; Kwang, Hyon Joo; Yoo, Jin Young

2017-01-01

Vasculitis, characterized by inflammation of vessel walls, is comprised of heterogeneous clinicopathological entities, and thus poses a diagnostic challenge. The most widely used approach for classifying vasculitides is based on the International Chapel Hill Consensus Conference (CHCC) nomenclature system. Based on the recently revised CHCC 2012, we propose computed tomography (CT) features of vasculitides and a differential diagnosis based on location and morphological characteristics. Finally, vasculitis mimics should be differentiated, because erroneous application of immunosuppressive drugs on vasculitis mimics may be ineffective, even deteriorating. This article presents the utility of CT in the diagnosis and differential diagnosis of vasculitides

CT features of vasculitides based on the 2012 international chapel hill consensus conference revised classification

Energy Technology Data Exchange (ETDEWEB)

Hur, Jee Hye; Chun, Eun Ju; Kim, Hae Young; Kim, Jeong Jae; Lee, Kyung Won [Dept. of Radiology, Seoul National University Bundang Hospital, Seongnam (Korea, Republic of); Kwang, Hyon Joo [Dept. of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of); Yoo, Jin Young [Dept. of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)

2017-09-15

Vasculitis, characterized by inflammation of vessel walls, is comprised of heterogeneous clinicopathological entities, and thus poses a diagnostic challenge. The most widely used approach for classifying vasculitides is based on the International Chapel Hill Consensus Conference (CHCC) nomenclature system. Based on the recently revised CHCC 2012, we propose computed tomography (CT) features of vasculitides and a differential diagnosis based on location and morphological characteristics. Finally, vasculitis mimics should be differentiated, because erroneous application of immunosuppressive drugs on vasculitis mimics may be ineffective, even deteriorating. This article presents the utility of CT in the diagnosis and differential diagnosis of vasculitides.

Comparative description of morphological features of flax oily (Linum usitatissimum L. different sorts in the conditions of Precarpathian

Directory of Open Access Journals (Sweden)

Inessa F. Drozd

2012-03-01

Full Text Available The paper contains the results of investigation of meteorological conditions influence on morphological features of different flax oily sorts in the conditions of Precarpathian. The results confirm, that weather terms and the terms of the sowing have influence on the height of plants, number of capsules and seeds per plant.

Category-based guidance of spatial attention during visual search for feature conjunctions.

Science.gov (United States)

Nako, Rebecca; Grubert, Anna; Eimer, Martin

2016-10-01

The question whether alphanumerical category is involved in the control of attentional target selection during visual search remains a contentious issue. We tested whether category-based attentional mechanisms would guide the allocation of attention under conditions where targets were defined by a combination of alphanumerical category and a basic visual feature, and search displays could contain both targets and partially matching distractor objects. The N2pc component was used as an electrophysiological marker of attentional object selection in tasks where target objects were defined by a conjunction of color and category (Experiment 1) or shape and category (Experiment 2). Some search displays contained the target or a nontarget object that matched either the target color/shape or its category among 3 nonmatching distractors. In other displays, the target and a partially matching nontarget object appeared together. N2pc components were elicited not only by targets and by color- or shape-matching nontargets, but also by category-matching nontarget objects, even on trials where a target was present in the same display. On these trials, the summed N2pc components to the 2 types of partially matching nontargets were initially equal in size to the target N2pc, suggesting that attention was allocated simultaneously and independently to all objects with target-matching features during the early phase of attentional processing. Results demonstrate that alphanumerical category is a genuine guiding feature that can operate in parallel with color or shape information to control the deployment of attention during visual search. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Bayesian object classification of gold nanoparticles

KAUST Repository

Konomi, Bledar A.

2013-06-01

The properties of materials synthesized with nanoparticles (NPs) are highly correlated to the sizes and shapes of the nanoparticles. The transmission electron microscopy (TEM) imaging technique can be used to measure the morphological characteristics of NPs, which can be simple circles or more complex irregular polygons with varying degrees of scales and sizes. A major difficulty in analyzing the TEM images is the overlapping of objects, having different morphological properties with no specific information about the number of objects present. Furthermore, the objects lying along the boundary render automated image analysis much more difficult. To overcome these challenges, we propose a Bayesian method based on the marked-point process representation of the objects. We derive models, both for the marks which parameterize the morphological aspects and the points which determine the location of the objects. The proposed model is an automatic image segmentation and classification procedure, which simultaneously detects the boundaries and classifies the NPs into one of the predetermined shape families. We execute the inference by sampling the posterior distribution using Markov chainMonte Carlo (MCMC) since the posterior is doubly intractable. We apply our novel method to several TEM imaging samples of gold NPs, producing the needed statistical characterization of their morphology. © Institute of Mathematical Statistics, 2013.

Bayesian object classification of gold nanoparticles

KAUST Repository

Konomi, Bledar A.; Dhavala, Soma S.; Huang, Jianhua Z.; Kundu, Subrata; Huitink, David; Liang, Hong; Ding, Yu; Mallick, Bani K.

2013-01-01

The properties of materials synthesized with nanoparticles (NPs) are highly correlated to the sizes and shapes of the nanoparticles. The transmission electron microscopy (TEM) imaging technique can be used to measure the morphological characteristics of NPs, which can be simple circles or more complex irregular polygons with varying degrees of scales and sizes. A major difficulty in analyzing the TEM images is the overlapping of objects, having different morphological properties with no specific information about the number of objects present. Furthermore, the objects lying along the boundary render automated image analysis much more difficult. To overcome these challenges, we propose a Bayesian method based on the marked-point process representation of the objects. We derive models, both for the marks which parameterize the morphological aspects and the points which determine the location of the objects. The proposed model is an automatic image segmentation and classification procedure, which simultaneously detects the boundaries and classifies the NPs into one of the predetermined shape families. We execute the inference by sampling the posterior distribution using Markov chainMonte Carlo (MCMC) since the posterior is doubly intractable. We apply our novel method to several TEM imaging samples of gold NPs, producing the needed statistical characterization of their morphology. © Institute of Mathematical Statistics, 2013.

Finding Objects for Assisting Blind People.

Science.gov (United States)

Yi, Chucai; Flores, Roberto W; Chincha, Ricardo; Tian, Yingli

2013-07-01

Computer vision technology has been widely used for blind assistance, such as navigation and wayfinding. However, few camera-based systems are developed for helping blind or visually-impaired people to find daily necessities. In this paper, we propose a prototype system of blind-assistant object finding by camera-based network and matching-based recognition. We collect a dataset of daily necessities and apply Speeded-Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) feature descriptors to perform object recognition. Experimental results demonstrate the effectiveness of our prototype system.

[Object-oriented stand type classification based on the combination of multi-source remote sen-sing data].

Science.gov (United States)

Mao, Xue Gang; Wei, Jing Yu

2017-11-01

The recognition of forest type is one of the key problems in forest resource monitoring. The Radarsat-2 data and QuickBird remote sensing image were used for object-based classification to study the object-based forest type classification and recognition based on the combination of multi-source remote sensing data. In the process of object-based classification, three segmentation schemes (segmentation with QuickBird remote sensing image only, segmentation with Radarsat-2 data only, segmentation with combination of QuickBird and Radarsat-2) were adopted. For the three segmentation schemes, ten segmentation scale parameters were adopted (25-250, step 25), and modified Euclidean distance 3 index was further used to evaluate the segmented results to determine the optimal segmentation scheme and segmentation scale. Based on the optimal segmented result, three forest types of Chinese fir, Masson pine and broad-leaved forest were classified and recognized using Support Vector Machine (SVM) classifier with Radial Basis Foundation (RBF) kernel according to different feature combinations of topography, height, spectrum and common features. The results showed that the combination of Radarsat-2 data and QuickBird remote sensing image had its advantages of object-based forest type classification over using Radarsat-2 data or QuickBird remote sensing image only. The optimal scale parameter for QuickBirdRadarsat-2 segmentation was 100, and at the optimal scale, the accuracy of object-based forest type classification was the highest (OA=86%, Kappa=0.86), when using all features which were extracted from two kinds of data resources. This study could not only provide a reference for forest type recognition using multi-source remote sensing data, but also had a practical significance for forest resource investigation and monitoring.

Automatic target recognition using a feature-based optical neural network

Science.gov (United States)

Chao, Tien-Hsin

1992-01-01

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Tiger hair morphology and its variations for wildlife forensic investigation

Directory of Open Access Journals (Sweden)

Thitika Kitpipit

2013-11-01

Full Text Available Tiger population has dramatically decreased due to illegal consumption and commercialisation of their body parts. Frequently, hair samples are the only evidence found in the crime scene. Thus, they play an important role in species identification for wildlife forensic investigation. In this study, we provide the first in-depth report on a variety of qualitative and quantitative characteristics of tiger guard hairs (24 hairs per individual from four individuals. The proposed method could reduce subjectivity of expert opinions on species identification based on hair morphology. Variations in 23 hair morphological characteristics were quantified at three levels: hair section, body region, and intra-species. The results indicate statistically significant variations in most morphological characteristics in all levels. Intra-species variations of four variables, namely hair length, hair index, scale separation and scale pattern, were low. Therefore, identification of tiger hairs using these multiple features in combination with other characteristics with high inter-species variations (e.g. medulla type should bring about objective and accurate tiger hair identification. The method used should serve as a guideline and be further applied to other species to establish a wildlife hair morphology database. Statistical models could then be constructed to distinguish species and provide evidential values in terms of likelihood ratios.

Exploring the relationship between object realism and object-based attention effects.

Science.gov (United States)

Roque, Nelson; Boot, Walter R

2015-09-01

Visual attention prioritizes processing of locations in space, and evidence also suggests that the benefits of attention can be shaped by the presence of objects (object-based attention). However, the prevalence of object-based attention effects has been called into question recently by evidence from a large-sampled study employing classic attention paradigms (Pilz et al., 2012). We conducted two experiments to explore factors that might determine when and if object-based attention effects are observed, focusing on the degree to which the concreteness and realism of objects might contribute to these effects. We adapted the classic attention paradigm first reported by Egly, Driver, and Rafal (1994) by replacing abstract bar stimuli in some conditions with objects that were more concrete and familiar to participants: items of silverware. Furthermore, we varied the realism of these items of silverware, presenting either cartoon versions or photo-realistic versions. Contrary to predictions, increased realism did not increase the size of object-based effects. In fact, no clear object-based effects were observed in either experiment, consistent with previous failures to replicate these effects in similar paradigms. While object-based attention may exist, and may have important influences on how we parse the visual world, these and other findings suggest that the two-object paradigm typically relied upon to study object-based effects may not be the best paradigm to investigate these issues. Copyright © 2015 Elsevier B.V. All rights reserved.

Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

Science.gov (United States)

Li, Zhongyu; Butler, Erik; Li, Kang; Lu, Aidong; Ji, Shuiwang; Zhang, Shaoting

2018-02-12

Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

Geographic Object-Based Image Analysis - Towards a new paradigm.

Science.gov (United States)

Blaschke, Thomas; Hay, Geoffrey J; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ' per-pixel paradigm ' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Object-based warping: an illusory distortion of space within objects.

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Vickery, Timothy J; Chun, Marvin M

2010-12-01

Visual objects are high-level primitives that are fundamental to numerous perceptual functions, such as guidance of attention. We report that objects warp visual perception of space in such a way that spatial distances within objects appear to be larger than spatial distances in ground regions. When two dots were placed inside a rectangular object, they appeared farther apart from one another than two dots with identical spacing outside of the object. To investigate whether this effect was object based, we measured the distortion while manipulating the structure surrounding the dots. Object displays were constructed with a single object, multiple objects, a partially occluded object, and an illusory object. Nonobject displays were constructed to be comparable to object displays in low-level visual attributes. In all cases, the object displays resulted in a more powerful distortion of spatial perception than comparable non-object-based displays. These results suggest that perception of space within objects is warped.

Morphological and molecular characterization of L-methioninase ...

African Journals Online (AJOL)

Six species of L-methioninase producing Aspergillus species, isolated from Egyptian soil, were selected for comprehensive morphotypic and molecular characterization. Based on morphological and physiological features, these isolates were identified as Aspergillus flavipes, Aspergillus carneus, Aspergillus flavus, ...

Connection-based and object-based grouping in multiple-object tracking: A developmental study

OpenAIRE

Hallen, Ruth; Reusens, J. (Julie); Evers, K. (Kris); de-Wit, Lee; Wagemans, Johan

2018-01-01

textabstractDevelopmental research on Gestalt laws has previously revealed that, even as young as infancy, we are bound to group visual elements into unitary structures in accordance with a variety of organizational principles. Here, we focus on the developmental trajectory of both connection-based and object-based grouping, and investigate their impact on object formation in participants, aged 9-21 years old (N = 113), using a multiple-object tracking paradigm. Results reveal a main effect o...

Sperm Morphological Features Associated with Chronic Chagas Disease in the Semen of Experimentally Infected Dogs

Science.gov (United States)

Rodríguez-Morales, Olivia; Pedro-Martínez, Elvia; Hernández-Pichardo, José Ernesto; Alejandre-Aguilar, Ricardo; Aranda-Fraustro, Alberto; Graullera-Rivera, Verónica; Arce-Fonseca, Minerva

2014-01-01

The presence of trypanosomatids in the reproductive systems of different mammals (causing genital lesions in the acute stage of the disease) may predispose the animals to low semen quality. However, there are no studies examining the alterations in the sperm morphological features in the chronic stage of Trypanosoma cruzi infection. Knowledge of these aspects is important to understand the other ways of transmission of the Chagas disease. Progressive motility, mass motility, concentration, and sperm morphology of 84 ejaculates of dogs that were chronically infected with T. cruzi were evaluated. Most of the findings were consistent with the reference values and with those obtained from healthy control dogs. The scrotal circumference was not correlated with spermatozoa concentration in the infected animals. In conclusion, the T. cruzi Ninoa (MHOM/MX/1994/Ninoa) strain does not cause significant alterations in the semen quality of dogs experiencing chronic Chagas disease (at concentrations of 5 × 104 to 1 × 106 parasites per animal). PMID:25114010

Review of research in feature based design

NARCIS (Netherlands)

Salomons, O.W.; van Houten, Frederikus J.A.M.; Kals, H.J.J.

1993-01-01

Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems

Quantitative evaluation method of the bubble structure of sponge cake by using morphology image processing

Science.gov (United States)

Tatebe, Hironobu; Kato, Kunihito; Yamamoto, Kazuhiko; Katsuta, Yukio; Nonaka, Masahiko

2005-12-01

Now a day, many evaluation methods for the food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that are using for the quality evaluation. An advantage of the image processing is to be able to evaluate objectively. The goal of our research is structure evaluation of sponge cake by using image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner. Because the depth of field of this type scanner is very shallow, the bubble region of the surface has low gray scale values, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. First, input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

The best stain for morphological study of human seminal fluid's ...

African Journals Online (AJOL)

Objectives:There is a high need for proper evaluation of the morphological features of human sperms. The importance of this lies in the field of andrology, male fertility and in vitro fertilization. The wet smears can give rough clue about the shape of the sperms, but it is neither accurate nor reproducible. This study aimed to ...

Face detection and facial feature localization using notch based templates

International Nuclear Information System (INIS)

Qayyum, U.

2007-01-01

We present a real time detection off aces from the video with facial feature localization as well as the algorithm capable of differentiating between the face/non-face patterns. The need of face detection and facial feature localization arises in various application of computer vision, so a lot of research is dedicated to come up with a real time solution. The algorithm should remain simple to perform real time whereas it should not compromise on the challenges encountered during the detection and localization phase, keeping simplicity and all challenges i.e. algorithm invariant to scale, translation, and (+-45) rotation transformations. The proposed system contains two parts. Visual guidance and face/non-face classification. The visual guidance phase uses the fusion of motion and color cues to classify skin color. Morphological operation with union-structure component labeling algorithm extracts contiguous regions. Scale normalization is applied by nearest neighbor interpolation method to avoid the effect of different scales. Using the aspect ratio of width and height size. Region of Interest (ROI) is obtained and then passed to face/non-face classifier. Notch (Gaussian) based templates/ filters are used to find circular darker regions in ROI. The classified face region is handed over to facial feature localization phase, which uses YCbCr eyes/lips mask for face feature localization. The empirical results show an accuracy of 90% for five different videos with 1000 face/non-face patterns and processing rate of proposed algorithm is 15 frames/sec. (author)

Feature-based attention in early vision for the modulation of figure–ground segregation

Directory of Open Access Journals (Sweden)

Nobuhiko eWagatsuma

2013-03-01

Full Text Available We investigated psychophysically whether feature-based attention modulates the perception of figure–ground (F–G segregation and, based on the results, we investigated computationally the neural mechanisms underlying attention modulation. In the psychophysical experiments, the attention of participants was drawn to a specific motion direction and they were then asked to judge the side of figure in an ambiguous figure with surfaces consisting of distinct motion directions. The results of these experiments showed that the surface consisting of the attended direction of motion was more frequently observed as figure, with a degree comparable to that of spatial attention (Wagatsuma, Shimizu, and Sakai, 2008. These experiments also showed that perception was dependent on the distribution of feature contrast, specifically the motion direction differences. These results led us to hypothesize that feature-based attention functions in a framework similar to that of spatial attention. We proposed a V1–V2 model in which feature-based attention modulates the contrast of low-level feature in V1, and this modulation of contrast changes directly the surround modulation of border-ownership-selective cells in V2; thus, perception of F–G is biased. The model exhibited good agreement with human perception in the magnitude of attention modulation and its invariance among stimuli. These results indicate that early-level features that are modified by feature-based attention alter subsequent processing along afferent pathway, and that such modification could even change the perception of object.

Feature-Based Attention in Early Vision for the Modulation of Figure–Ground Segregation

Science.gov (United States)

Wagatsuma, Nobuhiko; Oki, Megumi; Sakai, Ko

2013-01-01

We investigated psychophysically whether feature-based attention modulates the perception of figure–ground (F–G) segregation and, based on the results, we investigated computationally the neural mechanisms underlying attention modulation. In the psychophysical experiments, the attention of participants was drawn to a specific motion direction and they were then asked to judge the side of figure in an ambiguous figure with surfaces consisting of distinct motion directions. The results of these experiments showed that the surface consisting of the attended direction of motion was more frequently observed as figure, with a degree comparable to that of spatial attention (Wagatsuma et al., 2008). These experiments also showed that perception was dependent on the distribution of feature contrast, specifically the motion direction differences. These results led us to hypothesize that feature-based attention functions in a framework similar to that of spatial attention. We proposed a V1–V2 model in which feature-based attention modulates the contrast of low-level feature in V1, and this modulation of contrast changes directly the surround modulation of border-ownership-selective cells in V2; thus, perception of F–G is biased. The model exhibited good agreement with human perception in the magnitude of attention modulation and its invariance among stimuli. These results indicate that early-level features that are modified by feature-based attention alter subsequent processing along afferent pathway, and that such modification could even change the perception of object. PMID:23515841

Feature-based attention in early vision for the modulation of figure-ground segregation.

Science.gov (United States)

Wagatsuma, Nobuhiko; Oki, Megumi; Sakai, Ko

2013-01-01

We investigated psychophysically whether feature-based attention modulates the perception of figure-ground (F-G) segregation and, based on the results, we investigated computationally the neural mechanisms underlying attention modulation. In the psychophysical experiments, the attention of participants was drawn to a specific motion direction and they were then asked to judge the side of figure in an ambiguous figure with surfaces consisting of distinct motion directions. The results of these experiments showed that the surface consisting of the attended direction of motion was more frequently observed as figure, with a degree comparable to that of spatial attention (Wagatsuma et al., 2008). These experiments also showed that perception was dependent on the distribution of feature contrast, specifically the motion direction differences. These results led us to hypothesize that feature-based attention functions in a framework similar to that of spatial attention. We proposed a V1-V2 model in which feature-based attention modulates the contrast of low-level feature in V1, and this modulation of contrast changes directly the surround modulation of border-ownership-selective cells in V2; thus, perception of F-G is biased. The model exhibited good agreement with human perception in the magnitude of attention modulation and its invariance among stimuli. These results indicate that early-level features that are modified by feature-based attention alter subsequent processing along afferent pathway, and that such modification could even change the perception of object.

Esophageal Dysfunction in Friesian Horses: Morphological Features

NARCIS (Netherlands)

Ploeg, M.; Grone, A.; Saey, V.; Bruijn, de C.M.; Back, W.; Weeren, van P.R.; Scheideman, W.; Picavet, T.; Ducro, B.J.; Wijnberg, I.; Delesalle, C.

2015-01-01

Megaesophagus appears to be more common in Friesian horses than in other breeds. A prevalence of approximately 2% was observed among Friesian horses presented to the Wolvega Equine Clinic and the Utrecht University Equine Clinic. In this study, morphologic changes in the esophagi of Friesian horses

Atorvastatin effect evaluation based on feature combination of three-dimension ultrasound images

Science.gov (United States)

Luo, Yongkang; Ding, Mingyue

2016-03-01

In the past decades, stroke has become the worldwide common cause of death and disability. It is well known that ischemic stroke is mainly caused by carotid atherosclerosis. As an inexpensive, convenient and fast means of detection, ultrasound technology is applied widely in the prevention and treatment of carotid atherosclerosis. Recently, many studies have focused on how to quantitatively evaluate local arterial effects of medicine treatment for carotid diseases. So the evaluation method based on feature combination was proposed to detect potential changes in the carotid arteries after atorvastatin treatment. And the support vector machine (SVM) and 10-fold cross-validation protocol were utilized on a database of 5533 carotid ultrasound images of 38 patients (17 atorvastatin groups and 21 placebo groups) at baseline and after 3 months of the treatment. With combination optimization of many features (including morphological and texture features), the evaluation results of single feature and different combined features were compared. The experimental results showed that the performance of single feature is poor and the best feature combination have good recognition ability, with the accuracy 92.81%, sensitivity 80.95%, specificity 95.52%, positive predictive value 80.47%, negative predictive value 95.65%, Matthew's correlation coefficient 76.27%, and Youden's index 76.48%. And the receiver operating characteristic (ROC) curve was also performed well with 0.9663 of the area under the ROC curve (AUC), which is better than all the features with 0.9423 of the AUC. Thus, it is proved that this novel method can reliably and accurately evaluate the effect of atorvastatin treatment.

Weighted simultaneous algebraic reconstruction technique for tomosynthesis imaging of objects with high-attenuation features

International Nuclear Information System (INIS)

Levakhina, Y. M.; Müller, J.; Buzug, T. M.; Duschka, R. L.; Vogt, F.; Barkhausen, J.

2013-01-01

Purpose: This paper introduces a nonlinear weighting scheme into the backprojection operation within the simultaneous algebraic reconstruction technique (SART). It is designed for tomosynthesis imaging of objects with high-attenuation features in order to reduce limited angle artifacts. Methods: The algorithm estimates which projections potentially produce artifacts in a voxel. The contribution of those projections into the updating term is reduced. In order to identify those projections automatically, a four-dimensional backprojected space representation is used. Weighting coefficients are calculated based on a dissimilarity measure, evaluated in this space. For each combination of an angular view direction and a voxel position an individual weighting coefficient for the updating term is calculated. Results: The feasibility of the proposed approach is shown based on reconstructions of the following real three-dimensional tomosynthesis datasets: a mammography quality phantom, an apple with metal needles, a dried finger bone in water, and a human hand. Datasets have been acquired with a Siemens Mammomat Inspiration tomosynthesis device and reconstructed using SART with and without suggested weighting. Out-of-focus artifacts are described using line profiles and measured using standard deviation (STD) in the plane and below the plane which contains artifact-causing features. Artifacts distribution in axial direction is measured using an artifact spread function (ASF). The volumes reconstructed with the weighting scheme demonstrate the reduction of out-of-focus artifacts, lower STD (meaning reduction of artifacts), and narrower ASF compared to nonweighted SART reconstruction. It is achieved successfully for different kinds of structures: point-like structures such as phantom features, long structures such as metal needles, and fine structures such as trabecular bone structures. Conclusions: Results indicate the feasibility of the proposed algorithm to reduce typical

Weighted simultaneous algebraic reconstruction technique for tomosynthesis imaging of objects with high-attenuation features

Energy Technology Data Exchange (ETDEWEB)

Levakhina, Y. M. [Institute of Medical Engineering, University of Luebeck, Luebeck 23562, Germany and Graduate School for Computing in Medicine and Life Sciences, Luebeck 23562 (Germany); Mueller, J.; Buzug, T. M. [Institute of Medical Engineering, University of Luebeck, Luebeck 23562 (Germany); Duschka, R. L.; Vogt, F.; Barkhausen, J. [Clinic for Radiology, University Clinics Schleswig-Holstein, Luebeck 23562 (Germany)

2013-03-15

Purpose: This paper introduces a nonlinear weighting scheme into the backprojection operation within the simultaneous algebraic reconstruction technique (SART). It is designed for tomosynthesis imaging of objects with high-attenuation features in order to reduce limited angle artifacts. Methods: The algorithm estimates which projections potentially produce artifacts in a voxel. The contribution of those projections into the updating term is reduced. In order to identify those projections automatically, a four-dimensional backprojected space representation is used. Weighting coefficients are calculated based on a dissimilarity measure, evaluated in this space. For each combination of an angular view direction and a voxel position an individual weighting coefficient for the updating term is calculated. Results: The feasibility of the proposed approach is shown based on reconstructions of the following real three-dimensional tomosynthesis datasets: a mammography quality phantom, an apple with metal needles, a dried finger bone in water, and a human hand. Datasets have been acquired with a Siemens Mammomat Inspiration tomosynthesis device and reconstructed using SART with and without suggested weighting. Out-of-focus artifacts are described using line profiles and measured using standard deviation (STD) in the plane and below the plane which contains artifact-causing features. Artifacts distribution in axial direction is measured using an artifact spread function (ASF). The volumes reconstructed with the weighting scheme demonstrate the reduction of out-of-focus artifacts, lower STD (meaning reduction of artifacts), and narrower ASF compared to nonweighted SART reconstruction. It is achieved successfully for different kinds of structures: point-like structures such as phantom features, long structures such as metal needles, and fine structures such as trabecular bone structures. Conclusions: Results indicate the feasibility of the proposed algorithm to reduce typical

Feature-Based versus Category-Based Induction with Uncertain Categories

Science.gov (United States)

Griffiths, Oren; Hayes, Brett K.; Newell, Ben R.

2012-01-01

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is…

Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features

International Nuclear Information System (INIS)

Li, Bangyu; Zhang, Hui; Xu, Fanjiang

2014-01-01

This paper addresses the problem of water extraction from high resolution remote sensing images (including R, G, B, and NIR channels), which draws considerable attention in recent years. Previous work on water extraction mainly faced two difficulties. 1) It is difficult to obtain accurate position of water boundary because of using low resolution images. 2) Like all other image based object classification problems, the phenomena of ''different objects same image'' or ''different images same object'' affects the water extraction. Shadow of elevated objects (e.g. buildings, bridges, towers and trees) scattered in the remote sensing image is a typical noise objects for water extraction. In many cases, it is difficult to discriminate between water and shadow in a remote sensing image, especially in the urban region. We propose a water extraction method with two hierarchies: the statistical feature of spectral characteristic based on image segmentation and the shape feature based on shadow removing. In the first hierarchy, the Statistical Region Merging (SRM) algorithm is adopted for image segmentation. The SRM includes two key steps: one is sorting adjacent regions according to a pre-ascertained sort function, and the other one is merging adjacent regions based on a pre-ascertained merging predicate. The sort step is done one time during the whole processing without considering changes caused by merging which may cause imprecise results. Therefore, we modify the SRM with dynamic sort processing, which conducts sorting step repetitively when there is large adjacent region changes after doing merging. To achieve robust segmentation, we apply the merging region with six features (four remote sensing image bands, Normalized Difference Water Index (NDWI), and Normalized Saturation-value Difference Index (NSVDI)). All these features contribute to segment image into region of object. NDWI and NSVDI are discriminate between water and

Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-Based Classification of Polarimetric SAR and Object-Based Image Analysis

Directory of Open Access Journals (Sweden)

Simon Plank

2014-12-01

Full Text Available In developing countries, there is a high correlation between the dependence of oil exports and violent conflicts. Furthermore, even in countries which experienced a peaceful development of their oil industry, land use and environmental issues occur. Therefore, independent monitoring of oil field infrastructure may support problem solving. Earth observation data enables fast monitoring of large areas which allows comparing the real amount of land used by the oil exploitation and the companies’ contractual obligations. The target feature of this monitoring is the infrastructure of the oil exploitation, oil well pads—rectangular features of bare land covering an area of approximately 50–60 m × 100 m. This article presents an automated feature extraction procedure based on the combination of a pixel-based unsupervised classification of polarimetric synthetic aperture radar data (PolSAR and an object-based post-classification. The method is developed and tested using dual-polarimetric TerraSAR-X imagery acquired over the Doba basin in south Chad. The advantages of PolSAR are independence of the cloud coverage (vs. optical imagery and the possibility of detailed land use classification (vs. single-pol SAR. The PolSAR classification uses the polarimetric Wishart probability density function based on the anisotropy/entropy/alpha decomposition. The object-based post-classification refinement, based on properties of the feature targets such as shape and area, increases the user’s accuracy of the methodology by an order of a magnitude. The final achieved user’s and producer’s accuracy is 59%–71% in each case (area based accuracy assessment. Considering only the numbers of correctly/falsely detected oil well pads, the user’s and producer’s accuracies increase to even 74%–89%. In an iterative training procedure the best suited polarimetric speckle filter and processing parameters of the developed feature extraction procedure are

Perirhinal Cortex Resolves Feature Ambiguity in Configural Object Recognition and Perceptual Oddity Tasks

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Bartko, Susan J.; Winters, Boyer D.; Cowell, Rosemary A.; Saksida, Lisa M.; Bussey, Timothy J.

2007-01-01

The perirhinal cortex (PRh) has a well-established role in object recognition memory. More recent studies suggest that PRh is also important for two-choice visual discrimination tasks. Specifically, it has been suggested that PRh contains conjunctive representations that help resolve feature ambiguity, which occurs when a task cannot easily be…

Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

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Jae Kwon Kim

2017-01-01

Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes.

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Zhong, Zichun; Guo, Xiaohu; Cai, Yiqi; Yang, Yin; Wang, Jing; Jia, Xun; Mao, Weihua

2016-01-01

By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes

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Zichun Zhong

2016-01-01

Full Text Available By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

Spectral features based tea garden extraction from digital orthophoto maps

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Jamil, Akhtar; Bayram, Bulent; Kucuk, Turgay; Zafer Seker, Dursun

2018-05-01

The advancements in the photogrammetry and remote sensing technologies has made it possible to extract useful tangible information from data which plays a pivotal role in various application such as management and monitoring of forests and agricultural lands etc. This study aimed to evaluate the effectiveness of spectral signatures for extraction of tea gardens from 1 : 5000 scaled digital orthophoto maps obtained from Rize city in Turkey. First, the normalized difference vegetation index (NDVI) was derived from the input images to suppress the non-vegetation areas. NDVI values less than zero were discarded and the output images was normalized in the range 0-255. Individual pixels were then mapped into meaningful objects using global region growing technique. The resulting image was filtered and smoothed to reduce the impact of noise. Furthermore, geometrical constraints were applied to remove small objects (less than 500 pixels) followed by morphological opening operator to enhance the results. These objects served as building blocks for further image analysis. Finally, for the classification stage, a range of spectral values were empirically calculated for each band and applied on candidate objects to extract tea gardens. For accuracy assessment, we employed an area based similarity metric by overlapping obtained tea garden boundaries with the manually digitized tea garden boundaries created by experts of photogrammetry. The overall accuracy of the proposed method scored 89 % for tea gardens from 10 sample orthophoto maps. We concluded that exploiting the spectral signatures using object based analysis is an effective technique for extraction of dominant tree species from digital orthophoto maps.

Toward automating Hammersmith pulled-to-sit examination of infants using feature point based video object tracking.

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Dogra, Debi P; Majumdar, Arun K; Sural, Shamik; Mukherjee, Jayanta; Mukherjee, Suchandra; Singh, Arun

2012-01-01

Hammersmith Infant Neurological Examination (HINE) is a set of tests used for grading neurological development of infants on a scale of 0 to 3. These tests help in assessing neurophysiological development of babies, especially preterm infants who are born before (the fetus reaches) the gestational age of 36 weeks. Such tests are often conducted in the follow-up clinics of hospitals for grading infants with suspected disabilities. Assessment based on HINE depends on the expertise of the physicians involved in conducting the examinations. It has been noted that some of these tests, especially pulled-to-sit and lateral tilting, are difficult to assess solely based on visual observation. For example, during the pulled-to-sit examination, the examiner needs to observe the relative movement of the head with respect to torso while pulling the infant by holding wrists. The examiner may find it difficult to follow the head movement from the coronal view. Video object tracking based automatic or semi-automatic analysis can be helpful in this case. In this paper, we present a video based method to automate the analysis of pulled-to-sit examination. In this context, a dynamic programming and node pruning based efficient video object tracking algorithm has been proposed. Pulled-to-sit event detection is handled by the proposed tracking algorithm that uses a 2-D geometric model of the scene. The algorithm has been tested with normal as well as marker based videos of the examination recorded at the neuro-development clinic of the SSKM Hospital, Kolkata, India. It is found that the proposed algorithm is capable of estimating the pulled-to-sit score with sensitivity (80%-92%) and specificity (89%-96%).

Taxonomic revision of the Malagasy members of the Nesomyrmex angulatus species group using the automated morphological species delineation protocol NC-PART-clustering

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Sándor Csősz

2016-03-01

Full Text Available Background. Applying quantitative morphological approaches in systematics research is a promising way to discover cryptic biological diversity. Information obtained through twenty-first century science poses new challenges to taxonomy by offering the possibility of increased objectivity in independent and automated hypothesis formation. In recent years a number of promising new algorithmic approaches have been developed to recognize morphological diversity among insects based on multivariate morphometric analyses. These algorithms objectively delimit components in the data by automatically assigning objects into clusters. Method. In this paper, hypotheses on the diversity of the Malagasy Nesomyrmex angulatus group are formulated via a highly automated protocol involving a fusion of two algorithms, (1 Nest Centroid clustering (NC clustering and (2 Partitioning Algorithm based on Recursive Thresholding (PART. Both algorithms assign samples into clusters, making the class assignment results of different algorithms readily inferable. The results were tested by confirmatory cross-validated Linear Discriminant Analysis (LOOCV-LDA. Results. Here we reveal the diversity of a unique and largely unexplored fragment of the Malagasy ant fauna using NC-PART-clustering on continuous morphological data, an approach that brings increased objectivity to taxonomy. We describe eight morphologically distinct species, including seven new species: Nesomyrmex angulatus (Mayr, 1862, N. bidentatus sp. n., N. clypeatus sp. n., N. devius sp. n., N. exiguus sp. n., N. fragilis sp. n., N. gracilis sp. n., and N. hirtellus sp. n.. An identification key for their worker castes using morphometric data is provided. Conclusions. Combining the dimensionality reduction feature of NC clustering with the assignment of samples into clusters by PART advances the automatization of morphometry-based alpha taxonomy.

Characterisation of taro (Colocasia esculenta based on morphological and isozymic patterns markers

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SUGIYARTO

2011-03-01

Full Text Available Trimanto, Sajidan, Sugiyarto. 2011. Characterization of taro (Colocasia esculenta based on morphological and isozymic patterns markers. Nusantara Bioscience: 7-14. The aims of this research were to find out: (i the variety of Colocasia esculenta based on the morphological characteristics; (ii the variety of C. esculenta based on the isozymic banding pattern; and (iii the correlation of genetic distance based on the morphological characteristics and isozymic banding pattern. Survey research conducted in the Karanganyar district, which include high, medium and low altitude. The sample was taken using random purposive sampling technique, including 9 sampling points. The morphological data was elaborated descriptively and then made dendogram. The data on isozymic banding pattern was analyzed quantitatively based on the presence or absence of bands appeared on the gel, and then made dendogram. The correlation based on the morphological characteristics and isozymic banding pattern were analyzed based on the product-moment correlation coefficient with goodness of fit criterion. The result showed : (i in Karanganyar was founded 10 variety of C. esculenta; (ii morphological characteristics are not affected by altitude; (iii isozymic banding pattern of peroxides forms 14 banding patterns, esterase forms 11 banding patterns and shikimic dehydrogenase forms 15 banding patterns; (iv the correlation of morphological data and the isozymic banding pattern of peroxidase has good correlation (0.893542288 while esterase and shikimic dehydrogenase isozymes have very good correlation (0.917557716 and 0.9121985446; (v isozymic banding pattern of data supports the morphological character data.

A morphological comparison of narrow, low-gradient streams traversing wetland environments to alluvial streams.

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Jurmu, Michael C

2002-12-01

Twelve morphological features from research on alluvial streams are compared in four narrow, low-gradient wetland streams located in different geographic regions (Connecticut, Indiana, and Wisconsin, USA). All four reaches differed in morphological characteristics in five of the features compared (consistent bend width, bend cross-sectional shape, riffle width compared to pool width, greatest width directly downstream of riffles, and thalweg location), while three reaches differed in two comparisons (mean radius of curvature to width ratio and axial wavelength to width ratio). The remaining five features compared had at least one reach where different characteristics existed. This indicates the possibility of varying morphology for streams traversing wetland areas further supporting the concept that the unique qualities of wetland environments might also influence the controls on fluvial dynamics and the development of streams. If certain morphological features found in streams traversing wetland areas differ from current fluvial principles, then these varying features should be incorporated into future wetland stream design and creation projects. The results warrant further research on other streams traversing wetlands to determine if streams in these environments contain unique morphology and further investigation of the impact of low-energy fluvial processes on morphological development. Possible explanations for the morphology deviations in the study streams and some suggestions for stream design in wetland areas based upon the results and field observations are also presented.

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

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Jaison Bennet

2014-01-01

Full Text Available Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN, naive Bayes, and support vector machine (SVM. Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT and moving window technique (MWT is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Morphology and microstructure of composite materials

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Tiwari, S. N.; Srinivansan, K.

1991-01-01

Lightweight continuous carbon fiber based polymeric composites are currently enjoying increasing acceptance as structural materials capable of replacing metals and alloys in load bearing applications. As with most new materials, these composites are undergoing trials with several competing processing techniques aimed at cost effectively producing void free consolidations with good mechanical properties. As metallic materials have been in use for several centuries, a considerable database exists on their morphology - microstructure; and the interrelationships between structure and properties have been well documented. Numerous studies on composites have established the crucial relationship between microstructure - morphology and properties. The various microstructural and morphological features of composite materials, particularly those accompanying different processing routes, are documented.

Object-based encoding in visual working memory: a life span study.

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Zhang, Qiong; Shen, Mowei; Tang, Ning; Zhao, Guohua; Gao, Zaifeng

2013-08-20

Recent studies on development of visual working memory (VWM) predominantly focus on VWM capacity and spatial-based information filtering in VWM. Here we explored another new aspect of VWM development: object-based encoding (OBE), which refers to the fact that even if one feature dimension is required to be selected into VWM, the other irrelevant dimensions are also extracted. We explored the OBE in children, young adults, and old adults, by probing an "irrelevant-change distracting effect" in which a change of stored irrelevant feature dramatically affects the performance of task-relevant features in a change-detection task. Participants were required to remember two or four simple colored shapes, while color was used as the relevant dimension. We found that changes to irrelevant shapes led to a significant distracting effect across the three age groups in both load conditions; however, children showed a greater degree of OBE than did young and old adults. These results suggest that OBE exists in VWM over the life span (6-67 years), yet continues to develop along with VWM.

Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

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Pradipta Maji

Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

Inter-relationships between objective handwriting features and executive control among children with developmental dysgraphia

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2018-01-01

Objective To describe handwriting and executive control features and their inter-relationships among children with developmental dysgraphia, in comparison to controls. Method Participants included 64 children, aged 10–12 years, 32 with dysgraphia based on the Handwriting Proficiency Screening Questionnaire (HPSQ) and 32 matched controls. Children copied a paragraph onto paper affixed to a digitizer that supplied handwriting process objective measures (Computerized Penmanship Evaluation Tool (ComPET). Their written product was evaluated by the Hebrew Handwriting Evaluation (HHE). Parents completed the Behavior Rating Inventory of Executive Function (BRIEF) questionnaire about their child's executive control abilities. Results Significant group differences were found for handwriting performance measures (HHE and ComPET) and executive control domains (BRIEF). Based on one discriminate function, including handwriting performance and executive control measures, 98.4% of the participants were correctly classified into groups. Significant correlations were found in each group between working memory and legibility as well as for other executive domains and handwriting measures. Furthermore, twenty percent of the variability of the mean pressure applied towards the writing surface among children with was explained by their 'emotional control' (BRIEF). Conclusion The results strongly suggest consideration of executive control domains to obtain better insight into handwriting impairment characteristics among children with dysgraphia to improve their identification, evaluation and the intervention process. PMID:29689111

Development of automatic extraction of the corpus callosum from magnetic resonance imaging of the head and examination of the early dementia objective diagnostic technique in feature analysis

International Nuclear Information System (INIS)

Kodama, Naoki; Kaneko, Tomoyuki

2005-01-01

We examined the objective diagnosis of dementia based on changes in the corpus callosum. We examined midsagittal head MR images of 17 early dementia patients (2 men and 15 women; mean age, 77.2±3.3 years) and 18 healthy elderly controls (2 men and 16 women; mean age, 73.8±6.5 years), 35 subjects altogether. First, the corpus callosum was automatically extracted from the MR images. Next, early dementia was compared with the healthy elderly individuals using 5 features of the straight-line methods, 5 features of the Run-Length Matrix, and 6 features of the Co-occurrence Matrix from the corpus callosum. Automatic extraction of the corpus callosum showed an accuracy rate of 84.1±3.7%. A statistically significant difference was found in 6 of the 16 features between early dementia patients and healthy elderly controls. Discriminant analysis using the 6 features demonstrated a sensitivity of 88.2% and specificity of 77.8%, with an overall accuracy of 82.9%. These results indicate that feature analysis based on changes in the corpus callosum can be used as an objective diagnostic technique for early dementia. (author)

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

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Tao Ye

2018-06-01

Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

Effect of geometrical features various objects on the data quality obtained with measured by TLS

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Pawłowicz, J. A.

2017-08-01

Collecting data on different building structures using Terrestrial Laser Scanning (TLS) has become in recent years a very popular due to minimize the time required to complete the task as compared to traditional methods. Technical parameters of 3D scanning devices (digitizers) are increasingly being improved, and the accuracy of the data collected allows you to play not only the geometry of an existing object in a digital image, but also enables the assessment of his condition. This is possible thanks to the digitalization of existing objects e.g., a 3D laser scanner, with which is obtained a digital data base is presented in the form of a cloud of points and by using reverse engineering. Measurements using laser scanners depends to a large extent, on the quality of the returning beam reflected from the target surface, towards the receiver. High impact on the strength and quality of the beam returning to the geometric features of the object. These properties may contribute to the emergence of some, sometimes even serious errors during scanning of various shapes. The study defined the effect of the laser beam distortion during the measurement objects with the same material but with different geometrical features on their three-dimensional imaging obtained from measurements made using TLS. We present the problem of data quality, dependent on the deflection of the beam intensity and shape of the object selected examples. The knowledge of these problems allows to obtain valuable data necessary for the implementation of digitization and the visualization of virtually any building structure made of any materials. The studies has been proven that the increase in the density of scanning does not affect the values of mean square error. The increase in the angle of incidence of the beam onto a flat surface, however, causes a decrease in the intensity of scattered radiation that reaches the receiver. The article presents an analysis of the laser beam reflected from broken at

Morphological features of choroidal metastases: An OCT analysis

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Ludovico Iannetti

2013-01-01

Full Text Available The morphological characteristics and retinal changes of chroidal metastases using Spectral Domain OCT are described in a case with primary lung adenocarcinoma and secondary choroidal involvement.

KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS

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F. Boochs

2012-07-01

Full Text Available Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This “understanding” enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL, used for formulating the knowledge base and the Semantic Web Rule Language (SWRL with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists’ knowledge of the scene and algorithmic processing.

Knowledge-Based Object Detection in Laser Scanning Point Clouds

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Boochs, F.; Karmacharya, A.; Marbs, A.

2012-07-01

Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This "understanding" enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL), used for formulating the knowledge base and the Semantic Web Rule Language (SWRL) with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists' knowledge of the scene and algorithmic processing.

Simulation on scattering features of biological tissue based on generated refractive-index model

International Nuclear Information System (INIS)

Wang Baoyong; Ding Zhihua

2011-01-01

Important information on morphology of biological tissue can be deduced from elastic scattering spectra, and their analyses are based on the known refractive-index model of tissue. In this paper, a new numerical refractive-index model is put forward, and its scattering properties are intensively studied. Spectral decomposition [1] is a widely used method to generate random medium in geology, but it is never used in biology. Biological tissue is different from geology in the sense of random medium. Autocorrelation function describe almost all of features in geology, but biological tissue is not as random as geology, its structure is regular in the sense of fractal geometry [2] , and fractal dimension can be used to describe its regularity under random. Firstly scattering theories of this fractal media are reviewed. Secondly the detailed generation process of refractive-index is presented. Finally the scattering features are simulated in FDTD (Finite Difference Time Domain) Solutions software. From the simulation results, we find that autocorrelation length and fractal dimension controls scattering feature of biological tissue.

EEG feature selection method based on decision tree.

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Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review

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Weal, Mark; Morrison, Leanne; Yardley, Lucy

2018-01-01

Background Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. Objective The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. Methods Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. Results A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. Conclusions Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included

Superpixel-Based Feature for Aerial Image Scene Recognition

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Hongguang Li

2018-01-01

Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

Aerial surveillance based on hierarchical object classification for ground target detection

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Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

2015-03-01

Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary

Science.gov (United States)

Kangale, Akshay; Krishna Kumar, S.; Arshad Naeem, Mohd; Williams, Mark; Tiwari, M. K.

2016-10-01

With the massive growth of the internet, product reviews increasingly serve as an important source of information for customers to make choices online. Customers depend on these reviews to understand users' experience, and manufacturers rely on this user-generated content to capture user sentiments about their product. Therefore, it is in the best interest of both customers and manufacturers to have a portal where they can read a complete comprehensive summary of these reviews in minimum time. With this in mind, we arrived at our first objective which is to generate a feature-based review-summary. Our second objective is to develop a predictive model to know the next week's product sales based on numerical review ratings and textual features embedded in the reviews. When it comes to product features, every user has different priorities for different features. To capture this aspect of decision-making, we have designed a new mechanism to generate a numerical rating for every feature of the product individually. The data have been collected from a well-known commercial website for two different products. The validation of the model is carried out using a crowd-sourcing technique.

A multicolor photometric study of the tidal features in interacting galaxies

International Nuclear Information System (INIS)

Schombert, J.M.; Wallin, J.F.; Struck-Marcell, C.

1990-01-01

Four-color surface photometry (BVri) is presented for low-surface-brightness tidal features in interacting galaxies. Objects were selected on the basis of visual morphology including a cross section of tails, bridges, plumes, shells, and extended envelopes. Intensity cross sections and surface brightness suggests that plumes are face-on or near face-on sheets; tails and bridges are more nearly one-dimensional, linear figures. In many cases the colors of tidal features are similar to the outer regions of the primary galaxies, confirming the stripping origin hypothesis. However, large color variations are found among the morphological components within most systems, and within individual components. Blue colors in primaries and tidal features are most dramatic in B-V, not V-1, indicating that star formation, not metallicity or age, is the dominant component. There is clear evidence in the sample of a correlation between the magnitude of the color variation and the time. The color variations are largest a short time after the beginning of the interaction, and they diminish to a very low level in merged systems. This correlation provides an alternate estimator of interaction age in systems with ambiguous morphologies. 49 refs

Spatiotemporal Features for Asynchronous Event-based Data

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Image edge detection based tool condition monitoring with morphological component analysis.

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Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng

2017-07-01

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images

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ZHANG Zhiqiang

2018-01-01

Full Text Available Timely and accurate change detection of buildings provides important information for urban planning and management.Accompanying with the rapid development of satellite remote sensing technology,detecting building changes from high-resolution remote sensing images have received wide attention.Given that pixel-based methods of change detection often lead to low accuracy while object-based methods are complicated for uses,this research proposes a method that combines pixel-based and object-based methods for detecting building changes from high-resolution remote sensing images.First,based on the multiple features extracted from the high-resolution images,a random forest classifier is applied to detect changed building at the pixel level.Then,a segmentation method is applied to segement the post-phase remote sensing image and to get post-phase image objects.Finally,both changed building at the pixel level and post-phase image objects are fused to recognize the changed building objects.Multi-temporal QuickBird images are used as experiment data for building change detection with high-resolution remote sensing images,the results indicate that the proposed method could reduce the influence of environmental difference,such as light intensity and view angle,on building change detection,and effectively improve the accuracies of building change detection.

Morphology-based Enhancement of a French SIMPLE Lexicon

OpenAIRE

Namer , Fiammetta; Bouillon , Pierrette; Jacquey , Evelyne; Ruimy , Nilda

2009-01-01

International audience; In this paper, we propose a semi-automatic methodology for acquiring a French SIMPLE lexicon based on the morphological properties of complex words. This method combines the results of the French morphological analyzer DériF with infor-mation from general lexical resources and corpora, when available. It is evaluated on a set of neolo-gisms extracted from Le Monde newspaper cor-pora.

Cognitive object recognition system (CORS)

Science.gov (United States)

Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

2010-04-01

We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

Geographic Object-Based Image Analysis – Towards a new paradigm

Science.gov (United States)

Blaschke, Thomas; Hay, Geoffrey J.; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm. PMID:24623958

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding.

Directory of Open Access Journals (Sweden)

Khan BahadarKhan

Full Text Available Diabetic Retinopathy (DR harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction and STARE (STructured Analysis of the REtina databases along with the ground truth data that has been precisely marked by the experts.

Aerodynamic multi-objective integrated optimization based on principal component analysis

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Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

Cortical mechanisms for trans-saccadic memory and integration of multiple object features

Science.gov (United States)

Prime, Steven L.; Vesia, Michael; Crawford, J. Douglas

2011-01-01

Constructing an internal representation of the world from successive visual fixations, i.e. separated by saccadic eye movements, is known as trans-saccadic perception. Research on trans-saccadic perception (TSP) has been traditionally aimed at resolving the problems of memory capacity and visual integration across saccades. In this paper, we review this literature on TSP with a focus on research showing that egocentric measures of the saccadic eye movement can be used to integrate simple object features across saccades, and that the memory capacity for items retained across saccades, like visual working memory, is restricted to about three to four items. We also review recent transcranial magnetic stimulation experiments which suggest that the right parietal eye field and frontal eye fields play a key functional role in spatial updating of objects in TSP. We conclude by speculating on possible cortical mechanisms for governing egocentric spatial updating of multiple objects in TSP. PMID:21242142

Innovations in individual feature history management - The significance of feature-based temporal model

Science.gov (United States)

Choi, J.; Seong, J.C.; Kim, B.; Usery, E.L.

2008-01-01

A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO's temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO's temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure. ?? Springer Science+Business Media, LLC 2007.

A wavelet-based Bayesian framework for 3D object segmentation in microscopy

Science.gov (United States)

Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

2012-03-01

In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

Weak localization of electromagnetic waves and opposition phenomena exhibited by high-albedo atmosphereless solar system objects.

Science.gov (United States)

Mishchenko, Michael I; Rosenbush, Vera K; Kiselev, Nikolai N