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.

Object feature extraction and recognition model

International Nuclear Information System (INIS)

Wan Min; Xiang Rujian; Wan Yongxing

2001-01-01

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

Breaking object correspondence across saccadic eye movements deteriorates object recognition

Directory of Open Access Journals (Sweden)

Christian H. Poth

2015-12-01

Full Text Available Visual perception is based on information processing during periods of eye fixations that are interrupted by fast saccadic eye movements. The ability to sample and relate information on task-relevant objects across fixations implies that correspondence between presaccadic and postsaccadic objects is established. Postsaccadic object information usually updates and overwrites information on the corresponding presaccadic object. The presaccadic object representation is then lost. In contrast, the presaccadic object is conserved when object correspondence is broken. This helps transsaccadic memory but it may impose attentional costs on object recognition. Therefore, we investigated how breaking object correspondence across the saccade affects postsaccadic object recognition. In Experiment 1, object correspondence was broken by a brief postsaccadic blank screen. Observers made a saccade to a peripheral object which was displaced during the saccade. This object reappeared either immediately after the saccade or after the blank screen. Within the postsaccadic object, a letter was briefly presented (terminated by a mask. Observers reported displacement direction and letter identity in different blocks. Breaking object correspondence by blanking improved displacement identification but deteriorated postsaccadic letter recognition. In Experiment 2, object correspondence was broken by changing the object’s contrast-polarity. There were no object displacements and observers only reported letter identity. Again, breaking object correspondence deteriorated postsaccadic letter recognition. These findings identify transsaccadic object correspondence as a key determinant of object recognition across the saccade. This is in line with the recent hypothesis that breaking object correspondence results in separate representations of presaccadic and postsaccadic objects which then compete for limited attentional processing resources (Schneider, 2013. Postsaccadic

Running Improves Pattern Separation during Novel Object Recognition.

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Bolz, Leoni; Heigele, Stefanie; Bischofberger, Josef

2015-10-09

Running increases adult neurogenesis and improves pattern separation in various memory tasks including context fear conditioning or touch-screen based spatial learning. However, it is unknown whether pattern separation is improved in spontaneous behavior, not emotionally biased by positive or negative reinforcement. Here we investigated the effect of voluntary running on pattern separation during novel object recognition in mice using relatively similar or substantially different objects.We show that running increases hippocampal neurogenesis but does not affect object recognition memory with 1.5 h delay after sample phase. By contrast, at 24 h delay, running significantly improves recognition memory for similar objects, whereas highly different objects can be distinguished by both, running and sedentary mice. These data show that physical exercise improves pattern separation, independent of negative or positive reinforcement. In sedentary mice there is a pronounced temporal gradient for remembering object details. In running mice, however, increased neurogenesis improves hippocampal coding and temporally preserves distinction of novel objects from familiar ones.

Infant visual attention and object recognition.

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Reynolds, Greg D

2015-05-15

This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition.

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Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-05-22

Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach

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.

Eye movements during object recognition in visual agnosia.

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Charles Leek, E; Patterson, Candy; Paul, Matthew A; Rafal, Robert; Cristino, Filipe

2012-07-01

This paper reports the first ever detailed study about eye movement patterns during single object recognition in visual agnosia. Eye movements were recorded in a patient with an integrative agnosic deficit during two recognition tasks: common object naming and novel object recognition memory. The patient showed normal directional biases in saccades and fixation dwell times in both tasks and was as likely as controls to fixate within object bounding contour regardless of recognition accuracy. In contrast, following initial saccades of similar amplitude to controls, the patient showed a bias for short saccades. In object naming, but not in recognition memory, the similarity of the spatial distributions of patient and control fixations was modulated by recognition accuracy. The study provides new evidence about how eye movements can be used to elucidate the functional impairments underlying object recognition deficits. We argue that the results reflect a breakdown in normal functional processes involved in the integration of shape information across object structure during the visual perception of shape. Copyright © 2012 Elsevier Ltd. All rights reserved.

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

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Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

Integration trumps selection in object recognition

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Saarela, Toni P.; Landy, Michael S.

2015-01-01

Summary Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several “cues” (color, luminance, texture etc.), and humans can integrate sensory cues to improve detection and recognition [1–3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue-invariance by responding to a given shape independent of the visual cue defining it [5–8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10,11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11,12], imaging [13–16], and single-cell and neural population recordings [17,18]. Besides single features, attention can select whole objects [19–21]. Objects are among the suggested “units” of attention because attention to a single feature of an object causes the selection of all of its features [19–21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near-optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

Integration trumps selection in object recognition.

Science.gov (United States)

Saarela, Toni P; Landy, Michael S

2015-03-30

Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. Copyright © 2015 Elsevier Ltd. All rights reserved.

View-invariant object recognition ability develops after discrimination, not mere exposure, at several viewing angles.

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Yamashita, Wakayo; Wang, Gang; Tanaka, Keiji

2010-01-01

One usually fails to recognize an unfamiliar object across changes in viewing angle when it has to be discriminated from similar distractor objects. Previous work has demonstrated that after long-term experience in discriminating among a set of objects seen from the same viewing angle, immediate recognition of the objects across 30-60 degrees changes in viewing angle becomes possible. The capability for view-invariant object recognition should develop during the within-viewing-angle discrimination, which includes two kinds of experience: seeing individual views and discriminating among the objects. The aim of the present study was to determine the relative contribution of each factor to the development of view-invariant object recognition capability. Monkeys were first extensively trained in a task that required view-invariant object recognition (Object task) with several sets of objects. The animals were then exposed to a new set of objects over 26 days in one of two preparatory tasks: one in which each object view was seen individually, and a second that required discrimination among the objects at each of four viewing angles. After the preparatory period, we measured the monkeys' ability to recognize the objects across changes in viewing angle, by introducing the object set to the Object task. Results indicated significant view-invariant recognition after the second but not first preparatory task. These results suggest that discrimination of objects from distractors at each of several viewing angles is required for the development of view-invariant recognition of the objects when the distractors are similar to the objects.

Hippocampal histone acetylation regulates object recognition and the estradiol-induced enhancement of object recognition.

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Zhao, Zaorui; Fan, Lu; Fortress, Ashley M; Boulware, Marissa I; Frick, Karyn M

2012-02-15

Histone acetylation has recently been implicated in learning and memory processes, yet necessity of histone acetylation for such processes has not been demonstrated using pharmacological inhibitors of histone acetyltransferases (HATs). As such, the present study tested whether garcinol, a potent HAT inhibitor in vitro, could impair hippocampal memory consolidation and block the memory-enhancing effects of the modulatory hormone 17β-estradiol E2. We first showed that bilateral infusion of garcinol (0.1, 1, or 10 μg/side) into the dorsal hippocampus (DH) immediately after training impaired object recognition memory consolidation in ovariectomized female mice. A behaviorally effective dose of garcinol (10 μg/side) also significantly decreased DH HAT activity. We next examined whether DH infusion of a behaviorally subeffective dose of garcinol (1 ng/side) could block the effects of DH E2 infusion on object recognition and epigenetic processes. Immediately after training, ovariectomized female mice received bilateral DH infusions of vehicle, E2 (5 μg/side), garcinol (1 ng/side), or E2 plus garcinol. Forty-eight hours later, garcinol blocked the memory-enhancing effects of E2. Garcinol also reversed the E2-induced increase in DH histone H3 acetylation, HAT activity, and levels of the de novo methyltransferase DNMT3B, as well as the E2-induced decrease in levels of the memory repressor protein histone deacetylase 2. Collectively, these findings suggest that histone acetylation is critical for object recognition memory consolidation and the beneficial effects of E2 on object recognition. Importantly, this work demonstrates that the role of histone acetylation in memory processes can be studied using a HAT inhibitor.

Associative recognition and the hippocampus: differential effects of hippocampal lesions on object-place, object-context and object-place-context memory.

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Langston, Rosamund F; Wood, Emma R

2010-10-01

The hippocampus is thought to be required for the associative recognition of objects together with the spatial or temporal contexts in which they occur. However, recent data showing that rats with fornix lesions perform as well as controls in an object-place task, while being impaired on an object-place-context task (Eacott and Norman (2004) J Neurosci 24:1948-1953), suggest that not all forms of context-dependent associative recognition depend on the integrity of the hippocampus. To examine the role of the hippocampus in context-dependent recognition directly, the present study tested the effects of large, selective, bilateral hippocampus lesions in rats on performance of a series of spontaneous recognition memory tasks: object recognition, object-place recognition, object-context recognition and object-place-context recognition. Consistent with the effects of fornix lesions, animals with hippocampus lesions were impaired only on the object-place-context task. These data confirm that not all forms of context-dependent associative recognition are mediated by the hippocampus. Subsequent experiments suggested that the object-place task does not require an allocentric representation of space, which could account for the lack of impairment following hippocampus lesions. Importantly, as the object-place-context task has similar spatial requirements, the selective deficit in object-place-context recognition suggests that this task requires hippocampus-dependent neural processes distinct from those required for allocentric spatial memory, or for object memory, object-place memory or object-context memory. Two possibilities are that object, place, and context information converge only in the hippocampus, or that recognition of integrated object-place-context information requires a hippocampus-dependent mode of retrieval, such as recollection. © 2009 Wiley-Liss, Inc.

Active exploration and keypoint clustering for object recognition

NARCIS (Netherlands)

Kootstra, G.W.; Ypma, J; de Boer, B.

2008-01-01

Object recognition is a challenging problem for artificial systems. This is especially true for objects that are placed in cluttered and uncontrolled environments. To challenge this problem, we discuss an active approach to object recognition. Instead of passively observing objects, we use a robot

The Role of Perceptual Load in Object Recognition

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Lavie, Nilli; Lin, Zhicheng; Zokaei, Nahid; Thoma, Volker

2009-01-01

Predictions from perceptual load theory (Lavie, 1995, 2005) regarding object recognition across the same or different viewpoints were tested. Results showed that high perceptual load reduces distracter recognition levels despite always presenting distracter objects from the same view. They also showed that the levels of distracter recognition were…

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

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

2013-08-01

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

One-single physical exercise session after object recognition learning promotes memory persistence through hippocampal noradrenergic mechanisms.

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da Silva de Vargas, Liane; Neves, Ben-Hur Souto das; Roehrs, Rafael; Izquierdo, Iván; Mello-Carpes, Pâmela

2017-06-30

Previously we showed the involvement of the hippocampal noradrenergic system in the consolidation and persistence of object recognition (OR) memory. Here we show that one-single physical exercise session performed immediately after learning promotes OR memory persistence and increases norepinephrine levels in the hippocampus. Additionally, effects of exercise on memory are avoided by an intra-hippocampal beta-adrenergic antagonist infusion. Taken together, these results suggest that exercise effects on memory can be related to noradrenergic mechanisms and acute physical exercise can be a non-pharmacological intervention to assist memory consolidation and persistence, with few or no side effects. Copyright © 2017 Elsevier B.V. All rights reserved.

Crossmodal object recognition in rats with and without multimodal object pre-exposure: no effect of hippocampal lesions.

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Reid, James M; Jacklin, Derek L; Winters, Boyer D

2012-10-01

The neural mechanisms and brain circuitry involved in the formation, storage, and utilization of multisensory object representations are poorly understood. We have recently introduced a crossmodal object recognition (CMOR) task that enables the study of such questions in rats. Our previous research has indicated that the perirhinal and posterior parietal cortices functionally interact to mediate spontaneous (tactile-to-visual) CMOR performance in rats; however, it remains to be seen whether other brain regions, particularly those receiving polymodal sensory inputs, contribute to this cognitive function. In the current study, we assessed the potential contribution of one such polymodal region, the hippocampus (HPC), to crossmodal object recognition memory. Rats with bilateral excitotoxic HPC lesions were tested in two versions of crossmodal object recognition: (1) the original CMOR task, which requires rats to compare between a stored tactile object representation and visually-presented objects to discriminate the novel and familiar stimuli; and (2) a novel 'multimodal pre-exposure' version of the CMOR task (PE/CMOR), in which simultaneous exploration of the tactile and visual sensory features of an object 24 h prior to the sample phase enhances CMOR performance across longer retention delays. Hippocampus-lesioned rats performed normally on both crossmodal object recognition tasks, but were impaired on a radial arm maze test of spatial memory, demonstrating the functional effectiveness of the lesions. These results strongly suggest that the HPC, despite its polymodal anatomical connections, is not critically involved in tactile-to-visual crossmodal object recognition memory. Copyright © 2012 Elsevier Inc. All rights reserved.

Object Recognition and Localization: The Role of Tactile Sensors

Directory of Open Access Journals (Sweden)

Achint Aggarwal

2014-02-01

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

Differential effects of spaced vs. massed training in long-term object-identity and object-location recognition memory.

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Bello-Medina, Paola C; Sánchez-Carrasco, Livia; González-Ornelas, Nadia R; Jeffery, Kathryn J; Ramírez-Amaya, Víctor

2013-08-01

Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently. Copyright © 2013 Elsevier B.V. All rights reserved.

Recognition judgments and the performance of the recognition heuristic depend on the size of the reference class

Directory of Open Access Journals (Sweden)

Ulrich Hoffrage

2011-02-01

Full Text Available In a series of three experiments, participants made inferences about which one of a pair of two objects scored higher on a criterion. The first experiment was designed to contrast the prediction of Probabilistic Mental Model theory (Gigerenzer, Hoffrage, and Kleinbolting, 1991 concerning sampling procedure with the hard-easy effect. The experiment failed to support the theory's prediction that a particular pair of randomly sampled item sets would differ in percentage correct; but the observation that German participants performed practically as well on comparisons between U.S. cities (many of which they did not even recognize than on comparisons between German cities (about which they knew much more ultimately led to the formulation of the recognition heuristic. Experiment 2 was a second, this time successful, attempt to unconfound item difficulty and sampling procedure. In Experiment 3, participants' knowledge and recognition of each city was elicited, and how often this could be used to make an inference was manipulated. Choices were consistent with the recognition heuristic in about 80% of the cases when it discriminated and people had no additional knowledge about the recognized city (and in about 90% when they had such knowledge. The frequency with which the heuristic could be used affected the percentage correct, mean confidence, and overconfidence as predicted. The size of the reference class, which was also manipulated, modified these effects in meaningful and theoretically important ways.

Object recognition memory: neurobiological mechanisms of encoding, consolidation and retrieval.

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Winters, Boyer D; Saksida, Lisa M; Bussey, Timothy J

2008-07-01

Tests of object recognition memory, or the judgment of the prior occurrence of an object, have made substantial contributions to our understanding of the nature and neurobiological underpinnings of mammalian memory. Only in recent years, however, have researchers begun to elucidate the specific brain areas and neural processes involved in object recognition memory. The present review considers some of this recent research, with an emphasis on studies addressing the neural bases of perirhinal cortex-dependent object recognition memory processes. We first briefly discuss operational definitions of object recognition and the common behavioural tests used to measure it in non-human primates and rodents. We then consider research from the non-human primate and rat literature examining the anatomical basis of object recognition memory in the delayed nonmatching-to-sample (DNMS) and spontaneous object recognition (SOR) tasks, respectively. The results of these studies overwhelmingly favor the view that perirhinal cortex (PRh) is a critical region for object recognition memory. We then discuss the involvement of PRh in the different stages--encoding, consolidation, and retrieval--of object recognition memory. Specifically, recent work in rats has indicated that neural activity in PRh contributes to object memory encoding, consolidation, and retrieval processes. Finally, we consider the pharmacological, cellular, and molecular factors that might play a part in PRh-mediated object recognition memory. Recent studies in rodents have begun to indicate the remarkable complexity of the neural substrates underlying this seemingly simple aspect of declarative memory.

Color descriptors for object category recognition

NARCIS (Netherlands)

van de Sande, K.E.A.; Gevers, T.; Snoek, C.G.M.

2008-01-01

Category recognition is important to access visual information on the level of objects. A common approach is to compute image descriptors first and then to apply machine learning to achieve category recognition from annotated examples. As a consequence, the choice of image descriptors is of great

Evaluating color descriptors for object and scene recognition.

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van de Sande, Koen E A; Gevers, Theo; Snoek, Cees G M

2010-09-01

Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

Directory of Open Access Journals (Sweden)

Juan M. Gandarias

2018-02-01

Full Text Available The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM. Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more, with a lower mean of pressure values (up to 72% less than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI. A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78% with a rigid sensor.

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

Science.gov (United States)

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

Very deep recurrent convolutional neural network for object recognition

Science.gov (United States)

Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

2017-03-01

In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

Neural substrates of view-invariant object recognition developed without experiencing rotations of the objects.

Science.gov (United States)

Okamura, Jun-Ya; Yamaguchi, Reona; Honda, Kazunari; Wang, Gang; Tanaka, Keiji

2014-11-05

One fails to recognize an unfamiliar object across changes in viewing angle when it must be discriminated from similar distractor objects. View-invariant recognition gradually develops as the viewer repeatedly sees the objects in rotation. It is assumed that different views of each object are associated with one another while their successive appearance is experienced in rotation. However, natural experience of objects also contains ample opportunities to discriminate among objects at each of the multiple viewing angles. Our previous behavioral experiments showed that after experiencing a new set of object stimuli during a task that required only discrimination at each of four viewing angles at 30° intervals, monkeys could recognize the objects across changes in viewing angle up to 60°. By recording activities of neurons from the inferotemporal cortex after various types of preparatory experience, we here found a possible neural substrate for the monkeys' performance. For object sets that the monkeys had experienced during the task that required only discrimination at each of four viewing angles, many inferotemporal neurons showed object selectivity covering multiple views. The degree of view generalization found for these object sets was similar to that found for stimulus sets with which the monkeys had been trained to conduct view-invariant recognition. These results suggest that the experience of discriminating new objects in each of several viewing angles develops the partially view-generalized object selectivity distributed over many neurons in the inferotemporal cortex, which in turn bases the monkeys' emergent capability to discriminate the objects across changes in viewing angle. Copyright © 2014 the authors 0270-6474/14/3415047-13$15.00/0.

Object recognition in images by human vision and computer vision

NARCIS (Netherlands)

Chen, Q.; Dijkstra, J.; Vries, de B.

2010-01-01

Object recognition plays a major role in human behaviour research in the built environment. Computer based object recognition techniques using images as input are challenging, but not an adequate representation of human vision. This paper reports on the differences in object shape recognition

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...

Left posterior BA37 is involved in object recognition: a TMS study

DEFF Research Database (Denmark)

Stewart, Lauren; Meyer, Bernd-Ulrich; Frith, Uta

2001-01-01

Functional imaging studies have proposed a role for left BA37 in phonological retrieval, semantic processing, face processing and object recognition. The present study targeted the posterior aspect of BA37 to see whether a deficit, specific to one of the above types of processing could be induced...... to name pictures when TMS was given over lBA37 compared to vertex or rBA37. rTMS over lBA37 had no significant effect on word reading, nonword reading or colour naming. The picture naming deficit is suggested to result from a disruption to object recognition processes. This study corroborates the finding...... from a recent imaging study, that the most posterior part of left hemispheric BA37 has a necessary role in object recognition....

Utilization-based object recognition in confined spaces

Science.gov (United States)

Shirkhodaie, Amir; Telagamsetti, Durga; Chan, Alex L.

2017-05-01

Recognizing substantially occluded objects in confined spaces is a very challenging problem for ground-based persistent surveillance systems. In this paper, we discuss the ontology inference of occluded object recognition in the context of in-vehicle group activities (IVGA) and describe an approach that we refer to as utilization-based object recognition method. We examine the performance of three types of classifiers tailored for the recognition of objects with partial visibility, namely, (1) Hausdorff Distance classifier, (2) Hamming Network classifier, and (3) Recurrent Neural Network classifier. In order to train these classifiers, we have generated multiple imagery datasets containing a mixture of common objects appearing inside a vehicle with full or partial visibility and occultation. To generate dynamic interactions between multiple people, we model the IVGA scenarios using a virtual simulation environment, in which a number of simulated actors perform a variety of IVGA tasks independently or jointly. This virtual simulation engine produces the much needed imagery datasets for the verification and validation of the efficiency and effectiveness of the selected object recognizers. Finally, we improve the performance of these object recognizers by incorporating human gestural information that differentiates various object utilization or handling methods through the analyses of dynamic human-object interactions (HOI), human-human interactions (HHI), and human-vehicle interactions (HVI) in the context of IVGA.

Contextual System of Symbol Structural Recognition based on an Object-Process Methodology

OpenAIRE

Delalandre, Mathieu

2005-01-01

We present in this paper a symbol recognition system for the graphic documents. This one is based on a contextual approach for symbol structural recognition exploiting an Object-Process Methodology. It uses a processing library composed of structural recognition processings and contextual evaluation processings. These processings allow our system to deal with the multi-representation of symbols. The different processings are controlled, in an automatic way, by an inference engine during the r...

Transformation-tolerant object recognition in rats revealed by visual priming.

Science.gov (United States)

Tafazoli, Sina; Di Filippo, Alessandro; Zoccolan, Davide

2012-01-04

Successful use of rodents as models for studying object vision crucially depends on the ability of their visual system to construct representations of visual objects that tolerate (i.e., remain relatively unchanged with respect to) the tremendous changes in object appearance produced, for instance, by size and viewpoint variation. Whether this is the case is still controversial, despite some recent demonstration of transformation-tolerant object recognition in rats. In fact, it remains unknown to what extent such a tolerant recognition has a spontaneous, perceptual basis, or, alternatively, mainly reflects learning of arbitrary associative relations among trained object appearances. In this study, we addressed this question by training rats to categorize a continuum of morph objects resulting from blending two object prototypes. The resulting psychometric curve (reporting the proportion of responses to one prototype along the morph line) served as a reference when, in a second phase of the experiment, either prototype was briefly presented as a prime, immediately before a test morph object. The resulting shift of the psychometric curve showed that recognition became biased toward the identity of the prime. Critically, this bias was observed also when the primes were transformed along a variety of dimensions (i.e., size, position, viewpoint, and their combination) that the animals had never experienced before. These results indicate that rats spontaneously perceive different views/appearances of an object as similar (i.e., as instances of the same object) and argue for the existence of neuronal substrates underlying formation of transformation-tolerant object representations in rats.

Object Recognition System-on-Chip Using the Support Vector Machines

Directory of Open Access Journals (Sweden)

Houzet Dominique

2005-01-01

Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

Cultural differences in visual object recognition in 3-year-old children

Science.gov (United States)

Kuwabara, Megumi; Smith, Linda B.

2016-01-01

Recent research indicates that culture penetrates fundamental processes of perception and cognition (e.g. Nisbett & Miyamoto, 2005). Here, we provide evidence that these influences begin early and influence how preschool children recognize common objects. The three tasks (n=128) examined the degree to which nonface object recognition by 3 year olds was based on individual diagnostic features versus more configural and holistic processing. Task 1 used a 6-alternative forced choice task in which children were asked to find a named category in arrays of masked objects in which only 3 diagnostic features were visible for each object. U.S. children outperformed age-matched Japanese children. Task 2 presented pictures of objects to children piece by piece. U.S. children recognized the objects given fewer pieces than Japanese children and likelihood of recognition increased for U.S., but not Japanese children when the piece added was rated by both U.S. and Japanese adults as highly defining. Task 3 used a standard measure of configural progressing, asking the degree to which recognition of matching pictures was disrupted by the rotation of one picture. Japanese children’s recognition was more disrupted by inversion than was that of U.S. children, indicating more configural processing by Japanese than U.S. children. The pattern suggests early cross-cultural differences in visual processing; findings that raise important questions about how visual experiences differ across cultures and about universal patterns of cognitive development. PMID:26985576

Cultural differences in visual object recognition in 3-year-old children.

Science.gov (United States)

Kuwabara, Megumi; Smith, Linda B

2016-07-01

Recent research indicates that culture penetrates fundamental processes of perception and cognition. Here, we provide evidence that these influences begin early and influence how preschool children recognize common objects. The three tasks (N=128) examined the degree to which nonface object recognition by 3-year-olds was based on individual diagnostic features versus more configural and holistic processing. Task 1 used a 6-alternative forced choice task in which children were asked to find a named category in arrays of masked objects where only three diagnostic features were visible for each object. U.S. children outperformed age-matched Japanese children. Task 2 presented pictures of objects to children piece by piece. U.S. children recognized the objects given fewer pieces than Japanese children, and the likelihood of recognition increased for U.S. children, but not Japanese children, when the piece added was rated by both U.S. and Japanese adults as highly defining. Task 3 used a standard measure of configural progressing, asking the degree to which recognition of matching pictures was disrupted by the rotation of one picture. Japanese children's recognition was more disrupted by inversion than was that of U.S. children, indicating more configural processing by Japanese than U.S. children. The pattern suggests early cross-cultural differences in visual processing; findings that raise important questions about how visual experiences differ across cultures and about universal patterns of cognitive development. Copyright © 2016 Elsevier Inc. All rights reserved.

Communicative Signals Promote Object Recognition Memory and Modulate the Right Posterior STS.

Science.gov (United States)

Redcay, Elizabeth; Ludlum, Ruth S; Velnoskey, Kayla R; Kanwal, Simren

2016-01-01

Detection of communicative signals is thought to facilitate knowledge acquisition early in life, but less is known about the role these signals play in adult learning or about the brain systems supporting sensitivity to communicative intent. The current study examined how ostensive gaze cues and communicative actions affect adult recognition memory and modulate neural activity as measured by fMRI. For both the behavioral and fMRI experiments, participants viewed a series of videos of an actress acting on one of two objects in front of her. Communicative context in the videos was manipulated in a 2 × 2 design in which the actress either had direct gaze (Gaze) or wore a visor (NoGaze) and either pointed at (Point) or reached for (Reach) one of the objects (target) in front of her. Participants then completed a recognition memory task with old (target and nontarget) objects and novel objects. Recognition memory for target objects in the Gaze conditions was greater than NoGaze, but no effects of gesture type were seen. Similarly, the fMRI video-viewing task revealed a significant effect of Gaze within right posterior STS (pSTS), but no significant effects of Gesture. Furthermore, pSTS sensitivity to Gaze conditions was related to greater memory for objects viewed in Gaze, as compared with NoGaze, conditions. Taken together, these results demonstrate that the ostensive, communicative signal of direct gaze preceding an object-directed action enhances recognition memory for attended items and modulates the pSTS response to object-directed actions. Thus, establishment of a communicative context through ostensive signals remains an important component of learning and memory into adulthood, and the pSTS may play a role in facilitating this type of social learning.

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

Multispectral image analysis for object recognition and classification

Science.gov (United States)

Viau, C. R.; Payeur, P.; Cretu, A.-M.

2016-05-01

Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.

Experience moderates overlap between object and face recognition, suggesting a common ability.

Science.gov (United States)

Gauthier, Isabel; McGugin, Rankin W; Richler, Jennifer J; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E

2014-07-03

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. © 2014 ARVO.

Object recognition based on Google's reverse image search and image similarity

Science.gov (United States)

Horváth, András.

2015-12-01

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

Recognition of Simple 3D Geometrical Objects under Partial Occlusion

Science.gov (United States)

Barchunova, Alexandra; Sommer, Gerald

In this paper we present a novel procedure for contour-based recognition of partially occluded three-dimensional objects. In our approach we use images of real and rendered objects whose contours have been deformed by a restricted change of the viewpoint. The preparatory part consists of contour extraction, preprocessing, local structure analysis and feature extraction. The main part deals with an extended construction and functionality of the classifier ensemble Adaptive Occlusion Classifier (AOC). It relies on a hierarchical fragmenting algorithm to perform a local structure analysis which is essential when dealing with occlusions. In the experimental part of this paper we present classification results for five classes of simple geometrical figures: prism, cylinder, half cylinder, a cube, and a bridge. We compare classification results for three classical feature extractors: Fourier descriptors, pseudo Zernike and Zernike moments.

Object Recognition Memory and the Rodent Hippocampus

Science.gov (United States)

Broadbent, Nicola J.; Gaskin, Stephane; Squire, Larry R.; Clark, Robert E.

2010-01-01

In rodents, the novel object recognition task (NOR) has become a benchmark task for assessing recognition memory. Yet, despite its widespread use, a consensus has not developed about which brain structures are important for task performance. We assessed both the anterograde and retrograde effects of hippocampal lesions on performance in the NOR…

Sensor agnostic object recognition using a map seeking circuit

Science.gov (United States)

Overman, Timothy L.; Hart, Michael

2012-05-01

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

Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats

Directory of Open Access Journals (Sweden)

Federica Bianca Rosselli

2015-03-01

Full Text Available In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness. In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: i smaller and more scattered; ii only partially preserved across object views; and iii only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning.

Real object recognition using moment invariants

Indian Academy of Sciences (India)

are taken from different angles of view are the main features leading us to our objective. ... Two-dimensional moments of a digitally sampled M × M image that has gray function f (x, y), (x, .... in this paper. Information about the original colours of the objects is not used. .... multi-dimensional changes and recognition. Table 1.

Three-dimensional object recognition using similar triangles and decision trees

Science.gov (United States)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Distinct roles of basal forebrain cholinergic neurons in spatial and object recognition memory

OpenAIRE

Kana Okada; Kayo Nishizawa; Tomoko Kobayashi; Shogo Sakata; Kazuto Kobayashi

2015-01-01

Recognition memory requires processing of various types of information such as objects and locations. Impairment in recognition memory is a prominent feature of amnesia and a symptom of Alzheimer?s disease (AD). Basal forebrain cholinergic neurons contain two major groups, one localized in the medial septum (MS)/vertical diagonal band of Broca (vDB), and the other in the nucleus basalis magnocellularis (NBM). The roles of these cell groups in recognition memory have been debated, and it remai...

An ERP Study on Self-Relevant Object Recognition

Science.gov (United States)

Miyakoshi, Makoto; Nomura, Michio; Ohira, Hideki

2007-01-01

We performed an event-related potential study to investigate the self-relevance effect in object recognition. Three stimulus categories were prepared: SELF (participant's own objects), FAMILIAR (disposable and public objects, defined as objects with less-self-relevant familiarity), and UNFAMILIAR (others' objects). The participants' task was to…

Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review

Directory of Open Access Journals (Sweden)

Zhuowen Lv

2015-01-01

Full Text Available Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach.

Early age-dependent impairments of context-dependent extinction learning, object recognition, and object-place learning occur in rats.

Science.gov (United States)

Wiescholleck, Valentina; Emma André, Marion Agnès; Manahan-Vaughan, Denise

2014-03-01

The hippocampus is vulnerable to age-dependent memory decline. Multiple forms of memory depend on adequate hippocampal function. Extinction learning comprises active inhibition of no longer relevant learned information concurrent with suppression of a previously learned reaction. It is highly dependent on context, and evidence exists that it requires hippocampal activation. In this study, we addressed whether context-based extinction as well as hippocampus-dependent tasks, such as object recognition and object-place recognition, are equally affected by moderate aging. Young (7-8 week old) and older (7-8 month old) Wistar rats were used. For the extinction study, animals learned that a particular floor context indicated that they should turn into one specific arm (e.g., left) to receive a food reward. On the day after reaching the learning criterion of 80% correct choices, the floor context was changed, no reward was given and animals were expected to extinguish the learned response. Both, young and older rats managed this first extinction trial in the new context with older rats showing a faster extinction performance. One day later, animals were returned to the T-maze with the original floor context and renewal effects were assessed. In this case, only young but not older rats showed the expected renewal effect (lower extinction ratio as compared to the day before). To assess general memory abilities, animals were tested in the standard object recognition and object-place memory tasks. Evaluations were made at 5 min, 1 h and 7 day intervals. Object recognition memory was poor at short-term and intermediate time-points in older but not young rats. Object-place memory performance was unaffected at 5 min, but impaired at 1 h in older but not young rats. Both groups were impaired at 7 days. These findings support that not only aspects of general memory, but also context-dependent extinction learning, are affected by moderate aging. This may reflect less flexibility in

First-Class Object Sets

DEFF Research Database (Denmark)

Ernst, Erik

Typically, objects are monolithic entities with a fixed interface. To increase the flexibility in this area, this paper presents first-class object sets as a language construct. An object set offers an interface which is a disjoint union of the interfaces of its member objects. It may also be used...... for a special kind of method invocation involving multiple objects in a dynamic lookup process. With support for feature access and late-bound method calls object sets are similar to ordinary objects, only more flexible. The approach is made precise by means of a small calculus, and the soundness of its type...

Dopamine D1 receptor activation leads to object recognition memory in a coral reef fish.

Science.gov (United States)

Hamilton, Trevor J; Tresguerres, Martin; Kline, David I

2017-07-01

Object recognition memory is the ability to identify previously seen objects and is an adaptive mechanism that increases survival for many species throughout the animal kingdom. Previously believed to be possessed by only the highest order mammals, it is now becoming clear that fish are also capable of this type of memory formation. Similar to the mammalian hippocampus, the dorsolateral pallium regulates distinct memory processes and is modulated by neurotransmitters such as dopamine. Caribbean bicolour damselfish ( Stegastes partitus ) live in complex environments dominated by coral reef structures and thus likely possess many types of complex memory abilities including object recognition. This study used a novel object recognition test in which fish were first presented two identical objects, then after a retention interval of 10 min with no objects, the fish were presented with a novel object and one of the objects they had previously encountered in the first trial. We demonstrate that the dopamine D 1 -receptor agonist (SKF 38393) induces the formation of object recognition memories in these fish. Thus, our results suggest that dopamine-receptor mediated enhancement of spatial memory formation in fish represents an evolutionarily conserved mechanism in vertebrates. © 2017 The Author(s).

On the relation between face and object recognition in developmental prosopagnosia

DEFF Research Database (Denmark)

Gerlach, Christian; Klargaard, Solja K.; Starrfelt, Randi

2016-01-01

There is an ongoing debate about whether face recognition and object recognition constitute separate domains. Clarification of this issue can have important theoretical implications as face recognition is often used as a prime example of domain-specificity in mind and brain. An important source...... of input to this debate comes from studies of individuals with developmental prosopagnosia, suggesting that face recognition can be selectively impaired. We put the selectivity hypothesis to test by assessing the performance of 10 individuals with developmental prosopagnosia on demanding tests of visual...... object processing involving both regular and degraded drawings. None of the individuals exhibited a clear dissociation between face and object recognition, and as a group they were significantly more affected by degradation of objects than control participants. Importantly, we also find positive...

Exploring objects for recognition in the real world

NARCIS (Netherlands)

Kootstra, Gert; Ypma, Jelmer; de Boer, Bart

2007-01-01

Perception in natural systems is a highly active process. In this paper, we adopt the strategy of natural systems to explore objects for 3D object recognition using robots. The exploration of objects enables the system to learn objects from different viewpoints, which is essential for 3D object

Object recognition - Convergence of vision, audition, and touch

DEFF Research Database (Denmark)

Kassuba, Tanja

of object information across audition and touch or across all thee senses. Further, even though object recognition within different senses is to some degree redundant, the different senses differ with respect to their intrinsic efficiency in extracting types of information (Lederman & Klatzky, 2009...... magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and repetitive transcranial magnetic stimulation (rTMS). The following research questions were addressed: 1. Where in the human brain does object recognition converge across vision, audition, and touch? 2. How is audio-haptic object......-match-to-sample task was applied in which participants had to match a target object with a previously presented sample object within and across audition and touch in both directions (auditory─haptic and haptic─auditory). As a coherence in content is an important binding cue (Laurienti et al., 2004), semantic...

Comparison of Object Recognition Behavior in Human and Monkey

Science.gov (United States)

Rajalingham, Rishi; Schmidt, Kailyn

2015-01-01

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to

PROBABILISTIC APPROACH TO OBJECT DETECTION AND RECOGNITION FOR VIDEOSTREAM PROCESSING

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Volodymyr Kharchenko

2017-07-01

Full Text Available Purpose: The represented research results are aimed to improve theoretical basics of computer vision and artificial intelligence of dynamical system. Proposed approach of object detection and recognition is based on probabilistic fundamentals to ensure the required level of correct object recognition. Methods: Presented approach is grounded at probabilistic methods, statistical methods of probability density estimation and computer-based simulation at verification stage of development. Results: Proposed approach for object detection and recognition for video stream data processing has shown several advantages in comparison with existing methods due to its simple realization and small time of data processing. Presented results of experimental verification look plausible for object detection and recognition in video stream. Discussion: The approach can be implemented in dynamical system within changeable environment such as remotely piloted aircraft systems and can be a part of artificial intelligence in navigation and control systems.

Perceptual Plasticity for Auditory Object Recognition

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Heald, Shannon L. M.; Van Hedger, Stephen C.; Nusbaum, Howard C.

2017-01-01

In our auditory environment, we rarely experience the exact acoustic waveform twice. This is especially true for communicative signals that have meaning for listeners. In speech and music, the acoustic signal changes as a function of the talker (or instrument), speaking (or playing) rate, and room acoustics, to name a few factors. Yet, despite this acoustic variability, we are able to recognize a sentence or melody as the same across various kinds of acoustic inputs and determine meaning based on listening goals, expectations, context, and experience. The recognition process relates acoustic signals to prior experience despite variability in signal-relevant and signal-irrelevant acoustic properties, some of which could be considered as “noise” in service of a recognition goal. However, some acoustic variability, if systematic, is lawful and can be exploited by listeners to aid in recognition. Perceivable changes in systematic variability can herald a need for listeners to reorganize perception and reorient their attention to more immediately signal-relevant cues. This view is not incorporated currently in many extant theories of auditory perception, which traditionally reduce psychological or neural representations of perceptual objects and the processes that act on them to static entities. While this reduction is likely done for the sake of empirical tractability, such a reduction may seriously distort the perceptual process to be modeled. We argue that perceptual representations, as well as the processes underlying perception, are dynamically determined by an interaction between the uncertainty of the auditory signal and constraints of context. This suggests that the process of auditory recognition is highly context-dependent in that the identity of a given auditory object may be intrinsically tied to its preceding context. To argue for the flexible neural and psychological updating of sound-to-meaning mappings across speech and music, we draw upon examples

Partially Supervised Approach in Signal Recognition

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Catalina COCIANU

2009-01-01

Full Text Available The paper focuses on the potential of principal directions based approaches in signal classification and recognition. In probabilistic models, the classes are represented in terms of multivariate density functions, and an object coming from a certain class is modeled as a random vector whose repartition has the density function corresponding to this class. In cases when there is no statistical information concerning the set of density functions corresponding to the classes involved in the recognition process, usually estimates based on the information extracted from available data are used instead. In the proposed methodology, the characteristics of a class are given by a set of eigen vectors of the sample covariance matrix. The overall dissimilarity of an object X with a given class C is computed as the disturbance of the structure of C, when X is allotted to C. A series of tests concerning the behavior of the proposed recognition algorithm are reported in the final section of the paper.

Exploiting core knowledge for visual object recognition.

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Schurgin, Mark W; Flombaum, Jonathan I

2017-03-01

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

Training facilitates object recognition in cubist paintings

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Martin Wiesmann

2010-03-01

Full Text Available To the naïve observer, cubist paintings contain geometrical forms in which familiar objects are hardly recognizable, even in the presence of a meaningful title. We used fMRI to test whether a short training session about Cubism would facilitate object recognition in paintings by Picasso, Braque and Gris. Subjects, who had no formal art education, were presented with titled or untitled cubist paintings and scrambled images, and performed object recognition tasks. Relative to the control group, trained subjects recognized more objects in the paintings, their response latencies were significantly shorter, and they showed enhanced activation in the parahippocampal cortex, with a parametric increase in the amplitude of the fMRI signal as a function of the number of recognized objects. Moreover, trained subjects were slower to report not recognizing any familiar objects in the paintings and these longer response latencies were correlated with activation in a fronto-parietal network. These findings suggest that trained subjects adopted a visual search strategy and used contextual associations to perform the tasks. Our study supports the proactive brain framework, according to which the brain uses associations to generate predictions.

It takes two-skilled recognition of objects engages lateral areas in both hemispheres.

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Merim Bilalić

Full Text Available Our object recognition abilities, a direct product of our experience with objects, are fine-tuned to perfection. Left temporal and lateral areas along the dorsal, action related stream, as well as left infero-temporal areas along the ventral, object related stream are engaged in object recognition. Here we show that expertise modulates the activity of dorsal areas in the recognition of man-made objects with clearly specified functions. Expert chess players were faster than chess novices in identifying chess objects and their functional relations. Experts' advantage was domain-specific as there were no differences between groups in a control task featuring geometrical shapes. The pattern of eye movements supported the notion that experts' extensive knowledge about domain objects and their functions enabled superior recognition even when experts were not directly fixating the objects of interest. Functional magnetic resonance imaging (fMRI related exclusively the areas along the dorsal stream to chess specific object recognition. Besides the commonly involved left temporal and parietal lateral brain areas, we found that only in experts homologous areas on the right hemisphere were also engaged in chess specific object recognition. Based on these results, we discuss whether skilled object recognition does not only involve a more efficient version of the processes found in non-skilled recognition, but also qualitatively different cognitive processes which engage additional brain areas.

Hippocampal NMDA receptors are involved in rats' spontaneous object recognition only under high memory load condition.

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Sugita, Manami; Yamada, Kazuo; Iguchi, Natsumi; Ichitani, Yukio

2015-10-22

The possible involvement of hippocampal N-methyl-D-aspartate (NMDA) receptors in spontaneous object recognition was investigated in rats under different memory load conditions. We first estimated rats' object memory span using 3-5 objects in "Different Objects Task (DOT)" in order to confirm the highest memory load condition in object recognition memory. Rats were allowed to explore a field in which 3 (3-DOT), 4 (4-DOT), or 5 (5-DOT) different objects were presented. After a delay period, they were placed again in the same field in which one of the sample objects was replaced by another object, and their object exploration behavior was analyzed. Rats could differentiate the novel object from the familiar ones in 3-DOT and 4-DOT but not in 5-DOT, suggesting that rats' object memory span was about 4. Then, we examined the effects of hippocampal AP5 infusion on performance in both 2-DOT (2 different objects were used) and 4-DOT. The drug treatment before the sample phase impaired performance only in 4-DOT. These results suggest that hippocampal NMDA receptors play a critical role in spontaneous object recognition only when the memory load is high. Copyright © 2015 Elsevier B.V. All rights reserved.

Involvement of hippocampal NMDA receptors in retrieval of spontaneous object recognition memory in rats.

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Iwamura, Etsushi; Yamada, Kazuo; Ichitani, Yukio

2016-07-01

The involvement of hippocampal N-methyl-d-aspartate (NMDA) receptors in the retrieval process of spontaneous object recognition memory was investigated. The spontaneous object recognition test consisted of three phases. In the sample phase, rats were exposed to two identical objects several (2-5) times in the arena. After the sample phase, various lengths of delay intervals (24h-6 weeks) were inserted (delay phase). In the test phase in which both the familiar and the novel objects were placed in the arena, rats' novel object exploration behavior under the hippocampal treatment of NMDA receptor antagonist, AP5, or vehicle was observed. With 5 exposure sessions in the sample phase (experiment 1), AP5 treatment in the test phase significantly decreased discrimination ratio when the delay was 3 weeks but not when it was one week. On the other hand, with 2 exposure sessions in the sample phase (experiment 2) in which even vehicle-injected control animals could not discriminate the novel object from the familiar one with a 3 week delay, AP5 treatment significantly decreased discrimination ratio when the delay was one week, but not when it was 24h. Additional experiment (experiment 3) showed that the hippocampal treatment of an α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor antagonist, NBQX, decreased discrimination ratio with all delay intervals tested (24h-3 weeks). Results suggest that hippocampal NMDA receptors play an important role in the retrieval of spontaneous object recognition memory especially when the memory trace weakens. Copyright © 2016. Published by Elsevier B.V.

Perceptual differentiation and category effects in normal object recognition

DEFF Research Database (Denmark)

Gerlach, Christian; Law, I; Gade, A

1999-01-01

The purpose of the present PET study was (i) to investigate the neural correlates of object recognition, i.e. the matching of visual forms to memory, and (ii) to test the hypothesis that this process is more difficult for natural objects than for artefacts. This was done by using object decision...... tasks where subjects decided whether pictures represented real objects or non-objects. The object decision tasks differed in their difficulty (the degree of perceptual differentiation needed to perform them) and in the category of the real objects used (natural objects versus artefacts). A clear effect...... be the neural correlate of matching visual forms to memory, and the amount of activation in these regions may correspond to the degree of perceptual differentiation required for recognition to occur. With respect to behaviour, it took significantly longer to make object decisions on natural objects than...

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.

A Patient with Difficulty of Object Recognition: Semantic Amnesia for Manipulable Objects

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A. Yamadori

1992-01-01

Full Text Available We studied a patient who had recognition difficulty for manipulable objects. MRI showed a lesion in the left occipito-parietotemporal area. Differential diagnosis of agnosia, aphasia and apraxia is discussed. We believe this “object meaning amnesia” constitutes a distinct subtype of semantic amnesia.

Leveraging Cognitive Context for Object Recognition

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2014-06-01

established, links have an associated strength value which affects how much activation is passed along the link from chunk j to chunk i. Link strengths ... strength is updated iteratively whenever the model thinks about chunks i context suggests that an apple is most likely to be seen next (since it primes...1998. 1, 4 [3] M. E. Auckland , K. R. Cave, and N. Donnelly. Non- target objects can influence perceptual processes dur- ing object recognition

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.

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

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Lourenco Tomas

2011-08-01

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

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 Recognition System in Remote Controlled Weapon Station using SIFT and SURF Methods

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Midriem Mirdanies

2013-12-01

Full Text Available Object recognition system using computer vision that is implemented on Remote Controlled Weapon Station (RCWS is discussed. This system will make it easier to identify and shoot targeted object automatically. Algorithm was created to recognize real time multiple objects using two methods i.e. Scale Invariant Feature Transform (SIFT and Speeded Up Robust Features (SURF combined with K-Nearest Neighbors (KNN and Random Sample Consensus (RANSAC for verification. The algorithm is designed to improve object detection to be more robust and to minimize the processing time required. Objects are registered on the system consisting of the armored personnel carrier, tanks, bus, sedan, big foot, and police jeep. In addition, object selection can use mouse to shoot another object that has not been registered on the system. Kinect™ is used to capture RGB images and to find the coordinates x, y, and z of the object. The programming language used is C with visual studio IDE 2010 and opencv libraries. Object recognition program is divided into three parts: 1 reading image from kinect™ and simulation results, 2 object recognition process, and 3 transfer of the object data to the ballistic computer. Communication between programs is performed using shared memory. The detected object data is sent to the ballistic computer via Local Area Network (LAN using winsock for ballistic calculation, and then the motor control system moves the direction of the weapon model to the desired object. The experimental results show that the SIFT method is more suitable because more accurate and faster than SURF with the average processing time to detect one object is 430.2 ms, two object is 618.4 ms, three objects is 682.4 ms, and four objects is 756.2 ms. Object recognition program is able to recognize multi-objects and the data of the identified object can be processed by the ballistic computer in realtime.

Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

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I. Topalova

2005-03-01

Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

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I. Topalova

2008-11-01

Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

Effects of Acute Administration of Urtica dioica on the Novel Object-Recognition Task in Mice

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Hashemi-Firouzi

2015-08-01

Full Text Available Background Urtica dioica (nettle has a variety of uses in traditional medicine for the treatment of certain urogenital problems, gastrointestinal disorders, and diabetes. Objectives Recent studies have implicated the effect of U. dioica on brain functions such as pain and memory. However, there is no direct evidence of the acute effects of this plant on cognition. The aim of the present study was to evaluate the effect of U. dioica aqueous extract on the novel object-recognition task (NOR in mice. Materials and Methods First, U. dioica aqueous extract was prepared, then adult male mice were randomly divided into four experimental groups. During the training session, the mice were placed in a box and given 5 minutes to explore two identical objects. The next day, they were again placed in the box and allowed to explore one familiar and one novel object. They received intraperitoneal injections of saline or U. dioica aqueous extract (100 mg/kg before or immediately after the training session or before the test session of the NOR task. Results The results showed that there was a preference for the novel object compared to the familiar one in each of the experimental groups. The object-recognition discrimination index in the group of mice that received U. dioica before training was significantly less than in the other experimental groups. There was no significant difference in the discrimination index between the other groups. U. dioica did not decrease the time spent exploring familiar and unfamiliar objects, or the total time spent exploring both objects. Conclusions Acute administration of U. dioica impairs the object-recognition task if it is used only before the training session. This may be due to its modulation on the acquisition processing of object-recognition. U. dioica has no significant effects on the consolidation or retrieval processing stages of the NOR task. These results emphasize the unfavorable effect on cognitive function of pre

Representations and Techniques for 3D Object Recognition and Scene Interpretation

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Hoiem, Derek

2011-01-01

One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physi

Object and event recognition for stroke rehabilitation

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Ghali, Ahmed; Cunningham, Andrew S.; Pridmore, Tony P.

2003-06-01

Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and machine vision technologies to produce innovative stroke rehabilitation methods. We describe a combined object recognition and event detection system that provides real time feedback to stroke patients performing everyday kitchen tasks necessary for independent living, e.g. making a cup of coffee. The image plane position of each object, including the patient"s hand, is monitored using histogram-based recognition methods. The relative positions of hand and objects are then reported to a task monitor that compares the patient"s actions against a model of the target task. A prototype system has been constructed and is currently undergoing technical and clinical evaluation.

The role of nitric oxide in the object recognition memory.

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Pitsikas, Nikolaos

2015-05-15

The novel object recognition task (NORT) assesses recognition memory in animals. It is a non-rewarded paradigm that it is based on spontaneous exploratory behavior in rodents. This procedure is widely used for testing the effects of compounds on recognition memory. Recognition memory is a type of memory severely compromised in schizophrenic and Alzheimer's disease patients. Nitric oxide (NO) is sought to be an intra- and inter-cellular messenger in the central nervous system and its implication in learning and memory is well documented. Here I intended to critically review the role of NO-related compounds on different aspects of recognition memory. Current analysis shows that both NO donors and NO synthase (NOS) inhibitors are involved in object recognition memory and suggests that NO might be a promising target for cognition impairments. However, the potential neurotoxicity of NO would add a note of caution in this context. Copyright © 2014 Elsevier B.V. All rights reserved.

Single prolonged stress impairs social and object novelty recognition in rats.

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Eagle, Andrew L; Fitzpatrick, Chris J; Perrine, Shane A

2013-11-01

Posttraumatic stress disorder (PTSD) results from exposure to a traumatic event and manifests as re-experiencing, arousal, avoidance, and negative cognition/mood symptoms. Avoidant symptoms, as well as the newly defined negative cognitions/mood, are a serious complication leading to diminished interest in once important or positive activities, such as social interaction; however, the basis of these symptoms remains poorly understood. PTSD patients also exhibit impaired object and social recognition, which may underlie the avoidance and symptoms of negative cognition, such as social estrangement or diminished interest in activities. Previous studies have demonstrated that single prolonged stress (SPS), models PTSD phenotypes, including impairments in learning and memory. Therefore, it was hypothesized that SPS would impair social and object recognition memory. Male Sprague Dawley rats were exposed to SPS then tested in the social choice test (SCT) or novel object recognition test (NOR). These tests measure recognition of novelty over familiarity, a natural preference of rodents. Results show that SPS impaired preference for both social and object novelty. In addition, SPS impairment in social recognition may be caused by impaired behavioral flexibility, or an inability to shift behavior during the SCT. These results demonstrate that traumatic stress can impair social and object recognition memory, which may underlie certain avoidant symptoms or negative cognition in PTSD and be related to impaired behavioral flexibility. Copyright © 2013 Elsevier B.V. All rights reserved.

Electrophysiological evidence for effects of color knowledge in object recognition.

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Lu, Aitao; Xu, Guiping; Jin, Hua; Mo, Lei; Zhang, Jijia; Zhang, John X

2010-01-29

Knowledge about the typical colors associated with familiar everyday objects (i.e., strawberries are red) is well-known to be represented in the conceptual semantic system. Evidence that such knowledge may also play a role in early perceptual processes for object recognition is scant. In the present ERP study, participants viewed a list of object pictures and detected infrequent stimulus repetitions. Results show that shortly after stimulus onset, ERP components indexing early perceptual processes, including N1, P2, and N2, differentiated between objects in their appropriate or congruent color from these objects in an inappropriate or incongruent color. Such congruence effect also occurred in N3 associated with semantic processing of pictures but not in N4 for domain-general semantic processing. Our results demonstrate a clear effect of color knowledge in early object recognition stages and support the following proposal-color as a surface property is stored in a multiple-memory system where pre-semantic perceptual and semantic conceptual representations interact during object recognition. (c) 2009 Elsevier Ireland Ltd. All rights reserved.

First-Class Object Sets

DEFF Research Database (Denmark)

Ernst, Erik

2009-01-01

Typically, an object is a monolithic entity with a fixed interface.  To increase flexibility in this area, this paper presents first-class object sets as a language construct.  An object set offers an interface which is a disjoint union of the interfaces of its member objects.  It may also be use...... to a mainstream virtual machine in order to improve on the support for family polymorphism.  The approach is made precise by means of a small calculus, and the soundness of its type system has been shown by a mechanically checked proof in Coq....

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

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

2017-01-01

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

Improving package structure of object-oriented software using multi-objective optimization and weighted class connections

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Amarjeet

2017-07-01

Full Text Available The software maintenance activities performed without following the original design decisions about the package structure usually deteriorate the quality of software modularization, leading to decay of the quality of the system. One of the main reasons for such structural deterioration is inappropriate grouping of source code classes in software packages. To improve such grouping/modular-structure, previous researchers formulated the software remodularization problem as an optimization problem and solved it using search-based meta-heuristic techniques. These optimization approaches aimed at improving the quality metrics values of the structure without considering the original package design decisions, often resulting into a totally new software modularization. The entirely changed software modularization becomes costly to realize as well as difficult to understand for the developers/maintainers. To alleviate this issue, we propose a multi-objective optimization approach to improve the modularization quality of an object-oriented system with minimum possible movement of classes between existing packages of original software modularization. The optimization is performed using NSGA-II, a widely-accepted multi-objective evolutionary algorithm. In order to ensure minimum modification of original package structure, a new approach of computing class relations using weighted strengths has been proposed here. The weights of relations among different classes are computed on the basis of the original package structure. A new objective function has been formulated using these weighted class relations. This objective function drives the optimization process toward better modularization quality simultaneously ensuring preservation of original structure. To evaluate the results of the proposed approach, a series of experiments are conducted over four real-worlds and two random software applications. The experimental results clearly indicate the effectiveness

Deformation-specific and deformation-invariant visual object recognition: pose vs identity recognition of people and deforming objects

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Tristan J Webb

2014-04-01

Full Text Available When we see a human sitting down, standing up, or walking, we can recognise one of these poses independently of the individual, or we can recognise the individual person, independently of the pose. The same issues arise for deforming objects. For example, if we see a flag deformed by the wind, either blowing out or hanging languidly, we can usually recognise the flag, independently of its deformation; or we can recognise the deformation independently of the identity of the flag. We hypothesize that these types of recognition can be implemented by the primate visual system using temporo-spatial continuity as objects transform as a learning principle. In particular, we hypothesize that pose or deformation can be learned under conditions in which large numbers of different people are successively seen in the same pose, or objects in the same deformation. We also hypothesize that person-specific representations that are independent of pose, and object-specific representations that are independent of deformation and view, could be built, when individual people or objects are observed successively transforming from one pose or deformation and view to another. These hypotheses were tested in a simulation of the ventral visual system, VisNet, that uses temporal continuity, implemented in a synaptic learning rule with a short-term memory trace of previous neuronal activity, to learn invariant representations. It was found that depending on the statistics of the visual input, either pose-specific or deformation-specific representations could be built that were invariant with respect to individual and view; or that identity-specific representations could be built that were invariant with respect to pose or deformation and view. We propose that this is how pose-specific and pose-invariant, and deformation-specific and deformation-invariant, perceptual representations are built in the brain.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

Science.gov (United States)

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Science.gov (United States)

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Directory of Open Access Journals (Sweden)

Xin Tang

Full Text Available Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC. Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

Science.gov (United States)

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

Virtual classes: a powerful mechanism in object-oriented programming

DEFF Research Database (Denmark)

Madsen, Ole Lehrmann; Møller-Pedersen, Birger

1989-01-01

The notions of class, subclass and virtual procedure are fairly well understood and recognized as some of the key concepts in object-oriented programming. The possibility of modifying a virtual procedure in a subclass is a powerful technique for specializing the general properties of the superclass....... In most object-oriented languages, the attributes of an object may be references to objects and (virtual) procedures. In Simula and BETA it is also possible to have class attributes. The power of class attributes has not yet been widely recognized. In BETA a class may also have virtual class attributes...

Category-specificity in visual object recognition

DEFF Research Database (Denmark)

Gerlach, Christian

2009-01-01

Are all categories of objects recognized in the same manner visually? Evidence from neuropsychology suggests they are not: some brain damaged patients are more impaired in recognizing natural objects than artefacts whereas others show the opposite impairment. Category-effects have also been...... demonstrated in neurologically intact subjects, but the findings are contradictory and there is no agreement as to why category-effects arise. This article presents a Pre-semantic Account of Category Effects (PACE) in visual object recognition. PACE assumes two processing stages: shape configuration (the...... binding of shape elements into elaborate shape descriptions) and selection (among competing representations in visual long-term memory), which are held to be differentially affected by the structural similarity between objects. Drawing on evidence from clinical studies, experimental studies...

Experimental acquisition of long-range portraits of objects and their recognition

International Nuclear Information System (INIS)

Buryi, E V; Kosykh, A E

1998-01-01

An experimental investigation was made of recognition of the perspectives of model objects on the basis of the shape of the envelope of a scattered laser pulse. Stable recognition of various perspectives of an object was found to be possible even for high ratios of the probe pulse duration to the time of its propagation along the object surface. (laser applications and other topics in quantum electronics)

Superior voice recognition in a patient with acquired prosopagnosia and object agnosia.

Science.gov (United States)

Hoover, Adria E N; Démonet, Jean-François; Steeves, Jennifer K E

2010-11-01

Anecdotally, it has been reported that individuals with acquired prosopagnosia compensate for their inability to recognize faces by using other person identity cues such as hair, gait or the voice. Are they therefore superior at the use of non-face cues, specifically voices, to person identity? Here, we empirically measure person and object identity recognition in a patient with acquired prosopagnosia and object agnosia. We quantify person identity (face and voice) and object identity (car and horn) recognition for visual, auditory, and bimodal (visual and auditory) stimuli. The patient is unable to recognize faces or cars, consistent with his prosopagnosia and object agnosia, respectively. He is perfectly able to recognize people's voices and car horns and bimodal stimuli. These data show a reverse shift in the typical weighting of visual over auditory information for audiovisual stimuli in a compromised visual recognition system. Moreover, the patient shows selectively superior voice recognition compared to the controls revealing that two different stimulus domains, persons and objects, may not be equally affected by sensory adaptation effects. This also implies that person and object identity recognition are processed in separate pathways. These data demonstrate that an individual with acquired prosopagnosia and object agnosia can compensate for the visual impairment and become quite skilled at using spared aspects of sensory processing. In the case of acquired prosopagnosia it is advantageous to develop a superior use of voices for person identity recognition in everyday life. Copyright © 2010 Elsevier Ltd. All rights reserved.

Sub-OBB based object recognition and localization algorithm using range images

International Nuclear Information System (INIS)

Hoang, Dinh-Cuong; Chen, Liang-Chia; Nguyen, Thanh-Hung

2017-01-01

This paper presents a novel approach to recognize and estimate pose of the 3D objects in cluttered range images. The key technical breakthrough of the developed approach can enable robust object recognition and localization under undesirable condition such as environmental illumination variation as well as optical occlusion to viewing the object partially. First, the acquired point clouds are segmented into individual object point clouds based on the developed 3D object segmentation for randomly stacked objects. Second, an efficient shape-matching algorithm called Sub-OBB based object recognition by using the proposed oriented bounding box (OBB) regional area-based descriptor is performed to reliably recognize the object. Then, the 3D position and orientation of the object can be roughly estimated by aligning the OBB of segmented object point cloud with OBB of matched point cloud in a database generated from CAD model and 3D virtual camera. To detect accurate pose of the object, the iterative closest point (ICP) algorithm is used to match the object model with the segmented point clouds. From the feasibility test of several scenarios, the developed approach is verified to be feasible for object pose recognition and localization. (paper)

On the relation between face and object recognition in developmental prosopagnosia

DEFF Research Database (Denmark)

Gerlach, Christian; Klargaard, Solja; Starrfelt, Randi

2016-01-01

There is an ongoing debate about whether face recognition and object recognition constitute separate cognitive domains. Clarification of this issue can have important theoretical consequences as face recognition is often used as a prime example of domain-specificity in mind and brain. An importan...

Central administration of angiotensin IV rapidly enhances novel object recognition among mice.

Science.gov (United States)

Paris, Jason J; Eans, Shainnel O; Mizrachi, Elisa; Reilley, Kate J; Ganno, Michelle L; McLaughlin, Jay P

2013-07-01

Angiotensin IV (Val(1)-Tyr(2)-Ile(3)-His(4)-Pro(5)-Phe(6)) has demonstrated potential cognitive-enhancing effects. The present investigation assessed and characterized: (1) dose-dependency of angiotensin IV's cognitive enhancement in a C57BL/6J mouse model of novel object recognition, (2) the time-course for these effects, (3) the identity of residues in the hexapeptide important to these effects and (4) the necessity of actions at angiotensin IV receptors for procognitive activity. Assessment of C57BL/6J mice in a novel object recognition task demonstrated that prior administration of angiotensin IV (0.1, 1.0, or 10.0, but not 0.01 nmol, i.c.v.) significantly enhanced novel object recognition in a dose-dependent manner. These effects were time dependent, with improved novel object recognition observed when angiotensin IV (0.1 nmol, i.c.v.) was administered 10 or 20, but not 30 min prior to the onset of the novel object recognition testing. An alanine scan of the angiotensin IV peptide revealed that replacement of the Val(1), Ile(3), His(4), or Phe(6) residues with Ala attenuated peptide-induced improvements in novel object recognition, whereas Tyr(2) or Pro(5) replacement did not significantly affect performance. Administration of the angiotensin IV receptor antagonist, divalinal-Ang IV (20 nmol, i.c.v.), reduced (but did not abolish) novel object recognition; however, this antagonist completely blocked the procognitive effects of angiotensin IV (0.1 nmol, i.c.v.) in this task. Rotorod testing demonstrated no locomotor effects with any angiotensin IV or divalinal-Ang IV dose tested. These data demonstrate that angiotensin IV produces a rapid enhancement of associative learning and memory performance in a mouse model that was dependent on the angiotensin IV receptor. Copyright © 2013 Elsevier Ltd. All rights reserved.

The relationship between protein synthesis and protein degradation in object recognition memory.

Science.gov (United States)

Furini, Cristiane R G; Myskiw, Jociane de C; Schmidt, Bianca E; Zinn, Carolina G; Peixoto, Patricia B; Pereira, Luiza D; Izquierdo, Ivan

2015-11-01

For decades there has been a consensus that de novo protein synthesis is necessary for long-term memory. A second round of protein synthesis has been described for both extinction and reconsolidation following an unreinforced test session. Recently, it was shown that consolidation and reconsolidation depend not only on protein synthesis but also on protein degradation by the ubiquitin-proteasome system (UPS), a major mechanism responsible for protein turnover. However, the involvement of UPS on consolidation and reconsolidation of object recognition memory remains unknown. Here we investigate in the CA1 region of the dorsal hippocampus the involvement of UPS-mediated protein degradation in consolidation and reconsolidation of object recognition memory. Animals with infusion cannulae stereotaxically implanted in the CA1 region of the dorsal hippocampus, were exposed to an object recognition task. The UPS inhibitor β-Lactacystin did not affect the consolidation and the reconsolidation of object recognition memory at doses known to affect other forms of memory (inhibitory avoidance, spatial learning in a water maze) while the protein synthesis inhibitor anisomycin impaired the consolidation and the reconsolidation of the object recognition memory. However, β-Lactacystin was able to reverse the impairment caused by anisomycin on the reconsolidation process in the CA1 region of the hippocampus. Therefore, it is possible to postulate a direct link between protein degradation and protein synthesis during the reconsolidation of the object recognition memory. Copyright © 2015 Elsevier B.V. All rights reserved.

The Functional Architecture of Visual Object Recognition

Science.gov (United States)

1991-07-01

different forms of agnosia can provide clues to the representations underlying normal object recognition (Farah, 1990). For example, the pair-wise...patterns of deficit and sparing occur. In a review of 99 published cases of agnosia , the observed patterns of co- occurrence implicated two underlying

Neurocomputational bases of object and face recognition.

OpenAIRE

Biederman, I; Kalocsai, P

1997-01-01

A number of behavioural phenomena distinguish the recognition of faces and objects, even when members of a set of objects are highly similar. Because faces have the same parts in approximately the same relations, individuation of faces typically requires specification of the metric variation in a holistic and integral representation of the facial surface. The direct mapping of a hypercolumn-like pattern of activation onto a representation layer that preserves relative spatial filter values in...

Motor cortical processing is causally involved in object recognition.

Science.gov (United States)

Decloe, Rebecca; Obhi, Sukhvinder S

2013-12-14

Motor activity during vicarious experience of actions is a widely reported and studied phenomenon, and motor system activity also accompanies observation of graspable objects in the absence of any actions. Such motor activity is thought to reflect simulation of the observed action, or preparation to interact with the object, respectively. Here, in an initial exploratory study, we ask whether motor activity during observation of object directed actions is involved in processes related to recognition of the object after initial exposure. Single pulse Transcranial Magnetic Stimulation (TMS) was applied over the thumb representation of the motor cortex, or over the vertex, during observation of a model thumb typing on a cell-phone, and performance on a phone recognition task at the end of the trial was assessed. Disrupting motor processing over the thumb representation 100 ms after the onset of the typing video impaired the ability to recognize the phone in the recognition test, whereas there was no such effect for TMS applied over the vertex and no TMS trials. Furthermore, this effect only manifested for videos observed from the first person perspective. In an additional control condition, there was no evidence for any effects of TMS to the thumb representation or vertex when observing and recognizing non-action related shape stimuli. Overall, these data provide evidence that motor cortical processing during observation of object-directed actions from a first person perspective is causally linked to the formation of enduring representations of objects-of-action.

Properties of mathematical objects (Goedel on classes, properties and concepts)

International Nuclear Information System (INIS)

Materna, Pavel

2007-01-01

In terms of a sufficiently fine-grained theory we should distinguish between classes, properties and concepts. Since properties are best modeled as a kind of non-trivial intensions while mathematical objects are never non-trivial intensions we should not speak about properties of mathematical objects. When we do use the term property in mathematics (as Goedel did) we either mean classes, or the more fine-grained entities to be called concepts. In the latter case concepts have to be defined so that various distinct concepts could identify one and the same object. The notion of construction in transparent intensional logic makes it possible to construe concepts as abstract procedures. At the same time we have to distinguish between this notion and the notion of construction in constructivist systems: the former - unlike the latter - are objective and, therefore, acceptable for a realist

Spontaneous object recognition: a promising approach to the comparative study of memory

Directory of Open Access Journals (Sweden)

Rachel eBlaser

2015-07-01

Full Text Available Spontaneous recognition of a novel object is a popular measure of exploratory behavior, perception and recognition memory in rodent models. Because of its relative simplicity and speed of testing, the variety of stimuli that can be used, and its ecological validity across species, it is also an attractive task for comparative research. To date, variants of this test have been used with vertebrate and invertebrate species, but the methods have seldom been sufficiently standardized to allow cross-species comparison. Here, we review the methods necessary for the study of novel object recognition in mammalian and non-mammalian models, as well as the results of these experiments. Critical to the use of this test is an understanding of the organism’s initial response to a novel object, the modulation of exploration by context, and species differences in object perception and exploratory behaviors. We argue that with appropriate consideration of species differences in perception, object affordances, and natural exploratory behaviors, the spontaneous object recognition test can be a valid and versatile tool for translational research with non-mammalian models.

Object Recognition In HADOOP Using HIPI

Directory of Open Access Journals (Sweden)

Ankit Kumar Agrawal

2015-07-01

Full Text Available Abstract The amount of images and videos being shared by the user is exponentially increasing but applications that perform video analytics is severely lacking or work on limited set of data. It is also challenging to perform analytics with less time complexity. Object recognition is the primary step in video analytics. We implement a robust method to extract objects from the data which is in unstructured format and cannot be processed directly by relational databases. In this study we present our report with results after performance evaluation and compare them with results of MATLAB.

Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas

2018-01-01

Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...

Higher-order neural network software for distortion invariant object recognition

Science.gov (United States)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Global precedence effects account for individual differences in both face and object recognition performance

DEFF Research Database (Denmark)

Gerlach, Christian; Starrfelt, Randi

2018-01-01

examine whether global precedence effects, measured by means of non-face stimuli in Navon's paradigm, can also account for individual differences in face recognition and, if so, whether the effect is of similar magnitude for faces and objects. We find evidence that global precedence effects facilitate...... both face and object recognition, and to a similar extent. Our results suggest that both face and object recognition are characterized by a coarse-to-fine temporal dynamic, where global shape information is derived prior to local shape information, and that the efficiency of face and object recognition...

Implementation of CT and IHT Processors for Invariant Object Recognition System

Directory of Open Access Journals (Sweden)

J. Turan jr.

2004-12-01

Full Text Available This paper presents PDL or ASIC implementation of key modules ofinvariant object recognition system based on the combination of theIncremental Hough transform (IHT, correlation and rapid transform(RT. The invariant object recognition system was represented partiallyin C++ language for general-purpose processor on personal computer andpartially described in VHDL code for implementation in PLD or ASIC.

Probabilistic object and viewpoint models for active object recognition

CSIR Research Space (South Africa)

Govender, N

2013-09-01

Full Text Available ,θ′(f occ). V. EXPERIMENTS A. Dataset For our experiments, we use the active recognition dataset introduced by [12]. The training data consists of everyday objects such as cereal boxes, ornaments, spice bottle, etc. Images were captured every 20 degrees... are to be verified TABLE I CONFUSION MATRIX FOR BINARY A MODEL Obscured Obscured Obscured Obscured Obscured Obscured Obscured Obscured Obscured Obscured Cereal Battery Curry box Elephant Handbag MrMin Salad Bottle Spice Bottle Spray Can Spray Can 1 Cereal 0.9800 0...

Post-Training Reversible Inactivation of the Hippocampus Enhances Novel Object Recognition Memory

Science.gov (United States)

Oliveira, Ana M. M.; Hawk, Joshua D.; Abel, Ted; Havekes, Robbert

2010-01-01

Research on the role of the hippocampus in object recognition memory has produced conflicting results. Previous studies have used permanent hippocampal lesions to assess the requirement for the hippocampus in the object recognition task. However, permanent hippocampal lesions may impact performance through effects on processes besides memory…

Motor cortical processing is causally involved in object recognition

Science.gov (United States)

2013-01-01

Background Motor activity during vicarious experience of actions is a widely reported and studied phenomenon, and motor system activity also accompanies observation of graspable objects in the absence of any actions. Such motor activity is thought to reflect simulation of the observed action, or preparation to interact with the object, respectively. Results Here, in an initial exploratory study, we ask whether motor activity during observation of object directed actions is involved in processes related to recognition of the object after initial exposure. Single pulse Transcranial Magnetic Stimulation (TMS) was applied over the thumb representation of the motor cortex, or over the vertex, during observation of a model thumb typing on a cell-phone, and performance on a phone recognition task at the end of the trial was assessed. Disrupting motor processing over the thumb representation 100 ms after the onset of the typing video impaired the ability to recognize the phone in the recognition test, whereas there was no such effect for TMS applied over the vertex and no TMS trials. Furthermore, this effect only manifested for videos observed from the first person perspective. In an additional control condition, there was no evidence for any effects of TMS to the thumb representation or vertex when observing and recognizing non-action related shape stimuli. Conclusion Overall, these data provide evidence that motor cortical processing during observation of object-directed actions from a first person perspective is causally linked to the formation of enduring representations of objects-of-action. PMID:24330638

Figure-ground organization and object recognition processes: an interactive account.

Science.gov (United States)

Vecera, S P; O'Reilly, R C

1998-04-01

Traditional bottom-up models of visual processing assume that figure-ground organization precedes object recognition. This assumption seems logically necessary: How can object recognition occur before a region is labeled as figure? However, some behavioral studies find that familiar regions are more likely to be labeled figure than less familiar regions, a problematic finding for bottom-up models. An interactive account is proposed in which figure-ground processes receive top-down input from object representations in a hierarchical system. A graded, interactive computational model is presented that accounts for behavioral results in which familiarity effects are found. The interactive model offers an alternative conception of visual processing to bottom-up models.

Short-term blueberry-enriched antioxidant diet prevents and reverses object recognition memory loss in aged rats

Science.gov (United States)

Objective Previously, four months of a blueberry-enriched (BB) antioxidant diet prevented impaired object recognition memory in aged rats. Experiment 1 determined whether one and two-month BB diets would have a similar effect and whether the benefits would disappear promptly after terminating the d...

Dopamine D1 receptor stimulation modulates the formation and retrieval of novel object recognition memory: Role of the prelimbic cortex.

Science.gov (United States)

Pezze, Marie A; Marshall, Hayley J; Fone, Kevin C F; Cassaday, Helen J

2015-11-01

Previous studies have shown that dopamine D1 receptor antagonists impair novel object recognition memory but the effects of dopamine D1 receptor stimulation remain to be determined. This study investigated the effects of the selective dopamine D1 receptor agonist SKF81297 on acquisition and retrieval in the novel object recognition task in male Wistar rats. SKF81297 (0.4 and 0.8 mg/kg s.c.) given 15 min before the sampling phase impaired novel object recognition evaluated 10 min or 24 h later. The same treatments also reduced novel object recognition memory tested 24 h after the sampling phase and when given 15 min before the choice session. These data indicate that D1 receptor stimulation modulates both the encoding and retrieval of object recognition memory. Microinfusion of SKF81297 (0.025 or 0.05 μg/side) into the prelimbic sub-region of the medial prefrontal cortex (mPFC) in this case 10 min before the sampling phase also impaired novel object recognition memory, suggesting that the mPFC is one important site mediating the effects of D1 receptor stimulation on visual recognition memory. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

HONTIOR - HIGHER-ORDER NEURAL NETWORK FOR TRANSFORMATION INVARIANT OBJECT RECOGNITION

Science.gov (United States)

Spirkovska, L.

1994-01-01

Neural networks have been applied in numerous fields, including transformation invariant object recognition, wherein an object is recognized despite changes in the object's position in the input field, size, or rotation. One of the more successful neural network methods used in invariant object recognition is the higher-order neural network (HONN) method. With a HONN, known relationships are exploited and the desired invariances are built directly into the architecture of the network, eliminating the need for the network to learn invariance to transformations. This results in a significant reduction in the training time required, since the network needs to be trained on only one view of each object, not on numerous transformed views. Moreover, one hundred percent accuracy is guaranteed for images characterized by the built-in distortions, providing noise is not introduced through pixelation. The program HONTIOR implements a third-order neural network having invariance to translation, scale, and in-plane rotation built directly into the architecture, Thus, for 2-D transformation invariance, the network needs only to be trained on just one view of each object. HONTIOR can also be used for 3-D transformation invariant object recognition by training the network only on a set of out-of-plane rotated views. Historically, the major drawback of HONNs has been that the size of the input field was limited to the memory required for the large number of interconnections in a fully connected network. HONTIOR solves this problem by coarse coding the input images (coding an image as a set of overlapping but offset coarser images). Using this scheme, large input fields (4096 x 4096 pixels) can easily be represented using very little virtual memory (30Mb). The HONTIOR distribution consists of three main programs. The first program contains the training and testing routines for a third-order neural network. The second program contains the same training and testing procedures as the

Modeling guidance and recognition in categorical search: bridging human and computer object detection.

Science.gov (United States)

Zelinsky, Gregory J; Peng, Yifan; Berg, Alexander C; Samaras, Dimitris

2013-10-08

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery.

Differential cortical c-Fos and Zif-268 expression after object and spatial memory processing in a standard or episodic-like object recognition task

Directory of Open Access Journals (Sweden)

Flávio F Barbosa

2013-08-01

Full Text Available Episodic memory reflects the capacity to recollect what, where and when a specific event happened in an integrative manner. Animal studies have suggested that the medial temporal lobe and the medial pre-frontal cortex are important for episodic-like memory formation. The goal of present study was to evaluate whether there are different patterns of expression of the immediate early genes c-Fos and Zif-268 in these cortical areas after rats are exposed to object recognition tasks with different cognitive demands. Male rats were randomly assigned to five groups: home cage control (CTR-HC, empty open field (CTR-OF, open field with one object (CTR-OF + Obj, novel object recognition task (OR and episodic-like memory task (ELM and were killed one hour after the last behavioral procedure. Rats were able to discriminate the objects in the OR task. In the ELM task, rats showed spatial (but not temporal discrimination of the objects. We found an increase in the c-Fos expression in the dorsal dentate gyrus (DG and in the perirhinal cortex (PRh in the OR and ELM groups. The OR group also presented an increase of c-Fos expression in the medial prefrontal cortex (mPFC. Additionally, the OR and ELM groups had increased expression of Zif-268 in the mPFC. Moreover, Zif-268 was increased in the dorsal CA1 and perirhinal cortex only in the ELM group. In conclusion, the pattern of activation was different in tasks with different cognitive demands. Accordingly, correlation tests suggest the engagement of different neural networks in the object recognition tasks used. Specifically, perirhinal-dentate gyrus co-activation was detected after the what-where memory retrieval, but not after the novel object recognition task. Both regions correlated with the respective behavioral outcome. These findings can be helpful in the understanding of the neural networks underlying memory tasks with different cognitive demands.

Development of visuo-haptic transfer for object recognition in typical preschool and school-aged children.

Science.gov (United States)

Purpura, Giulia; Cioni, Giovanni; Tinelli, Francesca

2018-07-01

Object recognition is a long and complex adaptive process and its full maturation requires combination of many different sensory experiences as well as cognitive abilities to manipulate previous experiences in order to develop new percepts and subsequently to learn from the environment. It is well recognized that the transfer of visual and haptic information facilitates object recognition in adults, but less is known about development of this ability. In this study, we explored the developmental course of object recognition capacity in children using unimodal visual information, unimodal haptic information, and visuo-haptic information transfer in children from 4 years to 10 years and 11 months of age. Participants were tested through a clinical protocol, involving visual exploration of black-and-white photographs of common objects, haptic exploration of real objects, and visuo-haptic transfer of these two types of information. Results show an age-dependent development of object recognition abilities for visual, haptic, and visuo-haptic modalities. A significant effect of time on development of unimodal and crossmodal recognition skills was found. Moreover, our data suggest that multisensory processes for common object recognition are active at 4 years of age. They facilitate recognition of common objects, and, although not fully mature, are significant in adaptive behavior from the first years of age. The study of typical development of visuo-haptic processes in childhood is a starting point for future studies regarding object recognition in impaired populations.

Object Recognition in Clutter: Cortical Responses Depend on the Type of Learning

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Jay eHegdé

2012-06-01

Full Text Available Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects’ recognition performance for both sets of objects in strong clutter. We found many brain regions that were differentially responsive to objects during object recognition depending on whether they were learned in strong or weak clutter. In particular, the responses of the left fusiform gyrus reliably reflected, on a trial-to-trial basis, subjects’ object recognition performance for objects learned in the presence of strong clutter. These results indicate that the visual system does not use a single, general-purpose mechanism to cope with clutter. Instead, there are two distinct spatial patterns of activation whose responses are attributable not to the visual context in which the objects were seen, but to the context in which the objects were learned.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

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Vito Janko

2017-12-01

Full Text Available The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

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Janko, Vito; Luštrek, Mitja

2017-12-29

The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

Probabilistic Open Set Recognition

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Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

Visual object recognition and category-specificity

DEFF Research Database (Denmark)

Gerlach, Christian

This thesis is based on seven published papers. The majority of the papers address two topics in visual object recognition: (i) category-effects at pre-semantic stages, and (ii) the integration of visual elements into elaborate shape descriptions corresponding to whole objects or large object parts...... (shape configuration). In the early writings these two topics were examined more or less independently. In later works, findings concerning category-effects and shape configuration merge into an integrated model, termed RACE, advanced to explain category-effects arising at pre-semantic stages in visual...... in visual long-term memory. In the thesis it is described how this simple model can account for a wide range of findings on category-specificity in both patients with brain damage and normal subjects. Finally, two hypotheses regarding the neural substrates of the model's components - and how activation...

An Intelligent Systems Approach to Automated Object Recognition: A Preliminary Study

Science.gov (United States)

Maddox, Brian G.; Swadley, Casey L.

2002-01-01

Attempts at fully automated object recognition systems have met with varying levels of success over the years. However, none of the systems have achieved high enough accuracy rates to be run unattended. One of the reasons for this may be that they are designed from the computer's point of view and rely mainly on image-processing methods. A better solution to this problem may be to make use of modern advances in computational intelligence and distributed processing to try to mimic how the human brain is thought to recognize objects. As humans combine cognitive processes with detection techniques, such a system would combine traditional image-processing techniques with computer-based intelligence to determine the identity of various objects in a scene.

Industrial robots with sensors and object recognition systems

International Nuclear Information System (INIS)

Koehler, G.W.

1978-01-01

The previous development and the present status of industrial robots equipped with sensors and object recognition systems are described. This type of equipment allows flexible automation of many work stations in which industrial robots of the first generation, which are unable to react to changes in their respective environments automatically, apart from their being linked to other machines, could not be used because of the prevailing boundary conditions. A classification system facilitates an overview of the large number of technical solutions now available. The manifold possibilities of application of this equipment are demonstrated by a number of examples. As a result of the present state of development of the components required, and in view also of economic reasons, there is a trend towards special designs for a small number of specific purposes and towards stripped-down object recognition. systems with limited applications. A fitting description is offered of the term 'robot', which is now being used in various contexts, and an indication is made of the capabilities and components a machine to be called robot should have as a minimum. Finally, reference is made to some potential lines of development serving to reduce expediture and accelerate recognition processes. (orig.) [de

Evaluating Color Descriptors for Object and Scene Recognition

NARCIS (Netherlands)

van de Sande, K.E.A.; Gevers, T.; Snoek, C.G.M.

2010-01-01

Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been

Linguistic approach to object recognition by grasping

Energy Technology Data Exchange (ETDEWEB)

Marik, V

1982-01-01

A method for recognizing both the three-dimensional object shapes and their sizes by grasping them with an antropomorphic five-finger artificial hand is described. The hand is equipped with position sensing elements in the joints of the fingers and with a tactile transducer net on the palm surface. The linguistic method uses formal grammars and languages for the pattern description. The recognition is hierarchically arranged, every level being different from the others by a formal language which has been used. On every level the pattern description is generated and verified from the symmetrical and semantical points of view. The results of the implementation of the recognition of cones, pyramides, spheres, prisms and cylinders are presented and discussed. 8 references.

A biologically inspired neural network model to transformation invariant object recognition

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

2007-09-01

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

Deletion of the GluA1 AMPA receptor subunit impairs recency-dependent object recognition memory

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Sanderson, David J.; Hindley, Emma; Smeaton, Emily; Denny, Nick; Taylor, Amy; Barkus, Chris; Sprengel, Rolf; Seeburg, Peter H.; Bannerman, David M.

2011-01-01

Deletion of the GluA1 AMPA receptor subunit impairs short-term spatial recognition memory. It has been suggested that short-term recognition depends upon memory caused by the recent presentation of a stimulus that is independent of contextual–retrieval processes. The aim of the present set of experiments was to test whether the role of GluA1 extends to nonspatial recognition memory. Wild-type and GluA1 knockout mice were tested on the standard object recognition task and a context-independent recognition task that required recency-dependent memory. In a first set of experiments it was found that GluA1 deletion failed to impair performance on either of the object recognition or recency-dependent tasks. However, GluA1 knockout mice displayed increased levels of exploration of the objects in both the sample and test phases compared to controls. In contrast, when the time that GluA1 knockout mice spent exploring the objects was yoked to control mice during the sample phase, it was found that GluA1 deletion now impaired performance on both the object recognition and the recency-dependent tasks. GluA1 deletion failed to impair performance on a context-dependent recognition task regardless of whether object exposure in knockout mice was yoked to controls or not. These results demonstrate that GluA1 is necessary for nonspatial as well as spatial recognition memory and plays an important role in recency-dependent memory processes. PMID:21378100

Global precedence effects account for individual differences in both face and object recognition performance.

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Gerlach, Christian; Starrfelt, Randi

2018-03-20

There has been an increase in studies adopting an individual difference approach to examine visual cognition and in particular in studies trying to relate face recognition performance with measures of holistic processing (the face composite effect and the part-whole effect). In the present study we examine whether global precedence effects, measured by means of non-face stimuli in Navon's paradigm, can also account for individual differences in face recognition and, if so, whether the effect is of similar magnitude for faces and objects. We find evidence that global precedence effects facilitate both face and object recognition, and to a similar extent. Our results suggest that both face and object recognition are characterized by a coarse-to-fine temporal dynamic, where global shape information is derived prior to local shape information, and that the efficiency of face and object recognition is related to the magnitude of the global precedence effect.

The interplay between perceptual organization and object recognition: Temporal dynamics and neuropsychology

OpenAIRE

Torfs, Katrien

2012-01-01

The ease and efficiency with which we perceive objects in daily life masks the complexity of the processes involved. The main goal of my doctoral research was to enhance our understanding of the complex interplay between perceptual organization and object recognition. To this end, we investigated the dynamic interplay between different component processes of object recognition, and their temporal dynamics. In the first part of this thesis, I present three behavioral studies focusing on the ro...

Recurrent processing during object recognition

Directory of Open Access Journals (Sweden)

Randall C. O'Reilly

2013-04-01

Full Text Available How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of of naturally-occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain's visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time.

A Large-Scale 3D Object Recognition dataset

DEFF Research Database (Denmark)

Sølund, Thomas; Glent Buch, Anders; Krüger, Norbert

2016-01-01

geometric groups; concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching...... performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat...

Lateral septal vasopressin in rats : Role in social and object recognition?

NARCIS (Netherlands)

Everts, H.G J; Koolhaas, J.M.

1997-01-01

The capacity of male rats to remember familiar conspecifics is called social recognition. It is a form of short-term memory modulated by lateral septal (LS) vasopressin (VP). The specificity of this phenomenon was studied by examining whether recognition of previously investigated objects is also

Standardization of transportation classes for object-oriented deployment simulations.

Energy Technology Data Exchange (ETDEWEB)

Burke, J. F., Jr.; Howard, D. L.; Jackson, J.; Macal, C. M.; Nevins, M. R.; Van Groningen, C. N.

1999-07-30

Many recent efforts to integrate transportation and deployment simulations, although beneficial, have lacked a feature vital for seamless integration: a common data class representation. It is an objective of the Department of Defense (DoD) to standardize all classes used in object-oriented deployment simulations by developing a standard class attribute representation and behavior for all deployment simulations that rely on an underlying class representation. The Extensive Hierarchy and Object Representation for Transportation Simulations (EXHORT) is a collection of three hierarchies that together will constitute a standard and consistent class attribute representation and behavior that could be used directly by a large set of deployment simulations. The first hierarchy is the Transportation Class Hierarchy (TCH), which describes a significant portion of the defense transportation system; the other two deal with infrastructure and resource classes. EXHORT will allow deployment simulations to use the same set of underlying class data, ensure transparent exchanges, reduce the effort needed to integrate simulations, and permit a detailed analysis of the defense transportation system. This paper describes EXHORT's first hierarchy, the TCH, and provides a rationale for why it is a helpful tool for modeling major portions of the defense transportation system.

Noradrenergic activation of the basolateral amygdala modulates the consolidation of object-in-context recognition memory

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Areg eBarsegyan

2014-05-01

Full Text Available Noradrenergic activation of the basolateral complex of the amygdala (BLA is well known to enhance the consolidation of long-term memory of highly emotionally arousing training experiences. The present study investigated whether such noradrenergic activation of the BLA also influences the consolidation of object-in-context recognition memory, a low-arousing training task assessing episodic-like memory. Male Sprague–Dawley rats were exposed to two identical objects in one context for either 3 or 10 min, immediately followed by exposure to two other identical objects in a distinctly different context. Immediately after the training they received bilateral intra-BLA infusions of norepinephrine (0.3, 1.0 or 3.0 μg or the β-adrenoceptor antagonist propranolol (0.1, 0.3 or 1.0 μg. On the 24-h retention test, rats were placed back into one of the training contexts with one copy of each of the two training objects. Thus, although both objects were familiar, one of the objects had not previously been encountered in this particular test context. Hence, if the animal generated a long-term memory for the association between an object and its context, it would spend significantly more time exploring the object that was not previously experienced in this context. Saline-infused control rats exhibited poor 24-h retention when given 3 min of training and good retention when given 10 min of training. Norepinephrine administered after 3 min of object-in-context training induced a dose-dependent memory enhancement, whereas propranolol administered after 10 min of training produced memory impairment. These findings provide evidence that posttraining noradrenergic activation of the BLA also enhances the consolidation of memory of object-in-context recognition training, enabling accuracy of episodic-like memories.

Ear recognition from one sample per person.

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Long Chen

Full Text Available Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

Ear recognition from one sample per person.

Science.gov (United States)

Chen, Long; Mu, Zhichun; Zhang, Baoqing; Zhang, Yi

2015-01-01

Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP) in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP) available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC) ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

Speckle-learning-based object recognition through scattering media.

Science.gov (United States)

Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

2015-12-28

We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning.

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

Science.gov (United States)

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.

Critical object recognition in millimeter-wave images with robustness to rotation and scale.

Science.gov (United States)

Mohammadzade, Hoda; Ghojogh, Benyamin; Faezi, Sina; Shabany, Mahdi

2017-06-01

Locating critical objects is crucial in various security applications and industries. For example, in security applications, such as in airports, these objects might be hidden or covered under shields or secret sheaths. Millimeter-wave images can be utilized to discover and recognize the critical objects out of the hidden cases without any health risk due to their non-ionizing features. However, millimeter-wave images usually have waves in and around the detected objects, making object recognition difficult. Thus, regular image processing and classification methods cannot be used for these images and additional pre-processings and classification methods should be introduced. This paper proposes a novel pre-processing method for canceling rotation and scale using principal component analysis. In addition, a two-layer classification method is introduced and utilized for recognition. Moreover, a large dataset of millimeter-wave images is collected and created for experiments. Experimental results show that a typical classification method such as support vector machines can recognize 45.5% of a type of critical objects at 34.2% false alarm rate (FAR), which is a drastically poor recognition. The same method within the proposed recognition framework achieves 92.9% recognition rate at 0.43% FAR, which indicates a highly significant improvement. The significant contribution of this work is to introduce a new method for analyzing millimeter-wave images based on machine vision and learning approaches, which is not yet widely noted in the field of millimeter-wave image analysis.

Object recognition in images via a factor graph model

Science.gov (United States)

He, Yong; Wang, Long; Wu, Zhaolin; Zhang, Haisu

2018-04-01

Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of- Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.

Reliable Recognition of Partially Occluded Objects with Correlation Filters

Directory of Open Access Journals (Sweden)

Alexey Ruchay

2018-01-01

Full Text Available Design of conventional correlation filters requires explicit knowledge of the appearance and shape of a target object, so the performance of correlation filters is significantly affected by changes in the appearance of the object in the input scene. In particular, the performance of correlation filters worsens when objects to be recognized are partially occluded by other objects, and the input scene contains a cluttered background and noise. In this paper, we propose a new algorithm for the design of a system consisting of a set of adaptive correlation filters for recognition of partially occluded objects in noisy scenes. Since the input scene may contain different fragments of the target, false objects, and background to be rejected, the system is designed in such a manner to guarantee equally high correlation peaks corresponding to parts of the target in the scenes. The key points of the system are as follows: (i it consists of a bank of composite optimum filters, which yield the best performance for different parts of the target; (ii it includes a fragmentation of the target into a given number of parts in the training stage to provide equal intensity responses of the system for each part of the target. With the help of computer simulation, the performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.

Three-dimensional model-based object recognition and segmentation in cluttered scenes.

Science.gov (United States)

Mian, Ajmal S; Bennamoun, Mohammed; Owens, Robyn

2006-10-01

Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition †

Science.gov (United States)

Janko, Vito

2017-01-01

The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. PMID:29286301

Neurophysiological indices of perceptual object priming in the absence of explicit recognition memory.

Science.gov (United States)

Harris, Jill D; Cutmore, Tim R H; O'Gorman, John; Finnigan, Simon; Shum, David

2009-02-01

The aim of this study was to identify ERP correlates of perceptual object priming that are insensitive to factors affecting explicit, episodic memory. EEG was recorded from 21 participants while they performed a visual object recognition test on a combination of unstudied items and old items that were previously encountered during either a 'deep' or 'shallow' levels-of-processing (LOP) study task. The results demonstrated a midline P150 old/new effect which was sensitive only to objects' old/new status and not to the accuracy of recognition responses to old items, or to the LOP manipulation. Similar outcomes were observed for the subsequent P200 and N400 effects, the former of which had a parietal scalp maximum and the latter, a broadly distributed topography. In addition an LPC old/new effect typical of those reported in past ERP recognition studies was observed. These outcomes support the proposal that the P150 effect is reflective of perceptual object priming and moreover, provide novel evidence that this and the P200 effect are independent of explicit recognition memory process(es).

Category-Specific Visual Recognition and Aging from the PACE Theory Perspective: Evidence for a Presemantic Deficit in Aging Object Recognition

DEFF Research Database (Denmark)

Bordaberry, Pierre; Gerlach, Christian; Lenoble, Quentin

2016-01-01

Background/Study Context: The objective of this study was to investigate the object recognition deficit in aging. Age-related declines were examined from the presemantic account of category effects (PACE) theory perspective (Gerlach, 2009, Cognition, 111, 281–301). This view assumes that the stru......Background/Study Context: The objective of this study was to investigate the object recognition deficit in aging. Age-related declines were examined from the presemantic account of category effects (PACE) theory perspective (Gerlach, 2009, Cognition, 111, 281–301). This view assumes...... that the structural similarity/dissimilarity inherent in living and nonliving objects, respectively, can account for a wide range of category-specific effects. Methods: In two experiments on object recognition, young (36 participants, 18–27 years) and older (36 participants, 53–69 years) adult participants...... in the selection stage of the PACE theory (visual long-term memory matching) could be responsible for these impairments. Indeed, the older group showed a deficit when this stage was most relevant. This article emphasize on the critical need for taking into account structural component of the stimuli and type...

Detection, information fusion, and temporal processing for intelligence in recognition

Energy Technology Data Exchange (ETDEWEB)

Casasent, D. [Carnegie Mellon Univ., Pittsburgh, PA (United States)

1996-12-31

The use of intelligence in vision recognition uses many different techniques or tools. This presentation discusses several of these techniques for recognition. The recognition process is generally separated into several steps or stages when implemented in hardware, e.g. detection, segmentation and enhancement, and recognition. Several new distortion-invariant filters, biologically-inspired Gabor wavelet filter techniques, and morphological operations that have been found very useful for detection and clutter rejection are discussed. These are all shift-invariant operations that allow multiple object regions of interest in a scene to be located in parallel. We also discuss new algorithm fusion concepts by which the results from different detection algorithms are combined to reduce detection false alarms; these fusion methods utilize hierarchical processing and fuzzy logic concepts. We have found this to be most necessary, since no single detection algorithm is best for all cases. For the final recognition stage, we describe a new method of representing all distorted versions of different classes of objects and determining the object class and pose that most closely matches that of a given input. Besides being efficient in terms of storage and on-line computations required, it overcomes many of the problems that other classifiers have in terms of the required training set size, poor generalization with many hidden layer neurons, etc. It is also attractive in its ability to reject input regions as clutter (non-objects) and to learn new object descriptions. We also discuss its use in processing a temporal sequence of input images of the contents of each local region of interest. We note how this leads to robust results in which estimation efforts in individual frames can be overcome. This seems very practical, since in many scenarios a decision need not be made after only one frame of data, since subsequent frames of data enter immediately in sequence.

Modeling guidance and recognition in categorical search: Bridging human and computer object detection

OpenAIRE

Zelinsky, Gregory J.; Peng, Yifan; Berg, Alexander C.; Samaras, Dimitris

2013-01-01

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of ...

Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

Science.gov (United States)

Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

2015-01-01

Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

Development of novel tasks for studying view-invariant object recognition in rodents: Sensitivity to scopolamine.

Science.gov (United States)

Mitchnick, Krista A; Wideman, Cassidy E; Huff, Andrew E; Palmer, Daniel; McNaughton, Bruce L; Winters, Boyer D

2018-05-15

The capacity to recognize objects from different view-points or angles, referred to as view-invariance, is an essential process that humans engage in daily. Currently, the ability to investigate the neurobiological underpinnings of this phenomenon is limited, as few ethologically valid view-invariant object recognition tasks exist for rodents. Here, we report two complementary, novel view-invariant object recognition tasks in which rodents physically interact with three-dimensional objects. Prior to experimentation, rats and mice were given extensive experience with a set of 'pre-exposure' objects. In a variant of the spontaneous object recognition task, novelty preference for pre-exposed or new objects was assessed at various angles of rotation (45°, 90° or 180°); unlike control rodents, for whom the objects were novel, rats and mice tested with pre-exposed objects did not discriminate between rotated and un-rotated objects in the choice phase, indicating substantial view-invariant object recognition. Secondly, using automated operant touchscreen chambers, rats were tested on pre-exposed or novel objects in a pairwise discrimination task, where the rewarded stimulus (S+) was rotated (180°) once rats had reached acquisition criterion; rats tested with pre-exposed objects re-acquired the pairwise discrimination following S+ rotation more effectively than those tested with new objects. Systemic scopolamine impaired performance on both tasks, suggesting involvement of acetylcholine at muscarinic receptors in view-invariant object processing. These tasks present novel means of studying the behavioral and neural bases of view-invariant object recognition in rodents. Copyright © 2018 Elsevier B.V. All rights reserved.

Standard object recognition memory and "what" and "where" components: Improvement by post-training epinephrine in highly habituated rats.

Science.gov (United States)

Jurado-Berbel, Patricia; Costa-Miserachs, David; Torras-Garcia, Meritxell; Coll-Andreu, Margalida; Portell-Cortés, Isabel

2010-02-11

The present work examined whether post-training systemic epinephrine (EPI) is able to modulate short-term (3h) and long-term (24 h and 48 h) memory of standard object recognition, as well as long-term (24 h) memory of separate "what" (object identity) and "where" (object location) components of object recognition. Although object recognition training is associated to low arousal levels, all the animals received habituation to the training box in order to further reduce emotional arousal. Post-training EPI improved long-term (24 h and 48 h), but not short-term (3 h), memory in the standard object recognition task, as well as 24 h memory for both object identity and object location. These data indicate that post-training epinephrine: (1) facilitates long-term memory for standard object recognition; (2) exerts separate facilitatory effects on "what" (object identity) and "where" (object location) components of object recognition; and (3) is capable of improving memory for a low arousing task even in highly habituated rats.

Dopamine D4 receptor stimulation contributes to novel object recognition: Relevance to cognitive impairment in schizophrenia.

Science.gov (United States)

Miyauchi, Masanori; Neugebauer, Nichole M; Meltzer, Herbert Y

2017-04-01

Several atypical antipsychotic drugs (APDs) have high affinity for the dopamine (DA) D 4 receptor, but the relevance to the efficacy for the treatment of cognitive impairment associated with schizophrenia (CIAS) is poorly understood. The aim of this study was to investigate the effects of D 4 receptor stimulation or blockade on novel object recognition (NOR) in normal rats and on the sub-chronic phencyclidine (PCP)-induced novel object recognition deficit. The effect of the D 4 agonist, PD168077, and the D 4 antagonist, L-745,870, were studied alone, and in combination with clozapine and lurasidone. In normal rats, L-745,870 impaired novel object recognition, whereas PD168077 had no effect. PD168077 acutely reversed the sub-chronic phencyclidine-induced novel object recognition deficit. Co-administration of a sub-effective dose (SED) of PD168077 with a sub-effective dose of lurasidone also reversed this deficit, but a sub-effective dose of PD168077 with a sub-effective dose of clozapine, a more potent D 4 antagonist than lurasidone, did not reverse the sub-chronic phencyclidine-induced novel object recognition deficit. At a dose that did not induce a novel object recognition deficit, L-745,870 blocked the ability of clozapine, but not lurasidone, to reverse the novel object recognition deficit. D 4 receptor agonism has a beneficial effect on novel object recognition in sub-chronic PCP-treated rats and augments the cognitive enhancing efficacy of an atypical antipsychotic drug that lacks affinity for the D 4 receptor, lurasidone.

Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

NARCIS (Netherlands)

Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

2016-01-01

Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

A C++ Class for Rule-Base Objects

Directory of Open Access Journals (Sweden)

William J. Grenney

1992-01-01

Full Text Available A C++ class, called Tripod, was created as a tool to assist with the development of rule-base decision support systems. The Tripod class contains data structures for the rule-base and member functions for operating on the data. The rule-base is defined by three ASCII files. These files are translated by a preprocessor into a single file that is located when a rule-base object is instantiated. The Tripod class was tested as part of a proto-type decision support system (DSS for winter highway maintenance in the Intermountain West. The DSS is composed of two principal modules: the main program, called the wrapper, and a Tripod rule-base object. The wrapper is a procedural module that interfaces with remote sensors and an external meterological database. The rule-base contains the logic for advising an inexperienced user and for assisting with the decision making process.

It's all connected: Pathways in visual object recognition and early noun learning.

Science.gov (United States)

Smith, Linda B

2013-11-01

A developmental pathway may be defined as the route, or chain of events, through which a new structure or function forms. For many human behaviors, including object name learning and visual object recognition, these pathways are often complex and multicausal and include unexpected dependencies. This article presents three principles of development that suggest the value of a developmental psychology that explicitly seeks to trace these pathways and uses empirical evidence on developmental dependencies among motor development, action on objects, visual object recognition, and object name learning in 12- to 24-month-old infants to make the case. The article concludes with a consideration of the theoretical implications of this approach. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Multi-sensor Object Recognition: The Case of Electronics Recycling

NARCIS (Netherlands)

van Dop, E.R.

1999-01-01

In automated object recognition systems, measurements from a single source of information do not always suffice for the reconstruction of the underlying scene. Incompleteness, inaccuracy and unreliability of the information often leaves room for multiple interpretations of the world which are

Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

Science.gov (United States)

Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

2017-10-01

Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

The subjective experience of object recognition: comparing metacognition for object detection and object categorization.

Science.gov (United States)

Meuwese, Julia D I; van Loon, Anouk M; Lamme, Victor A F; Fahrenfort, Johannes J

2014-05-01

Perceptual decisions seem to be made automatically and almost instantly. Constructing a unitary subjective conscious experience takes more time. For example, when trying to avoid a collision with a car on a foggy road you brake or steer away in a reflex, before realizing you were in a near accident. This subjective aspect of object recognition has been given little attention. We used metacognition (assessed with confidence ratings) to measure subjective experience during object detection and object categorization for degraded and masked objects, while objective performance was matched. Metacognition was equal for degraded and masked objects, but categorization led to higher metacognition than did detection. This effect turned out to be driven by a difference in metacognition for correct rejection trials, which seemed to be caused by an asymmetry of the distractor stimulus: It does not contain object-related information in the detection task, whereas it does contain such information in the categorization task. Strikingly, this asymmetry selectively impacted metacognitive ability when objective performance was matched. This finding reveals a fundamental difference in how humans reflect versus act on information: When matching the amount of information required to perform two tasks at some objective level of accuracy (acting), metacognitive ability (reflecting) is still better in tasks that rely on positive evidence (categorization) than in tasks that rely more strongly on an absence of evidence (detection).

Spontaneous Object Recognition Memory in Aged Rats: Complexity versus Similarity

Science.gov (United States)

Gamiz, Fernando; Gallo, Milagros

2012-01-01

Previous work on the effect of aging on spontaneous object recognition (SOR) memory tasks in rats has yielded controversial results. Although the results at long-retention intervals are consistent, conflicting results have been reported at shorter delays. We have assessed the potential relevance of the type of object used in the performance of…

Selective attention affects conceptual object priming and recognition: a study with young and older adults.

Science.gov (United States)

Ballesteros, Soledad; Mayas, Julia

2014-01-01

In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1) and old-new recognition memory (Experiment 2) tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants were instructed to attend to the picture outline of a certain color and to classify the object as natural or artificial. After a short delay, participants performed a natural/artificial speeded conceptual classification task with repeated attended, repeated unattended, and new pictures. In Experiment 2, participants at encoding memorized the attended pictures and classify them as natural or artificial. After the encoding phase, they performed an old-new recognition memory task. Consistent with previous findings with perceptual priming tasks, we found that conceptual object priming, like explicit memory, required attention at encoding. Significant priming was obtained in both age groups, but only for those pictures that were attended at encoding. Although older adults were slower than young adults, both groups showed facilitation for attended pictures. In line with previous studies, young adults had better recognition memory than older adults.

Selective attention affects conceptual object priming and recognition: A study with young and older adults

Directory of Open Access Journals (Sweden)

Soledad eBallesteros

2015-01-01

Full Text Available In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1 and old-new recognition memory (Experiment 2 tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants were instructed to attend to the picture outline of a certain color and to classify the object as natural or artificial. After a short delay, participants performed a natural/ artificial speeded conceptual classification task with repeated attended, repeated unattended and new pictures. In Experiment 2, participants at encoding memorized the attended pictures and classified them as natural or artificial. After the encoding phase, they performed an old-new recognition memory task. Consistent with previous findings with perceptual priming tasks, we found that conceptual object priming, like explicit memory, required attention at encoding. Significant priming was obtained in both age groups, but only for those pictures that were attended at encoding. Although older adults were slower than young adults, both groups showed facilitation for attended pictures. In line with previous studies, young adults had better recognition memory than older adults.

Asymmetric Functional Connectivity of the Contra- and Ipsilateral Secondary Somatosensory Cortex during Tactile Object Recognition

Directory of Open Access Journals (Sweden)

Yinghua Yu

2018-01-01

Full Text Available In the somatosensory system, it is well known that the bilateral secondary somatosensory cortex (SII receives projections from the unilateral primary somatosensory cortex (SI, and the SII, in turn, sends feedback projections to SI. Most neuroimaging studies have clearly shown bilateral SII activation using only unilateral stimulation for both anatomical and functional connectivity across SII subregions. However, no study has unveiled differences in the functional connectivity of the contra- and ipsilateral SII network that relates to frontoparietal areas during tactile object recognition. Therefore, we used event-related functional magnetic resonance imaging (fMRI and a delayed match-to-sample (DMS task to investigate the contributions of bilateral SII during tactile object recognition. In the fMRI experiment, 14 healthy subjects were presented with tactile angle stimuli on their right index finger and asked to encode three sample stimuli during the encoding phase and one test stimulus during the recognition phase. Then, the subjects indicated whether the angle of test stimulus was presented during the encoding phase. The results showed that contralateral (left SII activity was greater than ipsilateral (right SII activity during the encoding phase, but there was no difference during the recognition phase. A subsequent psycho-physiological interaction (PPI analysis revealed distinct connectivity from the contra- and ipsilateral SII to other regions. The left SII functionally connected to the left SI and right primary and premotor cortex, while the right SII functionally connected to the left posterior parietal cortex (PPC. Our findings suggest that in situations involving unilateral tactile object recognition, contra- and ipsilateral SII will induce an asymmetrical functional connectivity to other brain areas, which may occur by the hand contralateral effect of SII.

Noradrenergic activation of the basolateral amygdala modulates the consolidation of object-in-context recognition memory

OpenAIRE

Barsegyan, Areg; McGaugh, James L.; Roozendaal, Benno

2014-01-01

Noradrenergic activation of the basolateral complex of the amygdala (BLA) is well known to enhance the consolidation of long-term memory of highly emotionally arousing training experiences. The present study investigated whether such noradrenergic activation of the BLA also influences the consolidation of object-in-context recognition memory, a low-arousing training task assessing episodic-like memory. Male Sprague-Dawley rats were exposed to two identical objects in one context for either 3 ...

Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma.

Science.gov (United States)

Thomaz, Ricardo de Lima; Carneiro, Pedro Cunha; Bonin, João Eliton; Macedo, Túlio Augusto Alves; Patrocinio, Ana Claudia; Soares, Alcimar Barbosa

2018-05-01

Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch's t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC.

Neuropeptide S interacts with the basolateral amygdala noradrenergic system in facilitating object recognition memory consolidation.

Science.gov (United States)

Han, Ren-Wen; Xu, Hong-Jiao; Zhang, Rui-San; Wang, Pei; Chang, Min; Peng, Ya-Li; Deng, Ke-Yu; Wang, Rui

2014-01-01

The noradrenergic activity in the basolateral amygdala (BLA) was reported to be involved in the regulation of object recognition memory. As the BLA expresses high density of receptors for Neuropeptide S (NPS), we investigated whether the BLA is involved in mediating NPS's effects on object recognition memory consolidation and whether such effects require noradrenergic activity. Intracerebroventricular infusion of NPS (1nmol) post training facilitated 24-h memory in a mouse novel object recognition task. The memory-enhancing effect of NPS could be blocked by the β-adrenoceptor antagonist propranolol. Furthermore, post-training intra-BLA infusions of NPS (0.5nmol/side) improved 24-h memory for objects, which was impaired by co-administration of propranolol (0.5μg/side). Taken together, these results indicate that NPS interacts with the BLA noradrenergic system in improving object recognition memory during consolidation. Copyright © 2013 Elsevier Inc. All rights reserved.

A General Polygon-based Deformable Model for Object Recognition

DEFF Research Database (Denmark)

Jensen, Rune Fisker; Carstensen, Jens Michael

1999-01-01

We propose a general scheme for object localization and recognition based on a deformable model. The model combines shape and image properties by warping a arbitrary prototype intensity template according to the deformation in shape. The shape deformations are constrained by a probabilistic distr...

Enriched environment effects on remote object recognition memory.

Science.gov (United States)

Melani, Riccardo; Chelini, Gabriele; Cenni, Maria Cristina; Berardi, Nicoletta

2017-06-03

Since Ebbinghaus' classical work on oblivion and saving effects, we know that declarative memories may become at first spontaneously irretrievable and only subsequently completely extinguished. Recently, this time-dependent path toward memory-trace loss has been shown to correlate with different patterns of brain activation. Environmental enrichment (EE) enhances learning and memory and affects system memory consolidation. However, there is no evidence on whether and how EE could affect the time-dependent path toward oblivion. We used Object Recognition Test (ORT) to assess in adult mice put in EE for 40days (EE mice) or left in standard condition (SC mice) memory retrieval of the familiar objects 9 and 21days after learning with or without a brief retraining performed the day before. We found that SC mice show preferential exploration of new object at day 9 only with retraining, while EE mice do it even without. At day 21 SC mice do not show preferential exploration of novel object, irrespective of the retraining, while EE mice are still capable to benefit from retraining, even if they were not able to spontaneously recover the trace. Analysis of c-fos expression 20days after learning shows a different pattern of active brain areas in response to the retraining session in EE and SC mice, with SC mice recruiting the same brain network as naïve SC or EE mice following de novo learning. This suggests that EE promotes formation of longer lasting object recognition memory, allowing a longer time window during which saving is present. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

An Approach to Object Recognition: Aligning Pictorial Descriptions.

Science.gov (United States)

1986-12-01

PERFORMING 0RGANIZATION NAMIE ANDORS IS551. PROGRAM ELEMENT. PROJECT. TASK Artificial Inteligence Laboratory AREKA A WORK UNIT NUMBERS ( 545 Technology... ARTIFICIAL INTELLIGENCE LABORATORY A.I. Memo No. 931 December, 1986 AN APPROACH TO OBJECT RECOGNITION: ALIGNING PICTORIAL DESCRIPTIONS Shimon Ullman...within the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Support for the A.I. Laboratory’s artificial intelligence

Attentional Selection for Object Recognition - A Gentle Way

National Research Council Canada - National Science Library

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

2002-01-01

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

Deficits in long-term recognition memory reveal dissociated subtypes in congenital prosopagnosia.

Directory of Open Access Journals (Sweden)

Rainer Stollhoff

Full Text Available The study investigates long-term recognition memory in congenital prosopagnosia (CP, a lifelong impairment in face identification that is present from birth. Previous investigations of processing deficits in CP have mostly relied on short-term recognition tests to estimate the scope and severity of individual deficits. We firstly report on a controlled test of long-term (one year recognition memory for faces and objects conducted with a large group of participants with CP. Long-term recognition memory is significantly impaired in eight CP participants (CPs. In all but one case, this deficit was selective to faces and didn't extend to intra-class recognition of object stimuli. In a test of famous face recognition, long-term recognition deficits were less pronounced, even after accounting for differences in media consumption between controls and CPs. Secondly, we combined test results on long-term and short-term recognition of faces and objects, and found a large heterogeneity in severity and scope of individual deficits. Analysis of the observed heterogeneity revealed a dissociation of CP into subtypes with a homogeneous phenotypical profile. Thirdly, we found that among CPs self-assessment of real-life difficulties, based on a standardized questionnaire, and experimentally assessed face recognition deficits are strongly correlated. Our results demonstrate that controlled tests of long-term recognition memory are needed to fully assess face recognition deficits in CP. Based on controlled and comprehensive experimental testing, CP can be dissociated into subtypes with a homogeneous phenotypical profile. The CP subtypes identified align with those found in prosopagnosia caused by cortical lesions; they can be interpreted with respect to a hierarchical neural system for face perception.

Deficits in long-term recognition memory reveal dissociated subtypes in congenital prosopagnosia.

Science.gov (United States)

Stollhoff, Rainer; Jost, Jürgen; Elze, Tobias; Kennerknecht, Ingo

2011-01-25

The study investigates long-term recognition memory in congenital prosopagnosia (CP), a lifelong impairment in face identification that is present from birth. Previous investigations of processing deficits in CP have mostly relied on short-term recognition tests to estimate the scope and severity of individual deficits. We firstly report on a controlled test of long-term (one year) recognition memory for faces and objects conducted with a large group of participants with CP. Long-term recognition memory is significantly impaired in eight CP participants (CPs). In all but one case, this deficit was selective to faces and didn't extend to intra-class recognition of object stimuli. In a test of famous face recognition, long-term recognition deficits were less pronounced, even after accounting for differences in media consumption between controls and CPs. Secondly, we combined test results on long-term and short-term recognition of faces and objects, and found a large heterogeneity in severity and scope of individual deficits. Analysis of the observed heterogeneity revealed a dissociation of CP into subtypes with a homogeneous phenotypical profile. Thirdly, we found that among CPs self-assessment of real-life difficulties, based on a standardized questionnaire, and experimentally assessed face recognition deficits are strongly correlated. Our results demonstrate that controlled tests of long-term recognition memory are needed to fully assess face recognition deficits in CP. Based on controlled and comprehensive experimental testing, CP can be dissociated into subtypes with a homogeneous phenotypical profile. The CP subtypes identified align with those found in prosopagnosia caused by cortical lesions; they can be interpreted with respect to a hierarchical neural system for face perception.

Effects of physical exercise on object recognition memory in adult rats of postnatal isoflurane exposures

Directory of Open Access Journals (Sweden)

Xiao-yan FANG

2017-08-01

Full Text Available Objective To investigate effects of physical exercise (PE on object recognition memory in adult rats of postnatal isoflurane (Iso exposures. Methods One hundred and ten postnatal 7-day SD rats (P7 were randomly divided into four groups: normal control group (Naive, Naive+PE group (received physical exercise in P21: a treadmill exercise 30min each day, 5 times/week, for 6 weeks, Iso group (three times of 2-hour Iso exposure in P7, P9, and P11, and Iso+PE group (received PE in P21 after postnatal Iso exposures. In P67, behavioral testing was conducted including open field and object recognition task (ORT, recording the time (Discrimination Ratios, DR that rats spent on exploring each object, evaluating effects of PE on object recognition memory. Results There was no significant difference in influence of PE on open field testing in all of the groups (P>0.05. Compared with Naive, there was no group difference in DR (P>0.05 for all groups, but the DR of Iso male rats was significantly higher than that of Naive female rats in P67, with significant difference (P=0.034. Compared with non-PE groups, whether or not postnatal Iso exposures, the DR of PE male groups was significantly higher (compared with Naive and Iso group: P67, P=0.050, P=0.017; P95, P=0.037, P=0.019; in female rats, the DR for ISO+PE group was lower than that of Iso group in P67 (P=0.036, but the DR of Naive+PE group was higher than that of Naive group in P95 (P=0.004. Compared with male rats, the DR of non-PE female rats was significantly higher in P67 (vis. Naive and Iso group: P=0.022, P=0.011; but in P95, the DR of non- Iso female groups was significantly higher than that of male groups (vis. Naive and Naive+PE: P=0.008, P=0.017. Conclusions There is no obvious impact of postnatal Iso exposures on object recognition memory of adult rats. These results also indicate that postnatal PE could improve object recognition memory of non-spatial learning in adult rats. In addition, exercise

A class Hierarchical, object-oriented approach to virtual memory management

Science.gov (United States)

Russo, Vincent F.; Campbell, Roy H.; Johnston, Gary M.

1989-01-01

The Choices family of operating systems exploits class hierarchies and object-oriented programming to facilitate the construction of customized operating systems for shared memory and networked multiprocessors. The software is being used in the Tapestry laboratory to study the performance of algorithms, mechanisms, and policies for parallel systems. Described here are the architectural design and class hierarchy of the Choices virtual memory management system. The software and hardware mechanisms and policies of a virtual memory system implement a memory hierarchy that exploits the trade-off between response times and storage capacities. In Choices, the notion of a memory hierarchy is captured by abstract classes. Concrete subclasses of those abstractions implement a virtual address space, segmentation, paging, physical memory management, secondary storage, and remote (that is, networked) storage. Captured in the notion of a memory hierarchy are classes that represent memory objects. These classes provide a storage mechanism that contains encapsulated data and have methods to read or write the memory object. Each of these classes provides specializations to represent the memory hierarchy.

Hierarchical object class representation using holes and notches

Energy Technology Data Exchange (ETDEWEB)

Osbourn, G.C.

1989-01-01

A general representation approach is described which employs a hierarchy of holes and notches. A matching procedure is also described which allows non-ideal image hierarchies to be matched to class representations. The representation and matching methods are demonstrated on a set of handgun photographs. Examples of handguns which are different in detail are shown to exhibit the same class characteristics, while other similarly shaped objects are correctly distinguished from the handgun class. 6 refs., 8 figs.

Towards an Artificial Space Object Taxonomy

Science.gov (United States)

Wilkins, M.; Schumacher, P.; Jah, M.; Pfeffer, A.

2013-09-01

Object recognition is the first step in positively identifying a resident space object (RSO), i.e. assigning an RSO to a category such as GPS satellite or space debris. Object identification is the process of deciding that two RSOs are in fact one and the same. Provided we have appropriately defined a satellite taxonomy that allows us to place a given RSO into a particular class of object without any ambiguity, one can assess the probability of assignment to a particular class by determining how well the object satisfies the unique criteria of belonging to that class. Ultimately, tree-based taxonomies delineate unique signatures by defining the minimum amount of information required to positively identify a RSO. Therefore, taxonomic trees can be used to depict hypotheses in a Bayesian object recognition and identification process. This work describes a new RSO taxonomy along with specific reasoning behind the choice of groupings. An alternative taxonomy was recently presented at the Sixth Conference on Space Debris in Darmstadt, Germany. [1] The best example of a taxonomy that enjoys almost universal scientific acceptance is the classical Linnaean biological taxonomy. A strength of Linnaean taxonomy is that it can be used to organize the different kinds of living organisms, simply and practically. Every species can be given a unique name. This uniqueness and stability are a result of the acceptance by biologists specializing in taxonomy, not merely of the binomial names themselves. Fundamentally, the taxonomy is governed by rules for the use of these names, and these are laid down in formal Nomenclature Codes. We seek to provide a similar formal nomenclature system for RSOs through a defined tree-based taxonomy structure. Each categorization, beginning with the most general or inclusive, at any level is called a taxon. Taxon names are defined by a type, which can be a specimen or a taxon of lower rank, and a diagnosis, a statement intended to supply characters that

Selective attention affects conceptual object priming and recognition: A study with young and older adults

OpenAIRE

Soledad eBallesteros; Julia eMayas

2015-01-01

In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1) and old–new recognition memory (Experiment 2) tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants wer...

Understanding Intra-Class Knowledge Inside CNN

OpenAIRE

Wei, Donglai; Zhou, Bolei; Torrabla, Antonio; Freeman, William

2015-01-01

Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers. To invert the intra-class knowledge into more interpretable images, we propose a non-parametric patch prior upon previous CNN...

Computing with Connections in Visual Recognition of Origami Objects.

Science.gov (United States)

Sabbah, Daniel

1985-01-01

Summarizes an initial foray in tackling artificial intelligence problems using a connectionist approach. The task chosen is visual recognition of Origami objects, and the questions answered are how to construct a connectionist network to represent and recognize projected Origami line drawings and the advantages such an approach would have. (30…

Visual object recognition for automatic micropropagation of plants

Science.gov (United States)

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

1994-11-01

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 system. In this paper we describe parts of the vision system 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, stem, 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.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

Science.gov (United States)

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Neural network application for thermal image recognition of low-resolution objects

Science.gov (United States)

Fang, Yi-Chin; Wu, Bo-Wen

2007-02-01

In the ever-changing situation on a battle field, accurate recognition of a distant object is critical to a commander's decision-making and the general public's safety. Efficiently distinguishing between an enemy's armoured vehicles and ordinary civilian houses under all weather conditions has become an important research topic. This study presents a system for recognizing an armoured vehicle by distinguishing marks and contours. The characteristics of 12 different shapes and 12 characters are used to explore thermal image recognition under the circumstance of long distance and low resolution. Although the recognition capability of human eyes is superior to that of artificial intelligence under normal conditions, it tends to deteriorate substantially under long-distance and low-resolution scenarios. This study presents an effective method for choosing features and processing images. The artificial neural network technique is applied to further improve the probability of accurate recognition well beyond the limit of the recognition capability of human eyes.

A Scientific Workflow Platform for Generic and Scalable Object Recognition on Medical Images

Science.gov (United States)

Möller, Manuel; Tuot, Christopher; Sintek, Michael

In the research project THESEUS MEDICO we aim at a system combining medical image information with semantic background knowledge from ontologies to give clinicians fully cross-modal access to biomedical image repositories. Therefore joint efforts have to be made in more than one dimension: Object detection processes have to be specified in which an abstraction is performed starting from low-level image features across landmark detection utilizing abstract domain knowledge up to high-level object recognition. We propose a system based on a client-server extension of the scientific workflow platform Kepler that assists the collaboration of medical experts and computer scientists during development and parameter learning.

The Consolidation of Object and Context Recognition Memory Involve Different Regions of the Temporal Lobe

Science.gov (United States)

Balderas, Israela; Rodriguez-Ortiz, Carlos J.; Salgado-Tonda, Paloma; Chavez-Hurtado, Julio; McGaugh, James L.; Bermudez-Rattoni, Federico

2008-01-01

These experiments investigated the involvement of several temporal lobe regions in consolidation of recognition memory. Anisomycin, a protein synthesis inhibitor, was infused into the hippocampus, perirhinal cortex, insular cortex, or basolateral amygdala of rats immediately after the sample phase of object or object-in-context recognition memory…

Metric invariance in object recognition: a review and further evidence.

Science.gov (United States)

Cooper, E E; Biederman, I; Hummel, J E

1992-06-01

Phenomenologically, human shape recognition appears to be invariant with changes of orientation in depth (up to parts occlusion), position in the visual field, and size. Recent versions of template theories (e.g., Ullman, 1989; Lowe, 1987) assume that these invariances are achieved through the application of transformations such as rotation, translation, and scaling of the image so that it can be matched metrically to a stored template. Presumably, such transformations would require time for their execution. We describe recent priming experiments in which the effects of a prior brief presentation of an image on its subsequent recognition are assessed. The results of these experiments indicate that the invariance is complete: The magnitude of visual priming (as distinct from name or basic level concept priming) is not affected by a change in position, size, orientation in depth, or the particular lines and vertices present in the image, as long as representations of the same components can be activated. An implemented seven layer neural network model (Hummel & Biederman, 1992) that captures these fundamental properties of human object recognition is described. Given a line drawing of an object, the model activates a viewpoint-invariant structural description of the object, specifying its parts and their interrelations. Visual priming is interpreted as a change in the connection weights for the activation of: a) cells, termed geon feature assemblies (GFAs), that conjoin the output of units that represent invariant, independent properties of a single geon and its relations (such as its type, aspect ratio, relations to other geons), or b) a change in the connection weights by which several GFAs activate a cell representing an object.

Glucocorticoid effects on object recognition memory require training-associated emotional arousal.

Science.gov (United States)

Okuda, Shoki; Roozendaal, Benno; McGaugh, James L

2004-01-20

Considerable evidence implicates glucocorticoid hormones in the regulation of memory consolidation and memory retrieval. The present experiments investigated whether the influence of these hormones on memory depends on the level of emotional arousal induced by the training experience. We investigated this issue in male Sprague-Dawley rats by examining the effects of immediate posttraining systemic injections of the glucocorticoid corticosterone on object recognition memory under two conditions that differed in their training-associated emotional arousal. In rats that were not previously habituated to the experimental context, corticosterone (0.3, 1.0, or 3.0 mg/kg, s.c.) administered immediately after a 3-min training trial enhanced 24-hr retention performance in an inverted-U shaped dose-response relationship. In contrast, corticosterone did not affect 24-hr retention of rats that received extensive prior habituation to the experimental context and, thus, had decreased novelty-induced emotional arousal during training. Additionally, immediate posttraining administration of corticosterone to nonhabituated rats, in doses that enhanced 24-hr retention, impaired object recognition performance at a 1-hr retention interval whereas corticosterone administered after training to well-habituated rats did not impair 1-hr retention. Thus, the present findings suggest that training-induced emotional arousal may be essential for glucocorticoid effects on object recognition memory.

Priming Contour-Deleted Images: Evidence for Immediate Representations in Visual Object Recognition.

Science.gov (United States)

Biederman, Irving; Cooper, Eric E.

1991-01-01

Speed and accuracy of identification of pictures of objects are facilitated by prior viewing. Contributions of image features, convex or concave components, and object models in a repetition priming task were explored in 2 studies involving 96 college students. Results provide evidence of intermediate representations in visual object recognition.…

Activity recognition from minimal distinguishing subsequence mining

Science.gov (United States)

Iqbal, Mohammad; Pao, Hsing-Kuo

2017-08-01

Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

Object class hierarchy for an incremental hypertext editor

Directory of Open Access Journals (Sweden)

A. Colesnicov

1995-02-01

Full Text Available The object class hierarchy design is considered due to a hypertext editor implementation. The following basic classes were selected: the editor's coordinate system, the memory manager, the text buffer executing basic editing operations, the inherited hypertext buffer, the edit window, the multi-window shell. Special hypertext editing features, the incremental hypertext creation support and further generalizations are discussed.

Discovery of M class objects among the near-earth asteroid population

Science.gov (United States)

Tedesco, Edward F.; Gradie, Jonathan

1987-01-01

Broadband colorimetry, visual photometry, near-infrared photometry, and 10 and 20 micron radiometry of the near-earth asteroids (NEAs) 1986 DA and 1986 EB are used to show that these objects belong to the M class of asteroids. The similarity among the distributions of taxonomic classes among the 38 NEAs to the abundances found in the inner astoroid belt between the 3:1 and 5:2 resonances suggests that NEAs have their origins among asteroids in the vicinity of these resonances. The implied mineralogy of 1986 DA and 1986 EB is mostly nickel-iron metal; if this is indeed the case, then current models for meteorite production based on strength-related collisional processes on asteroidal surfaces predict that these two objects alone should produce about one percent of all meteorite falls. Iron meteorites derived from these near-earth asteroids should have low cosmic-ray exposure ages.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

Science.gov (United States)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Supervised linear dimensionality reduction with robust margins for object recognition

Science.gov (United States)

Dornaika, F.; Assoum, A.

2013-01-01

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.

The effect of subjective and objective social class on health-related quality of life: new paradigm using longitudinal analysis.

Science.gov (United States)

Choi, Young; Kim, Jae-Hyun; Park, Eun-Cheol

2015-08-08

To investigate the impact of the gap between subjective and objective social status on health-related quality of life. We analyzed data from 12,350 participants aged ≥ 18 years in the Korean Health Panel Survey. Health-related quality of life was measured by EuroQol-Visual analogue scale. Objective (income and education) and subjective social class (measured by MacArthur scale) was classified into three groups (High, Middle, Low). In terms of a gap between objective and subjective social class, social class was grouped into nine categories ranging from High-High to Low-Low. A linear mixed model was used to investigate the association between the combined social class and health-related quality of life. The impact of the gap between objective and subjective status on Health-related quality of life varied according to the type of gap. Namely, at any given subjective social class, an individual's quality of life declined with a decrease in the objective social class. At any given objective social class (e.g., HH, HM, HL; in terms of both education and income), an individual's quality of life declined with a one-level decrease in subjective social class. Our results suggest that studies of the relationship between social class and health outcomes may consider the multidimensional nature of social status.

A rat in the sewer: How mental imagery interacts with object recognition.

Science.gov (United States)

Karimpur, Harun; Hamburger, Kai

2018-01-01

The role of mental imagery has been puzzling researchers for more than two millennia. Both positive and negative effects of mental imagery on information processing have been discussed. The aim of this work was to examine how mental imagery affects object recognition and associative learning. Based on different perceptual and cognitive accounts we tested our imagery-induced interaction hypothesis in a series of two experiments. According to that, mental imagery could lead to (1) a superior performance in object recognition and associative learning if these objects are imagery-congruent (semantically) and to (2) an inferior performance if these objects are imagery-incongruent. In the first experiment, we used a static environment and tested associative learning. In the second experiment, subjects encoded object information in a dynamic environment by means of a virtual sewer system. Our results demonstrate that subjects who received a role adoption task (by means of guided mental imagery) performed better when imagery-congruent objects were used and worse when imagery-incongruent objects were used. We finally discuss our findings also with respect to alternative accounts and plead for a multi-methodological approach for future research in order to solve this issue.

A Neural Model Combining Attentional Orienting to Object Recognition: Preliminary Explorations on the Interplay Between Where and What

National Research Council Canada - National Science Library

Miau, Florence

2001-01-01

... ("where") pathway and an object recognition ("what") pathway. The fast visual attention front-end rapidly selects the few most conspicuous image locations, and the slower object recognition back-end identifies objects at the selected locations...

Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers

Energy Technology Data Exchange (ETDEWEB)

Koch, Mark William [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Steinbach, Ryan Matthew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Moya, Mary M [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-10-01

Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithms using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.

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....

Dentate gyrus supports slope recognition memory, shades of grey-context pattern separation and recognition memory, and CA3 supports pattern completion for object memory.

Science.gov (United States)

Kesner, Raymond P; Kirk, Ryan A; Yu, Zhenghui; Polansky, Caitlin; Musso, Nick D

2016-03-01

In order to examine the role of the dorsal dentate gyrus (dDG) in slope (vertical space) recognition and possible pattern separation, various slope (vertical space) degrees were used in a novel exploratory paradigm to measure novelty detection for changes in slope (vertical space) recognition memory and slope memory pattern separation in Experiment 1. The results of the experiment indicate that control rats displayed a slope recognition memory function with a pattern separation process for slope memory that is dependent upon the magnitude of change in slope between study and test phases. In contrast, the dDG lesioned rats displayed an impairment in slope recognition memory, though because there was no significant interaction between the two groups and slope memory, a reliable pattern separation impairment for slope could not be firmly established in the DG lesioned rats. In Experiment 2, in order to determine whether, the dDG plays a role in shades of grey spatial context recognition and possible pattern separation, shades of grey were used in a novel exploratory paradigm to measure novelty detection for changes in the shades of grey context environment. The results of the experiment indicate that control rats displayed a shades of grey-context pattern separation effect across levels of separation of context (shades of grey). In contrast, the DG lesioned rats displayed a significant interaction between the two groups and levels of shades of grey suggesting impairment in a pattern separation function for levels of shades of grey. In Experiment 3 in order to determine whether the dorsal CA3 (dCA3) plays a role in object pattern completion, a new task requiring less training and using a choice that was based on choosing the correct set of objects on a two-choice discrimination task was used. The results indicated that control rats displayed a pattern completion function based on the availability of one, two, three or four cues. In contrast, the dCA3 lesioned rats

Crowded and Sparse Domains in Object Recognition: Consequences for Categorization and Naming

Science.gov (United States)

Gale, Tim M.; Laws, Keith R.; Foley, Kerry

2006-01-01

Some models of object recognition propose that items from structurally crowded categories (e.g., living things) permit faster access to superordinate semantic information than structurally dissimilar categories (e.g., nonliving things), but slower access to individual object information when naming items. We present four experiments that utilize…

Visual object recognition and tracking

Science.gov (United States)

Chang, Chu-Yin (Inventor); English, James D. (Inventor); Tardella, Neil M. (Inventor)

2010-01-01

This invention describes a method for identifying and tracking an object from two-dimensional data pictorially representing said object by an object-tracking system through processing said two-dimensional data using at least one tracker-identifier belonging to the object-tracking system for providing an output signal containing: a) a type of the object, and/or b) a position or an orientation of the object in three-dimensions, and/or c) an articulation or a shape change of said object in said three dimensions.

Exploring social class differences at work

OpenAIRE

Evans, Samantha

2016-01-01

This paper is part of a wider project that investigates how organisational and individual factors within the workplace contribute to social class differences and inequality by examining the relative impact of objective and subjective indicators of social class on explicit (e.g. salary, promotions) and implicit (e.g. career satisfaction, quality of working life, stress and well-being) career and work outcomes. \\ud There is increasing recognition that social class differences play a crucial rol...

Visual recognition and tracking of objects for robot sensing

International Nuclear Information System (INIS)

Lowe, D.G.

1994-01-01

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

Face Recognition Is Affected by Similarity in Spatial Frequency Range to a Greater Degree Than Within-Category Object Recognition

Science.gov (United States)

Collin, Charles A.; Liu, Chang Hong; Troje, Nikolaus F.; McMullen, Patricia A.; Chaudhuri, Avi

2004-01-01

Previous studies have suggested that face identification is more sensitive to variations in spatial frequency content than object recognition, but none have compared how sensitive the 2 processes are to variations in spatial frequency overlap (SFO). The authors tested face and object matching accuracy under varying SFO conditions. Their results…

Biometric correspondence between reface computerized facial approximations and CT-derived ground truth skin surface models objectively examined using an automated facial recognition system.

Science.gov (United States)

Parks, Connie L; Monson, Keith L

2018-05-01

This study employed an automated facial recognition system as a means of objectively evaluating biometric correspondence between a ReFace facial approximation and the computed tomography (CT) derived ground truth skin surface of the same individual. High rates of biometric correspondence were observed, irrespective of rank class (R k ) or demographic cohort examined. Overall, 48% of the test subjects' ReFace approximation probes (n=96) were matched to his or her corresponding ground truth skin surface image at R 1 , a rank indicating a high degree of biometric correspondence and a potential positive identification. Identification rates improved with each successively broader rank class (R 10 =85%, R 25 =96%, and R 50 =99%), with 100% identification by R 57 . A sharp increase (39% mean increase) in identification rates was observed between R 1 and R 10 across most rank classes and demographic cohorts. In contrast, significantly lower (p0.05) performance differences were observed across demographic cohorts or CT scan protocols. Performance measures observed in this research suggest that ReFace approximations are biometrically similar to the actual faces of the approximated individuals and, therefore, may have potential operational utility in contexts in which computerized approximations are utilized as probes in automated facial recognition systems. Copyright © 2018. Published by Elsevier B.V.

A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

Science.gov (United States)

Vera, J. Fernando; Macias, Rodrigo; Heiser, Willem J.

2009-01-01

In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of…

Distinct roles of the hippocampus and perirhinal cortex in GABAA receptor blockade-induced enhancement of object recognition memory.

Science.gov (United States)

Kim, Jong Min; Kim, Dong Hyun; Lee, Younghwan; Park, Se Jin; Ryu, Jong Hoon

2014-03-13

It is well known that the hippocampus plays a role in spatial and contextual memory, and that spatial information is tightly regulated by the hippocampus. However, it is still highly controversial whether the hippocampus plays a role in object recognition memory. In a pilot study, the administration of bicuculline, a GABAA receptor antagonist, enhanced memory in the passive avoidance task, but not in the novel object recognition task. In the present study, we hypothesized that these different results are related to the characteristics of each task and the different roles of hippocampus and perirhinal cortex. A region-specific drug-treatment model was employed to clarify the role of the hippocampus and perirhinal cortex in object recognition memory. After a single habituation in the novel object recognition task, intra-perirhinal cortical injection of bicuculline increased and intra-hippocampal injection decreased the exploration time ratio to novel object. In addition, when animals were repeatedly habituated to the context, intra-perirhinal cortical administration of bicuculline still increased exploration time ratio to novel object, but the effect of intra-hippocampal administration disappeared. Concurrent increases of c-Fos expression and ERK phosphorylation were observed in the perirhinal cortex of the object with context-exposed group either after single or repeated habituation to the context, but no changes were noted in the hippocampus. Altogether, these results suggest that object recognition memory formation requires the perirhinal cortex but not the hippocampus, and that hippocampal activation interferes with object recognition memory by the information encoding of unfamiliar environment. Copyright © 2014 Elsevier B.V. All rights reserved.

The Effect of Training Data Selection on Face Recognition in Surveillance Application

Directory of Open Access Journals (Sweden)

Jamal Ahmad DARGHAM

2014-12-01

Full Text Available Face recognition is an important biometric method because of its potential applications in many fields, such as access control and surveillance. In surveillance applications, the distance between the subject and the camera is changing. Thus, in this paper, the effect of the distance between the subject and the camera, distance class, the effect of the number of images per class, and also the effect of session used to acquire the images have been investigated. Three sessions are used to acquire the images in the database. The images in each session were equally divided into three distance classes: CLOSE, MEDIUM, and FAR, according to the distance of the subject from the camera. It was found that using images from the MEDIUM class for training gives better performance than using either the FAR or the CLOSE class. In addition, it was also found that using one image from each class for training gives the same recognition performance as using three images from the MEDIUM class for training. It was also found that as the number of images per class increases, the recognition performance also increases. Lastly, it was found that by using one image per class from all the available database sessions gives the best recognition performance.

A TWO-STEP CLASSIFICATION APPROACH TO DISTINGUISHING SIMILAR OBJECTS IN MOBILE LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

H. He

2017-09-01

Full Text Available Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Image processing and recognition for biological images.

Science.gov (United States)

Uchida, Seiichi

2013-05-01

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

Anomaly detection in forward looking infrared imaging using one-class classifiers

Science.gov (United States)

Popescu, Mihail; Stone, Kevin; Havens, Timothy; Ho, Dominic; Keller, James

2010-04-01

In this paper we describe a method for generating cues of possible abnormal objects present in the field of view of an infrared (IR) camera installed on a moving vehicle. The proposed method has two steps. In the first step, for each frame, we generate a set of possible points of interest using a corner detection algorithm. In the second step, the points related to the background are discarded from the point set using an one class classifier (OCC) trained on features extracted from a local neighborhood of each point. The advantage of using an OCC is that we do not need examples from the "abnormal object" class to train the classifier. Instead, OCC is trained using corner points from images known to be abnormal object free, i.e., that contain only background scenes. To further reduce the number of false alarms we use a temporal fusion procedure: a region has to be detected as "interesting" in m out of n, mGM). The comparison is performed using a set of about 900 background point neighborhoods for training and 400 for testing. The best performing OCC is then used to detect abnormal objects in a set of IR video sequences obtained on a 1 mile long country road.

Image object recognition based on the Zernike moment and neural networks

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Wan, Jianwei; Wang, Ling; Huang, Fukan; Zhou, Liangzhu

1998-03-01

This paper first give a comprehensive discussion about the concept of artificial neural network its research methods and the relations with information processing. On the basis of such a discussion, we expound the mathematical similarity of artificial neural network and information processing. Then, the paper presents a new method of image recognition based on invariant features and neural network by using image Zernike transform. The method not only has the invariant properties for rotation, shift and scale of image object, but also has good fault tolerance and robustness. Meanwhile, it is also compared with statistical classifier and invariant moments recognition method.

Where vision meets memory: prefrontal-posterior networks for visual object constancy during categorization and recognition.

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Schendan, Haline E; Stern, Chantal E

2008-07-01

Objects seen from unusual relative to more canonical views require more time to categorize and recognize, and, according to object model verification theories, additionally recruit prefrontal processes for cognitive control that interact with parietal processes for mental rotation. To test this using functional magnetic resonance imaging, people categorized and recognized known objects from unusual and canonical views. Canonical views activated some components of a default network more on categorization than recognition. Activation to unusual views showed that both ventral and dorsal visual pathways, and prefrontal cortex, have key roles in visual object constancy. Unusual views activated object-sensitive and mental rotation (and not saccade) regions in ventrocaudal intraparietal, transverse occipital, and inferotemporal sulci, and ventral premotor cortex for verification processes of model testing on any task. A collateral-lingual sulci "place" area activated for mental rotation, working memory, and unusual views on correct recognition and categorization trials to accomplish detailed spatial matching. Ventrolateral prefrontal cortex and object-sensitive lateral occipital sulcus activated for mental rotation and unusual views on categorization more than recognition, supporting verification processes of model prediction. This visual knowledge framework integrates vision and memory theories to explain how distinct prefrontal-posterior networks enable meaningful interactions with objects in diverse situations.

Hierarchical Context Modeling for Video Event Recognition.

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Wang, Xiaoyang; Ji, Qiang

2016-10-11

Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.

Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration.

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Wang, Panqu; Gauthier, Isabel; Cottrell, Garrison

2016-04-01

Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al. [Gauthier, I., McGugin, R. W., Richler, J. J., Herzmann, G., Speegle, M., & VanGulick, A. E. Experience moderates overlap between object and face recognition, suggesting a common ability. Journal of Vision, 14, 7, 2014] recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing ["The Model", TM, Cottrell, G. W., & Hsiao, J. H. Neurocomputational models of face processing. In A. J. Calder, G. Rhodes, M. Johnson, & J. Haxby (Eds.), The Oxford handbook of face perception. Oxford, UK: Oxford University Press, 2011]. We model the domain general ability v as the available computational resources (number of hidden units) in the mapping from input to label and experience as the frequency of individual exemplars in an object category appearing during network training. Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces (separating them in representational space) that

[Recognition of visual objects under forward masking. Effects of cathegorial similarity of test and masking stimuli].

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Gerasimenko, N Iu; Slavutskaia, A V; Kalinin, S A; Kulikov, M A; Mikhaĭlova, E S

2013-01-01

In 38 healthy subjects accuracy and response time were examined during recognition of two categories of images--animals andnonliving objects--under forward masking. We revealed new data that masking effects depended of categorical similarity of target and masking stimuli. The recognition accuracy was the lowest and the response time was the most slow, when the target and masking stimuli belongs to the same category, that was combined with high dispersion of response times. The revealed effects were more clear in the task of animal recognition in comparison with the recognition of nonliving objects. We supposed that the revealed effects connected with interference between cortical representations of the target and masking stimuli and discussed our results in context of cortical interference and negative priming.

Recognition of abstract objects via neural oscillators: interaction among topological organization, associative memory and gamma band synchronization.

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Ursino, Mauro; Magosso, Elisa; Cuppini, Cristiano

2009-02-01

Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.

A comparison of feature detectors and descriptors for object class matching

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Hietanen, Antti; Lankinen, Jukka; Kämäräinen, Joni-Kristian

2016-01-01

appearance variation can be large. We extend the benchmarks to the class matching setting and evaluate state-of-the-art detectors and descriptors with Caltech and ImageNet classes. Our experiments provide important findings with regard to object class matching: (1) the original SIFT is still the best...

Field of attention for instantaneous object recognition.

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Jian-Gao Yao

Full Text Available BACKGROUND: Instantaneous object discrimination and categorization are fundamental cognitive capacities performed with the guidance of visual attention. Visual attention enables selection of a salient object within a limited area of the visual field; we referred to as "field of attention" (FA. Though there is some evidence concerning the spatial extent of object recognition, the following questions still remain unknown: (a how large is the FA for rapid object categorization, (b how accuracy of attention is distributed over the FA, and (c how fast complex objects can be categorized when presented against backgrounds formed by natural scenes. METHODOLOGY/PRINCIPAL FINDINGS: To answer these questions, we used a visual perceptual task in which subjects were asked to focus their attention on a point while being required to categorize briefly flashed (20 ms photographs of natural scenes by indicating whether or not these contained an animal. By measuring the accuracy of categorization at different eccentricities from the fixation point, we were able to determine the spatial extent and the distribution of accuracy over the FA, as well as the speed of categorizing objects using stimulus onset asynchrony (SOA. Our results revealed that subjects are able to rapidly categorize complex natural images within about 0.1 s without eye movement, and showed that the FA for instantaneous image categorization covers a visual field extending 20° × 24°, and accuracy was highest (>90% at the center of FA and declined with increasing eccentricity. CONCLUSIONS/SIGNIFICANCE: In conclusion, human beings are able to categorize complex natural images at a glance over a large extent of the visual field without eye movement.

3-D OBJECT RECOGNITION FROM POINT CLOUD DATA

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W. Smith

2012-09-01

Full Text Available The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs. Massively parallel processes such as graphics processing unit (GPU computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM and digital elevation model (DEM, so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex

3-D Object Recognition from Point Cloud Data

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Smith, W.; Walker, A. S.; Zhang, B.

2011-09-01

The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs). Massively parallel processes such as graphics processing unit (GPU) computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM) and digital elevation model (DEM), so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex roofs. Several case

Short-term plasticity of visuo-haptic object recognition

DEFF Research Database (Denmark)

Kassuba, Tanja; Klinge, Corinna; Hölig, Cordula

2014-01-01

, the same stimulation gave rise to relative increases in activation during S2 processing in the right LO, left FG, bilateral IPS, and other regions previously associated with object recognition. Critically, the modality of S2 determined which regions were recruited after rTMS. Relative to sham rTMS, real r......TMS induced increased activations during crossmodal congruent matching in the left FG for haptic S2 and the temporal pole for visual S2. In addition, we found stronger activations for incongruent than congruent matching in the right anterior parahippocampus and middle frontal gyrus for crossmodal matching......Functional magnetic resonance imaging (fMRI) studies have provided ample evidence for the involvement of the lateral occipital cortex (LO), fusiform gyrus (FG), and intraparietal sulcus (IPS) in visuo-haptic object integration. Here we applied 30 min of sham (non-effective) or real offline 1 Hz...

Fast and efficient indexing approach for object recognition

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Hefnawy, Alaa; Mashali, Samia A.; Rashwan, Mohsen; Fikri, Magdi

1999-08-01

This paper introduces a fast and efficient indexing approach for both 2D and 3D model-based object recognition in the presence of rotation, translation, and scale variations of objects. The indexing entries are computed after preprocessing the data by Haar wavelet decomposition. The scheme is based on a unified image feature detection approach based on Zernike moments. A set of low level features, e.g. high precision edges, gray level corners, are estimated by a set of orthogonal Zernike moments, calculated locally around every image point. A high dimensional, highly descriptive indexing entries are then calculated based on the correlation of these local features and employed for fast access to the model database to generate hypotheses. A list of the most candidate models is then presented by evaluating the hypotheses. Experimental results are included to demonstrate the effectiveness of the proposed indexing approach.

Flipping a Calculus Class: One Instructor's Experience

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Palmer, Katrina

2015-01-01

This paper describes one instructor's experiences during a year of flipping four calculus classes. The first exploration attempts to understand student expectations of a math class and their preference towards a flipped classroom. The second examines success of students from a flipped classroom, and the last investigates relationships with student…

Invariant recognition drives neural representations of action sequences.

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Andrea Tacchetti

2017-12-01

Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

Caffeine improves adult mice performance in the object recognition task and increases BDNF and TrkB independent on phospho-CREB immunocontent in the hippocampus.

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Costa, Marcelo S; Botton, Paulo H; Mioranzza, Sabrina; Ardais, Ana Paula; Moreira, Julia D; Souza, Diogo O; Porciúncula, Lisiane O

2008-09-01

Caffeine is one of the most psychostimulants consumed all over the world that usually presents positive effects on cognition. In this study, effects of caffeine on mice performance in the object recognition task were tested in different intertrial intervals. In addition, it was analyzed the effects of caffeine on brain derived neurotrophic factor (BDNF) and its receptor, TrkB, immunocontent to try to establish a connection between the behavioral finding and BDNF, one of the neurotrophins strictly involved in memory and learning process. CF1 mice were treated during 4 consecutive days with saline (0.9g%, i.p.) or caffeine (10mg/kg, i.p., equivalent dose corresponding to 2-3 cups of coffee). Caffeine treatment was interrupted 24h before the object recognition task analysis. In the test session performed 15min after training session, caffeine-treated mice recognized more efficiently both the familiar and the novel object. In the test session performed 90min and 24h after training session, caffeine did not change the time spent in the familiar object but increased the object recognition index, when compared to control group. Western blotting analysis of hippocampus from caffeine-treated mice revealed an increase in BDNF and TrkB immunocontent, compared to their saline-matched controls. Phospho-CREB immunocontent did not change with caffeine treatment. Our results suggest that acute treatment with caffeine improves recognition memory, and this effect may be related to an increase of the BDNF and TrkB immunocontent in the hippocampus.

The Role of Sensory-Motor Information in Object Recognition: Evidence from Category-Specific Visual Agnosia

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Wolk, D.A.; Coslett, H.B.; Glosser, G.

2005-01-01

The role of sensory-motor representations in object recognition was investigated in experiments involving AD, a patient with mild visual agnosia who was impaired in the recognition of visually presented living as compared to non-living entities. AD named visually presented items for which sensory-motor information was available significantly more…

Vision holds a greater share in visuo-haptic object recognition than touch

DEFF Research Database (Denmark)

Kassuba, Tanja; Klinge, Corinna; Hölig, Cordula

2013-01-01

approach of multisensory integration would predict that haptics as the less efficient sense for object recognition gains more from integrating additional visual information than vice versa. To test for asymmetries between vision and touch in visuo-haptic interactions, we measured regional changes in brain...... processed the target object, being more pronounced for haptic than visual targets. This preferential response of visuo-haptic regions indicates a modality-specific asymmetry in crossmodal matching of visual and haptic object features, suggesting a functional primacy of vision over touch in visuo...

Nicotine enhances the reconsolidation of novel object recognition memory in rats.

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Tian, Shaowen; Pan, Si; You, Yong

2015-02-01

There is increasing evidence that nicotine is involved in learning and memory. However, there are only few studies that have evaluated the relationship between nicotine and memory reconsolidation. In this study, we investigated the effects of nicotine on the reconsolidation of novel object recognition memory in rats. Behavior procedure involved four training phases: habituation (Days 1 and 2), sample (Day 3), reactivation (Day 4) and test (Day 6). Rats were injected with saline or nicotine (0.1, 0.2 and 0.4 mg/kg) immediately or 6h after reactivation. The discrimination index was used to assess memory performance and calculated as the difference in time exploring on the novel and familiar objects. Results showed that nicotine administration immediately but not 6 h after reactivation significantly enhanced memory performance of rats. Further results showed that the enhancing effect of nicotine on memory performance was dependent on memory reactivation, and was not attributed to the changes of the nonspecific responses (locomotor activity and anxiety level) 48 h after nicotine administration. The results suggest that post-reactivation nicotine administration enhances the reconsolidation of novel object recognition memory. Our present finding extends previous research on the nicotinic effects on learning and memory. Copyright © 2014 Elsevier Inc. All rights reserved.

Rapid eye movement sleep deprivation disrupts consolidation but not reconsolidation of novel object recognition memory in rats.

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Chen, Lin; Tian, Shaowen; Ke, Jie

2014-03-20

There is increasing evidence that sleep plays a critical role in memory consolidation. However, there are comparatively few studies that have assessed the relationship between sleep and memory reconsolidation. In the present study, we explored the effects of rapid eye movement sleep deprivation (RSD) on the consolidation (experiment 1) and reconsolidation (experiment 2) of novel object recognition memory in rats. In experiment 1 behavioral procedure involved two training phases: sample and test. Rats were subjected to 6h RSD starting either immediately after sample (exposed to 2 objects) or 6h later. In experiment 2 behavioral procedure involved three training phases: sample, reactivation and test. Rats were subjected to 6h RSD starting either immediately after reactivation (exposed to the same 2 sample objects to reactivate the memory trace) or 6h later. Results from experiment 1 showed that post-sample RSD from 0 to 6h but not 6 to 12h disrupted novel object recognition memory consolidation. However, we found that post-reactivation RSD whether from 0 to 6h or 6 to 12h had no effect on novel object recognition memory reconsolidation in experiment 2. The results indicated that RSD selectively disrupted consolidation of novel object recognition memory, suggesting a dissociation effect of RSD on consolidation and reconsolidation. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Why does brain damage impair memory? A connectionist model of object recognition memory in perirhinal cortex.

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Cowell, Rosemary A; Bussey, Timothy J; Saksida, Lisa M

2006-11-22

Object recognition is the canonical test of declarative memory, the type of memory putatively impaired after damage to the temporal lobes. Studies of object recognition memory have helped elucidate the anatomical structures involved in declarative memory, indicating a critical role for perirhinal cortex. We offer a mechanistic account of the effects of perirhinal cortex damage on object recognition memory, based on the assumption that perirhinal cortex stores representations of the conjunctions of visual features possessed by complex objects. Such representations are proposed to play an important role in memory when it is difficult to solve a task using representations of only individual visual features of stimuli, thought to be stored in regions of the ventral visual stream caudal to perirhinal cortex. The account is instantiated in a connectionist model, in which development of object representations with visual experience provides a mechanism for judgment of previous occurrence. We present simulations addressing the following empirical findings: (1) that impairments after damage to perirhinal cortex (modeled by removing the "perirhinal cortex" layer of the network) are exacerbated by lengthening the delay between presentation of to-be-remembered items and test, (2) that such impairments are also exacerbated by lengthening the list of to-be-remembered items, and (3) that impairments are revealed only when stimuli are trial unique rather than repeatedly presented. This study shows that it may be possible to account for object recognition impairments after damage to perirhinal cortex within a hierarchical, representational framework, in which complex conjunctive representations in perirhinal cortex play a critical role.

Face Memory and Object Recognition in Children with High-Functioning Autism or Asperger Syndrome and in Their Parents

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Kuusikko-Gauffin, Sanna; Jansson-Verkasalo, Eira; Carter, Alice; Pollock-Wurman, Rachel; Jussila, Katja; Mattila, Marja-Leena; Rahko, Jukka; Ebeling, Hanna; Pauls, David; Moilanen, Irma

2011-01-01

Children with Autism Spectrum Disorders (ASDs) have reported to have impairments in face, recognition and face memory, but intact object recognition and object memory. Potential abnormalities, in these fields at the family level of high-functioning children with ASD remains understudied despite, the ever-mounting evidence that ASDs are genetic and…

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

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Charles F Cadieu

2014-12-01

Full Text Available The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition. This remarkable performance is mediated by the representation formed in inferior temporal (IT cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs. It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

Deteksi Penyakit Dengue Hemorrhagic Fever dengan Pendekatan One Class Classification

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Zida Ziyan Azkiya

2017-10-01

Full Text Available Two class classification problem maps input into two target classes. In certain cases, training data is available only in the form of a single class, as in the case of Dengue Hemorrhagic Fever (DHF patients, where only data of positive patients is available. In this paper, we report our experiment in building a classification model for detecting DHF infection using One Class Classification (OCC approach. Data from this study is sourced from laboratory tests of patients with dengue fever. The OCC methods compared are One-Class Support Vector Machine and One-Class K-Means. The result shows SVM method obtained precision value = 1.0, recall = 0.993, f-1 score = 0.997, and accuracy of 99.7% while the K-Means method obtained precision value = 0.901, recall = 0.973, f- 1 score = 0.936, and accuracy of 93.3%. This indicates that the SVM method is slightly superior to K-Means for One-Class Classification of DHF patients.

Mice deficient for striatal Vesicular Acetylcholine Transporter (VAChT) display impaired short-term but normal long-term object recognition memory.

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Palmer, Daniel; Creighton, Samantha; Prado, Vania F; Prado, Marco A M; Choleris, Elena; Winters, Boyer D

2016-09-15

Substantial evidence implicates Acetylcholine (ACh) in the acquisition of object memories. While most research has focused on the role of the cholinergic basal forebrain and its cortical targets, there are additional cholinergic networks that may contribute to object recognition. The striatum contains an independent cholinergic network comprised of interneurons. In the current study, we investigated the role of this cholinergic signalling in object recognition using mice deficient for Vesicular Acetylcholine Transporter (VAChT) within interneurons of the striatum. We tested whether these striatal VAChT(D2-Cre-flox/flox) mice would display normal short-term (5 or 15min retention delay) and long-term (3h retention delay) object recognition memory. In a home cage object recognition task, male and female VAChT(D2-Cre-flox/flox) mice were impaired selectively with a 15min retention delay. When tested on an object location task, VAChT(D2-Cre-flox/flox) mice displayed intact spatial memory. Finally, when object recognition was tested in a Y-shaped apparatus, designed to minimize the influence of spatial and contextual cues, only females displayed impaired recognition with a 5min retention delay, but when males were challenged with a 15min retention delay, they were also impaired; neither males nor females were impaired with the 3h delay. The pattern of results suggests that striatal cholinergic transmission plays a role in the short-term memory for object features, but not spatial location. Copyright © 2016 Elsevier B.V. All rights reserved.

The Role of Fixation and Visual Attention in Object Recognition.

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1995-01-01

computers", Technical Report, Aritificial Intelligence Lab, M.I. T., AI-Memo-915, June 1986. [29] D.P. Huttenlocher and S.Ullman, "Object Recognition Using...attention", Technical Report, Aritificial Intelligence Lab, M.I. T., AI-memo-770, Jan 1984. [35] E.Krotkov, K. Henriksen and R. Kories, "Stereo...MIT Artificial Intelligence Laboratory [ PCTBTBimON STATEMENT X \\ Afipioved tor puciic reieo*«* \\ »?*•;.., jDi*tiibutK» U»lisut»d* 19951004

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report.

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Poth, Christian H; Schneider, Werner X

2016-01-01

Human vision is organized in discrete processing episodes (e.g., eye fixations or task-steps). Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM), which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of 10 letters and reported as many as possible after a retention interval (whole report). Next, participants viewed a probe letter and indicated whether it had been one of the 10 letters (probe recognition). In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters) compared with non-encoded letters (non-reported letters). Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2, participants reported only one of 10 letters (partial report) and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM.

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report

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Christian H. Poth

2016-09-01

Full Text Available Human vision is organized in discrete processing episodes (e.g. eye fixations or task-steps. Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM, which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of ten letters and reported as many as possible after a retention interval (whole report. Next, participants viewed a probe letter and indicated whether it had been one of the ten letters (probe recognition. In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters compared with non-encoded letters (non-reported letters. Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2 participants reported only one of ten letters (partial report and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM.

Modelling of anisotropic compact stars of embedding class one

Energy Technology Data Exchange (ETDEWEB)

Bhar, Piyali [Government General Degree College, Department of Mathematics, Singur, Hooghly, West Bengal (India); Maurya, S.K. [University of Nizwa, Department of Mathematical and Physical Sciences, College of Arts and Science, Nizwa (Oman); Gupta, Y.K. [Raj Kumar Goel Institute of Technology, Department of Mathematics, Ghaziabad, U.P. (India); Manna, Tuhina [St. Xavier' s College, Department of Commerce (Evening), Kolkata, West Bengal (India)

2016-10-15

In the present article, we have constructed static anisotropic compact star models of Einstein field equations for the spherical symmetric metric of embedding class one. By assuming the particular form of the metric function ν, we have solved the Einstein field equations for anisotropic matter distribution. The anisotropic models represent the realistic compact objects such as SAX J 1808.4-3658 (SS1), Her X-1, Vela X-12, PSR J1614-2230 and Cen X-3. We have reported our results in details for the compact star Her X-1 on the ground of physical properties such as pressure, density, velocity of sound, energy conditions, TOV equation and red-shift etc. Along with these, we have also discussed about the stability of the compact star models. Finally we made a comparison between our anisotropic stars with the realistic objects on the key aspects as central density, central pressure, compactness and surface red-shift. (orig.)

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Science.gov (United States)

Hauffen, Karin; Bart, Eugene; Brady, Mark; Kersten, Daniel; Hegdé, Jay

2012-01-01

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2. Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings. First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints. Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases. Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms. Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created

Bayesian feature weighting for unsupervised learning, with application to object recognition

OpenAIRE

Carbonetto , Peter; De Freitas , Nando; Gustafson , Paul; Thompson , Natalie

2003-01-01

International audience; We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model parameters and cluster assignments can be computed simultaneous using an efficient EM algorithm. Applying our Bayesian shrinkage model to a complex problem in object recognition (Duygulu, Barnard, de Freitas and Forsyth 2002), our experiments yied good results.

Extracting UML Class Diagrams from Object-Oriented Fortran: ForUML

Directory of Open Access Journals (Sweden)

Aziz Nanthaamornphong

2015-01-01

Full Text Available Many scientists who implement computational science and engineering software have adopted the object-oriented (OO Fortran paradigm. One of the challenges faced by OO Fortran developers is the inability to obtain high level software design descriptions of existing applications. Knowledge of the overall software design is not only valuable in the absence of documentation, it can also serve to assist developers with accomplishing different tasks during the software development process, especially maintenance and refactoring. The software engineering community commonly uses reverse engineering techniques to deal with this challenge. A number of reverse engineering-based tools have been proposed, but few of them can be applied to OO Fortran applications. In this paper, we propose a software tool to extract unified modeling language (UML class diagrams from Fortran code. The UML class diagram facilitates the developers' ability to examine the entities and their relationships in the software system. The extracted diagrams enhance software maintenance and evolution. The experiments carried out to evaluate the proposed tool show its accuracy and a few of the limitations.

Marked Object Recognition Multitouch Screen Printed Touchpad for Interactive Applications.

Science.gov (United States)

Nunes, Jivago Serrado; Castro, Nelson; Gonçalves, Sergio; Pereira, Nélson; Correia, Vitor; Lanceros-Mendez, Senentxu

2017-12-01

The market for interactive platforms is rapidly growing, and touchscreens have been incorporated in an increasing number of devices. Thus, the area of smart objects and devices is strongly increasing by adding interactive touch and multimedia content, leading to new uses and capabilities. In this work, a flexible screen printed sensor matrix is fabricated based on silver ink in a polyethylene terephthalate (PET) substrate. Diamond shaped capacitive electrodes coupled with conventional capacitive reading electronics enables fabrication of a highly functional capacitive touchpad, and also allows for the identification of marked objects. For the latter, the capacitive signatures are identified by intersecting points and distances between them. Thus, this work demonstrates the applicability of a low cost method using royalty-free geometries and technologies for the development of flexible multitouch touchpads for the implementation of interactive and object recognition applications.

Marked Object Recognition Multitouch Screen Printed Touchpad for Interactive Applications

Directory of Open Access Journals (Sweden)

Jivago Serrado Nunes

2017-12-01

Full Text Available The market for interactive platforms is rapidly growing, and touchscreens have been incorporated in an increasing number of devices. Thus, the area of smart objects and devices is strongly increasing by adding interactive touch and multimedia content, leading to new uses and capabilities. In this work, a flexible screen printed sensor matrix is fabricated based on silver ink in a polyethylene terephthalate (PET substrate. Diamond shaped capacitive electrodes coupled with conventional capacitive reading electronics enables fabrication of a highly functional capacitive touchpad, and also allows for the identification of marked objects. For the latter, the capacitive signatures are identified by intersecting points and distances between them. Thus, this work demonstrates the applicability of a low cost method using royalty-free geometries and technologies for the development of flexible multitouch touchpads for the implementation of interactive and object recognition applications.

The active blind spot camera: hard real-time recognition of moving objects from a moving camera

OpenAIRE

Van Beeck, Kristof; Goedemé, Toon; Tuytelaars, Tinne

2014-01-01

This PhD research focuses on visual object recognition under specific demanding conditions. The object to be recognized as well as the camera move, and the time available for the recognition task is extremely short. This generic problem is applied here on a specific problem: the active blind spot camera. Statistics show a large number of accidents with trucks are related to the so-called blind spot, the area around the vehicle in which vulnerable road users are hard to perceive by the truck d...

Invariant object recognition based on the generalized discrete radon transform

Science.gov (United States)

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.

Dependent Classes

DEFF Research Database (Denmark)

Gasiunas, Vaidas; Mezini, Mira; Ostermann, Klaus

2007-01-01

of dependent classes and a machine-checked type soundness proof in Isabelle/HOL [29], the first of this kind for a language with virtual classes and path-dependent types. [29] T.Nipkow, L.C. Poulson, and M. Wenzel. Isabelle/HOL -- A Proof Assistant for Higher-Order Logic, volume 2283 of LNCS, Springer, 2002......Virtual classes allow nested classes to be refined in subclasses. In this way nested classes can be seen as dependent abstractions of the objects of the enclosing classes. Expressing dependency via nesting, however, has two limitations: Abstractions that depend on more than one object cannot...... be modeled and a class must know all classes that depend on its objects. This paper presents dependent classes, a generalization of virtual classes that expresses similar semantics by parameterization rather than by nesting. This increases expressivity of class variations as well as the flexibility...

Classifying objects in LWIR imagery via CNNs

Science.gov (United States)

Rodger, Iain; Connor, Barry; Robertson, Neil M.

2016-10-01

The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

Glucocorticoid effects on object recognition memory require training-associated emotional arousal

OpenAIRE

Okuda, Shoki; Roozendaal, Benno; McGaugh, James L.

2004-01-01

Considerable evidence implicates glucocorticoid hormones in the regulation of memory consolidation and memory retrieval. The present experiments investigated whether the influence of these hormones on memory depends on the level of emotional arousal induced by the training experience. We investigated this issue in male Sprague–Dawley rats by examining the effects of immediate posttraining systemic injections of the glucocorticoid corticosterone on object recognition memory under two condition...

Visual recognition of age class and preference for infantile features: implications for species-specific vs universal cognitive traits in primates.

Directory of Open Access Journals (Sweden)

Anna Sato

Full Text Available Despite not knowing the exact age of individuals, humans can estimate their rough age using age-related physical features. Nonhuman primates show some age-related physical features; however, the cognitive traits underlying their recognition of age class have not been revealed. Here, we tested the ability of two species of Old World monkey, Japanese macaques (JM and Campbell's monkeys (CM, to spontaneously discriminate age classes using visual paired comparison (VPC tasks based on the two distinct categories of infant and adult images. First, VPCs were conducted in JM subjects using conspecific JM stimuli. When analyzing the side of the first look, JM subjects significantly looked more often at novel images. Based on analyses of total looking durations, JM subjects looked at a novel infant image longer than they looked at a familiar adult image, suggesting the ability to spontaneously discriminate between the two age classes and a preference for infant over adult images. Next, VPCs were tested in CM subjects using heterospecific JM stimuli. CM subjects showed no difference in the side of their first look, but looked at infant JM images longer than they looked at adult images; the fact that CMs were totally naïve to JMs suggested that the attractiveness of infant images transcends species differences. This is the first report of visual age class recognition and a preference for infant over adult images in nonhuman primates. Our results suggest not only species-specific processing for age class recognition but also the evolutionary origins of the instinctive human perception of baby cuteness schema, proposed by the ethologist Konrad Lorenz.

Intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory.

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Takeda, Atsushi; Tamano, Haruna; Ogawa, Taisuke; Takada, Shunsuke; Nakamura, Masatoshi; Fujii, Hiroaki; Ando, Masaki

2014-11-01

The role of perforant pathway-dentate granule cell synapses in cognitive behavior was examined focusing on synaptic Zn(2+) signaling in the dentate gyrus. Object recognition memory was transiently impaired when extracellular Zn(2+) levels were decreased by injection of clioquinol and N,N,N',N'-tetrakis-(2-pyridylmethyl) ethylendediamine. To pursue the effect of the loss and/or blockade of Zn(2+) signaling in dentate granule cells, ZnAF-2DA (100 pmol, 0.1 mM/1 µl), an intracellular Zn(2+) chelator, was locally injected into the dentate molecular layer of rats. ZnAF-2DA injection, which was estimated to chelate intracellular Zn(2+) signaling only in the dentate gyrus, affected object recognition memory 1 h after training without affecting intracellular Ca(2+) signaling in the dentate molecular layer. In vivo dentate gyrus long-term potentiation (LTP) was affected under the local perfusion of the recording region (the dentate granule cell layer) with 0.1 mM ZnAF-2DA, but not with 1-10 mM CaEDTA, an extracellular Zn(2+) chelator, suggesting that the blockade of intracellular Zn(2+) signaling in dentate granule cells affects dentate gyrus LTP. The present study demonstrates that intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory, probably via dentate gyrus LTP expression. Copyright © 2014 Wiley Periodicals, Inc.

Chronic prenatal caffeine exposure impairs novel object recognition and radial arm maze behaviors in adult rats.

Science.gov (United States)

Soellner, Deborah E; Grandys, Theresa; Nuñez, Joseph L

2009-12-14

In this report, we demonstrate that chronic prenatal exposure to a moderate dose of caffeine disrupts novel object recognition and radial arm maze behaviors in adult male and female rats. Pregnant dams were administered either tap water or 75 mg/L caffeinated tap water throughout gestation. Oral self-administration in the drinking water led to an approximate maternal intake of 10mg/kg/day, equivalent to 2-3 cups of coffee/day in humans based on a metabolic body weight conversion. In adulthood, the offspring underwent testing on novel object recognition, radial arm maze, and Morris water maze tasks. Prenatal caffeine exposure was found to impair 24-h memory retention in the novel object recognition task and impair both working and reference memory in the radial arm maze. However, prenatal caffeine exposure did not alter Morris water maze performance in either a simple water maze procedure or in an advanced water maze procedure that included reversal and working memory paradigms. These findings demonstrate that chronic oral intake of caffeine throughout gestation can alter adult cognitive behaviors in rats.

Biased figure-ground assignment affects conscious object recognition in spatial neglect.

Science.gov (United States)

Eramudugolla, Ranmalee; Driver, Jon; Mattingley, Jason B

2010-09-01

Unilateral spatial neglect is a disorder of attention and spatial representation, in which early visual processes such as figure-ground segmentation have been assumed to be largely intact. There is evidence, however, that the spatial attention bias underlying neglect can bias the segmentation of a figural region from its background. Relatively few studies have explicitly examined the effect of spatial neglect on processing the figures that result from such scene segmentation. Here, we show that a neglect patient's bias in figure-ground segmentation directly influences his conscious recognition of these figures. By varying the relative salience of figural and background regions in static, two-dimensional displays, we show that competition between elements in such displays can modulate a neglect patient's ability to recognise parsed figures in a scene. The findings provide insight into the interaction between scene segmentation, explicit object recognition, and attention.

Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.

Science.gov (United States)

Kheradpisheh, Saeed R; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

2016-01-01

View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call "variation level." We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

Humans and deep networks largely agree on which kinds of variation make object recognition harder

Directory of Open Access Journals (Sweden)

Saeed Reza Kheradpisheh

2016-08-01

Full Text Available View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g. 3D rotations. Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN, which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth as well as their magnitude, which we call variation level. We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier. This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

Object detection and recognition in digital images theory and practice

CERN Document Server

Cyganek, Boguslaw

2013-01-01

Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.

Remembering the object you fear: brain potentials during recognition of spiders in spider-fearful individuals.

Science.gov (United States)

Michalowski, Jaroslaw M; Weymar, Mathias; Hamm, Alfons O

2014-01-01

In the present study we investigated long-term memory for unpleasant, neutral and spider pictures in 15 spider-fearful and 15 non-fearful control individuals using behavioral and electrophysiological measures. During the initial (incidental) encoding, pictures were passively viewed in three separate blocks and were subsequently rated for valence and arousal. A recognition memory task was performed one week later in which old and new unpleasant, neutral and spider pictures were presented. Replicating previous results, we found enhanced memory performance and higher confidence ratings for unpleasant when compared to neutral materials in both animal fearful individuals and controls. When compared to controls high animal fearful individuals also showed a tendency towards better memory accuracy and significantly higher confidence during recognition of spider pictures, suggesting that memory of objects prompting specific fear is also facilitated in fearful individuals. In line, spider-fearful but not control participants responded with larger ERP positivity for correctly recognized old when compared to correctly rejected new spider pictures, thus showing the same effects in the neural signature of emotional memory for feared objects that were already discovered for other emotional materials. The increased fear memory for phobic materials observed in the present study in spider-fearful individuals might result in an enhanced fear response and reinforce negative beliefs aggravating anxiety symptomatology and hindering recovery.

Short- and long-term effects of nicotine and the histone deacetylase inhibitor phenylbutyrate on novel object recognition in zebrafish.

Science.gov (United States)

Faillace, M P; Pisera-Fuster, A; Medrano, M P; Bejarano, A C; Bernabeu, R O

2017-03-01

Zebrafish have a sophisticated color- and shape-sensitive visual system, so we examined color cue-based novel object recognition in zebrafish. We evaluated preference in the absence or presence of drugs that affect attention and memory retention in rodents: nicotine and the histone deacetylase inhibitor (HDACi) phenylbutyrate (PhB). The objective of this study was to evaluate whether nicotine and PhB affect innate preferences of zebrafish for familiar and novel objects after short- and long-retention intervals. We developed modified object recognition (OR) tasks using neutral novel and familiar objects in different colors. We also tested objects which differed with respect to the exploratory behavior they elicited from naïve zebrafish. Zebrafish showed an innate preference for exploring red or green objects rather than yellow or blue objects. Zebrafish were better at discriminating color changes than changes in object shape or size. Nicotine significantly enhanced or changed short-term innate novel object preference whereas PhB had similar effects when preference was assessed 24 h after training. Analysis of other zebrafish behaviors corroborated these results. Zebrafish were innately reluctant or prone to explore colored novel objects, so drug effects on innate preference for objects can be evaluated changing the color of objects with a simple geometry. Zebrafish exhibited recognition memory for novel objects with similar innate significance. Interestingly, nicotine and PhB significantly modified innate object preference.

Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

2017-01-01

of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show...

Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids.

Science.gov (United States)

Raicar, Gaurav; Saini, Harsh; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

2016-08-07

Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

Brain dynamics of upstream perceptual processes leading to visual object recognition: a high density ERP topographic mapping study.

Science.gov (United States)

Schettino, Antonio; Loeys, Tom; Delplanque, Sylvain; Pourtois, Gilles

2011-04-01

Recent studies suggest that visual object recognition is a proactive process through which perceptual evidence accumulates over time before a decision can be made about the object. However, the exact electrophysiological correlates and time-course of this complex process remain unclear. In addition, the potential influence of emotion on this process has not been investigated yet. We recorded high density EEG in healthy adult participants performing a novel perceptual recognition task. For each trial, an initial blurred visual scene was first shown, before the actual content of the stimulus was gradually revealed by progressively adding diagnostic high spatial frequency information. Participants were asked to stop this stimulus sequence as soon as they could correctly perform an animacy judgment task. Behavioral results showed that participants reliably gathered perceptual evidence before recognition. Furthermore, prolonged exploration times were observed for pleasant, relative to either neutral or unpleasant scenes. ERP results showed distinct effects starting at 280 ms post-stimulus onset in distant brain regions during stimulus processing, mainly characterized by: (i) a monotonic accumulation of evidence, involving regions of the posterior cingulate cortex/parahippocampal gyrus, and (ii) true categorical recognition effects in medial frontal regions, including the dorsal anterior cingulate cortex. These findings provide evidence for the early involvement, following stimulus onset, of non-overlapping brain networks during proactive processes eventually leading to visual object recognition. Copyright © 2011 Elsevier Inc. All rights reserved.

Differential Roles for "Nr4a1" and "Nr4a2" in Object Location vs. Object Recognition Long-Term Memory

Science.gov (United States)

McNulty, Susan E.; Barrett, Ruth M.; Vogel-Ciernia, Annie; Malvaez, Melissa; Hernandez, Nicole; Davatolhagh, M. Felicia; Matheos, Dina P.; Schiffman, Aaron; Wood, Marcelo A.

2012-01-01

"Nr4a1" and "Nr4a2" are transcription factors and immediate early genes belonging to the nuclear receptor Nr4a family. In this study, we examine their role in long-term memory formation for object location and object recognition. Using siRNA to block expression of either "Nr4a1" or "Nr4a2", we found that "Nr4a2" is necessary for both long-term…

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

Science.gov (United States)

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…

Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

Science.gov (United States)

Spirkovska, Lilly; Reid, Max B.

1993-01-01

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

The medial prefrontal cortex-lateral entorhinal cortex circuit is essential for episodic-like memory and associative object-recognition.

Science.gov (United States)

Chao, Owen Y; Huston, Joseph P; Li, Jay-Shake; Wang, An-Li; de Souza Silva, Maria A

2016-05-01

The prefrontal cortex directly projects to the lateral entorhinal cortex (LEC), an important substrate for engaging item-associated information and relaying the information to the hippocampus. Here we ask to what extent the communication between the prefrontal cortex and LEC is critically involved in the processing of episodic-like memory. We applied a disconnection procedure to test whether the interaction between the medial prefrontal cortex (mPFC) and LEC is essential for the expression of recognition memory. It was found that male rats that received unilateral NMDA lesions of the mPFC and LEC in the same hemisphere, exhibited intact episodic-like (what-where-when) and object-recognition memories. When these lesions were placed in the opposite hemispheres (disconnection), episodic-like and associative memories for object identity, location and context were impaired. However, the disconnection did not impair the components of episodic memory, namely memory for novel object (what), object place (where) and temporal order (when), per se. Thus, the present findings suggest that the mPFC and LEC are a critical part of a neural circuit that underlies episodic-like and associative object-recognition memory. © 2015 Wiley Periodicals, Inc.

DATA Act File B Object Class and Program Activity - Social Security

Data.gov (United States)

Social Security Administration — The DATA Act Information Model Schema Reporting Submission Specification File B. File B includes the agency object class and program activity detail obligation and...

Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing.

Science.gov (United States)

Chica, Manuel

2012-11-01

A novel method for authenticating pollen grains in bright-field microscopic images is presented in this work. The usage of this new method is clear in many application fields such as bee-keeping sector, where laboratory experts need to identify fraudulent bee pollen samples against local known pollen types. Our system is based on image processing and one-class classification to reject unknown pollen grain objects. The latter classification technique allows us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types, and the impossibility of modeling all of them. Different one-class classification paradigms are compared to study the most suitable technique for solving the problem. In addition, feature selection algorithms are applied to reduce the complexity and increase the accuracy of the models. For each local pollen type, a one-class classifier is trained and aggregated into a multiclassifier model. This multiclassification scheme combines the output of all the one-class classifiers in a unique final response. The proposed method is validated by authenticating pollen grains belonging to different Spanish bee pollen types. The overall accuracy of the system on classifying fraudulent microscopic pollen grain objects is 92.3%. The system is able to rapidly reject pollen grains, which belong to nonlocal pollen types, reducing the laboratory work and effort. The number of possible applications of this authentication method in the microscopy research field is unlimited. Copyright © 2012 Wiley Periodicals, Inc.

The use of the Emotional-Object Recognition as an assay to assess learning and memory associated to an aversive stimulus in rodents.

Science.gov (United States)

Brancato, Anna; Lavanco, Gianluca; Cavallaro, Angela; Plescia, Fulvio; Cannizzaro, Carla

2016-12-01

Emotionally salient experiences induce the formation of explicit memory traces, besides eliciting automatic or implicit emotional memory in rodents. This study aims at investigating the implementation of a novel task for studying the formation of limbic memory engrams as a result of the acquisition- and retrieval- of fear-conditioning - biased declarative memory traces, measured by animal discrimination of an "emotional-object". Moreover, by using this new method we investigated the potential interactions between stimulation of cannabinoid transmission and integration of emotional information and cognitive functioning. The Emotional-Object Recognition task is composed of 3 following sessions: habituation; cued fear-conditioned learning; emotional recognition. Rats are exposed to Context "B chamber" for habituation and cued fear-conditioning, and tested in Context "A chamber" for emotional-object recognition. Cued fear-conditioning induces a reduction in emotional-object exploration time during the Emotional-Object Recognition task in controls. The activation of cannabinoid signalling impairs limbic memory formation, with respect to vehicle. The Emotional-Object Recognition test overcomes several limitations of commonly employed methods that explore declarative-, spatial memory and fear-conditioning in a non-integrated manner. It allows the assessment of unbiased cognitive indicators of emotional learning and memory. The Emotional-Object Recognition task is a valuable tool for investigating whether, and at what extent, specific drugs or pathological conditions that interfere with the individual affective/emotional homeostasis, can modulate the formation of emotionally salient explicit memory traces, thus jeopardizing control and regulation of animal behavioural strategy. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic radar target recognition of objects falling on railway tracks

International Nuclear Information System (INIS)

Mroué, A; Heddebaut, M; Elbahhar, F; Rivenq, A; Rouvaen, J-M

2012-01-01

This paper presents an automatic radar target recognition procedure based on complex resonances using the signals provided by ultra-wideband radar. This procedure is dedicated to detection and identification of objects lying on railway tracks. For an efficient complex resonance extraction, a comparison between several pole extraction methods is illustrated. Therefore, preprocessing methods are presented aiming to remove most of the erroneous poles interfering with the discrimination scheme. Once physical poles are determined, a specific discrimination technique is introduced based on the Euclidean distances. Both simulation and experimental results are depicted showing an efficient discrimination of different targets including guided transport passengers

Object location and object recognition memory impairments, motivation deficits and depression in a model of Gulf War illness.

Science.gov (United States)

Hattiangady, Bharathi; Mishra, Vikas; Kodali, Maheedhar; Shuai, Bing; Rao, Xiolan; Shetty, Ashok K

2014-01-01

Memory and mood deficits are the enduring brain-related symptoms in Gulf War illness (GWI). Both animal model and epidemiological investigations have indicated that these impairments in a majority of GW veterans are linked to exposures to chemicals such as pyridostigmine bromide (PB, an antinerve gas drug), permethrin (PM, an insecticide) and DEET (a mosquito repellant) encountered during the Persian Gulf War-1. Our previous study in a rat model has shown that combined exposures to low doses of GWI-related (GWIR) chemicals PB, PM, and DEET with or without 5-min of restraint stress (a mild stress paradigm) causes hippocampus-dependent spatial memory dysfunction in a water maze test (WMT) and increased depressive-like behavior in a forced swim test (FST). In this study, using a larger cohort of rats exposed to GWIR-chemicals and stress, we investigated whether the memory deficiency identified earlier in a WMT is reproducible with an alternative and stress free hippocampus-dependent memory test such as the object location test (OLT). We also ascertained the possible co-existence of hippocampus-independent memory dysfunction using a novel object recognition test (NORT), and alterations in mood function with additional tests for motivation and depression. Our results provide new evidence that exposure to low doses of GWIR-chemicals and mild stress for 4 weeks causes deficits in hippocampus-dependent object location memory and perirhinal cortex-dependent novel object recognition memory. An open field test performed prior to other behavioral analyses revealed that memory impairments were not associated with increased anxiety or deficits in general motor ability. However, behavioral tests for mood function such as a voluntary physical exercise paradigm and a novelty suppressed feeding test (NSFT) demonstrated decreased motivation levels and depression. Thus, exposure to GWIR-chemicals and stress causes both hippocampus-dependent and hippocampus-independent memory

Blockade of intracellular Zn2+ signaling in the basolateral amygdala affects object recognition memory via attenuation of dentate gyrus LTP.

Science.gov (United States)

Fujise, Yuki; Kubota, Mitsuyasu; Suzuki, Miki; Tamano, Haruna; Takeda, Atsushi

2017-09-01

Hippocampus-dependent memory is modulated by the amygdala. However, it is unknown whether intracellular Zn 2+ signaling in the amygdala is involved in hippocampus-dependent memory. On the basis of the evidence that intracellular Zn 2+ signaling in dentate granule cells (DGC) is necessary for object recognition memory via LTP at medial perforant pathway (PP)-DGC synapses, the present study examined whether intracellular Zn 2+ signaling in the amygdala influences object recognition memory via modulation of LTP at medial PP-DGC synapses. When ZnAF-2DA (100 μM, 2 μl) was injected into the basolateral amygdala (BLA), intracellular ZnAF-2 locally chelated intracellular Zn 2+ in the amygdala. Recognition memory was affected when training of object recognition test was performed 20 min after ZnAF-2DA injection into the BLA. Twenty minutes after injection of ZnAF-2DA into the BLA, LTP induction at medial PP-DGC synapses was attenuated, while LTP induction at PP-BLA synapses was potentiated and LTP induction at BLA-DGC synapses was attenuated. These results suggest that intracellular Zn 2+ signaling in the BLA is involved in BLA-associated LTP and modulates LTP at medial PP-DGC synapses, followed by modulation of object recognition memory. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cross-sensor iris recognition through kernel learning.

Science.gov (United States)

Pillai, Jaishanker K; Puertas, Maria; Chellappa, Rama

2014-01-01

Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.

Visualization of the ROOT 3D class objects with openInventor-like viewers

CERN Document Server

Fine, V; Kulikova, A; Panebrattsev, M

2004-01-01

The class library for conversion of the ROOT 3D class objects to the iv format for 3D image viewers is described in this paper. At present the library was tested using the STAR and ATLAS detector geometry without any changes and revision for concrete detector.

Chronic methylphenidate-effects over circadian cycle of young and adult rats submitted to open-field and object recognition tests.

Science.gov (United States)

Gomes, Karin M; Souza, Renan P; Valvassori, Samira S; Réus, Gislaine Z; Inácio, Cecília G; Martins, Márcio R; Comim, Clarissa M; Quevedo, João

2009-11-01

In this study age-, circadian rhythm- and methylphenidate administration- effect on open field habituation and object recognition were analyzed. Young and adult male Wistar rats were treated with saline or methylphenidate 2.0 mg/kg for 28 days. Experiments were performed during the light and the dark cycle. Locomotor activity was significantly altered by circadian cycle and methylphenidate treatment during the training session and by drug treatment during the testing session. Exploratory activity was significantly modulated by age during the training session and by age and drug treatment during the testing session. Object recognition memory was altered by cycle at the training session; by age 1.5 h later and by cycle and age 24 h after the training session. These results show that methylphenidate treatment was the major modulator factor on open-field test while cycle and age had an important effect on object recognition experiment.

Object instance recognition using motion cues and instance specific appearance models

Science.gov (United States)

Schumann, Arne

2014-03-01

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

Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

Science.gov (United States)

Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-01-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

Visual language recognition with a feed-forward network of spiking neurons

Energy Technology Data Exchange (ETDEWEB)

Rasmussen, Craig E [Los Alamos National Laboratory; Garrett, Kenyan [Los Alamos National Laboratory; Sottile, Matthew [GALOIS; Shreyas, Ns [INDIANA UNIV.

2010-01-01

An analogy is made and exploited between the recognition of visual objects and language parsing. A subset of regular languages is used to define a one-dimensional 'visual' language, in which the words are translational and scale invariant. This allows an exploration of the viewpoint invariant languages that can be solved by a network of concurrent, hierarchically connected processors. A language family is defined that is hierarchically tiling system recognizable (HREC). As inspired by nature, an algorithm is presented that constructs a cellular automaton that recognizes strings from a language in the HREC family. It is demonstrated how a language recognizer can be implemented from the cellular automaton using a feed-forward network of spiking neurons. This parser recognizes fixed-length strings from the language in parallel and as the computation is pipelined, a new string can be parsed in each new interval of time. The analogy with formal language theory allows inferences to be drawn regarding what class of objects can be recognized by visual cortex operating in purely feed-forward fashion and what class of objects requires a more complicated network architecture.

The Initial Development of Object Knowledge by a Learning Robot.

Science.gov (United States)

Modayil, Joseph; Kuipers, Benjamin

2008-11-30

We describe how a robot can develop knowledge of the objects in its environment directly from unsupervised sensorimotor experience. The object knowledge consists of multiple integrated representations: trackers that form spatio-temporal clusters of sensory experience, percepts that represent properties for the tracked objects, classes that support efficient generalization from past experience, and actions that reliably change object percepts. We evaluate how well this intrinsically acquired object knowledge can be used to solve externally specified tasks including object recognition and achieving goals that require both planning and continuous control.

A new exact anisotropic solution of embedding class one

Energy Technology Data Exchange (ETDEWEB)

Maurya, S.K.; Smitha, T.T. [University of Nizwa, Department of Mathematical and Physical Sciences, College of Arts and Science, Nizwa (Oman); Gupta, Y.K. [Raj Kumar Goel Institute of Technology, Department of Mathematics, Ghaziabad (India); Rahaman, Farook [Jadavpur University, Department of Mathematics, Kolkata, West Bengal (India)

2016-07-15

We have presented a new anisotropic solution of Einstein's field equations for compact-star models. Einstein's field equations are solved by using the class-one condition (S.N. Pandey, S.P. Sharma, Gen. Relativ. Gravit. 14, 113 (1982)). We constructed the expression for the anisotropy factor (Δ) by using the pressure anisotropy condition and thereafter we obtained the physical parameters like energy density, radial and transverse pressure. These models parameters are well-behaved inside the star and satisfy all the required physical conditions. Also we observed the very interesting result that all physical parameters depend upon the anisotropy factor (Δ). The mass and radius of the present compact-star models are quite compatible with the observational astrophysical compact stellar objects like Her X-1, RXJ 1856-37, SAX J1808.4-3658(SS1), SAX J1808.4-3658(SS2). (orig.)

Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

Directory of Open Access Journals (Sweden)

Tian Wang

2013-12-01

Full Text Available The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM, combined with its sparsified version (sparse online LS-OC-SVM. LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

Learning to recognise : A study on one-class classification and active learning

NARCIS (Netherlands)

Juszczak, P.

2006-01-01

The thesis treats classification problems which are undersampled or where there exist an unbalance between classes in the sampling. The thesis is divided into three parts. The first two parts treat the problem of one-class classification. In the one-class classification problem, it is assumed that

On hierarchical models for visual recognition and learning of objects, scenes, and activities

CERN Document Server

Spehr, Jens

2015-01-01

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model...

Novel object recognition ability in female mice following exposure to nanoparticle-rich diesel exhaust

Energy Technology Data Exchange (ETDEWEB)

Win-Shwe, Tin-Tin, E-mail: tin.tin.win.shwe@nies.go.jp [Center for Environmental Health Sciences, National Institute for Environmental Studies, 16‐2 Onogawa, Tsukuba, Ibaraki 305‐8506 (Japan); Fujimaki, Hidekazu; Fujitani, Yuji; Hirano, Seishiro [Center for Environmental Risk Research, National Institute for Environmental Studies, 16‐2 Onogawa, Tsukuba, Ibaraki 305‐8506 (Japan)

2012-08-01

Recently, our laboratory reported that exposure to nanoparticle-rich diesel exhaust (NRDE) for 3 months impaired hippocampus-dependent spatial learning ability and up-regulated the expressions of memory function-related genes in the hippocampus of female mice. However, whether NRDE affects the hippocampus-dependent non-spatial learning ability and the mechanism of NRDE-induced neurotoxicity was unknown. Female BALB/c mice were exposed to clean air, middle-dose NRDE (M-NRDE, 47 μg/m{sup 3}), high-dose NRDE (H-NRDE, 129 μg/m{sup 3}), or filtered H-NRDE (F-DE) for 3 months. We then investigated the effect of NRDE exposure on non-spatial learning ability and the expression of genes related to glutamate neurotransmission using a novel object recognition test and a real-time RT-PCR analysis, respectively. We also examined microglia marker Iba1 immunoreactivity in the hippocampus using immunohistochemical analyses. Mice exposed to H-NRDE or F-DE could not discriminate between familiar and novel objects. The control and M-NRDE-exposed groups showed a significantly increased discrimination index, compared to the H-NRDE-exposed group. Although no significant changes in the expression levels of the NMDA receptor subunits were observed, the expression of glutamate transporter EAAT4 was decreased and that of glutamic acid decarboxylase GAD65 was increased in the hippocampus of H-NRDE-exposed mice, compared with the expression levels in control mice. We also found that microglia activation was prominent in the hippocampal area of the H-NRDE-exposed mice, compared with the other groups. These results indicated that exposure to NRDE for 3 months impaired the novel object recognition ability. The present study suggests that genes related to glutamate metabolism may be involved in the NRDE-induced neurotoxicity observed in the present mouse model. -- Highlights: ► The effects of nanoparticle-induced neurotoxicity remain unclear. ► We investigated the effect of exposure to

Novel object recognition ability in female mice following exposure to nanoparticle-rich diesel exhaust

International Nuclear Information System (INIS)

Win-Shwe, Tin-Tin; Fujimaki, Hidekazu; Fujitani, Yuji; Hirano, Seishiro

2012-01-01

Recently, our laboratory reported that exposure to nanoparticle-rich diesel exhaust (NRDE) for 3 months impaired hippocampus-dependent spatial learning ability and up-regulated the expressions of memory function-related genes in the hippocampus of female mice. However, whether NRDE affects the hippocampus-dependent non-spatial learning ability and the mechanism of NRDE-induced neurotoxicity was unknown. Female BALB/c mice were exposed to clean air, middle-dose NRDE (M-NRDE, 47 μg/m 3 ), high-dose NRDE (H-NRDE, 129 μg/m 3 ), or filtered H-NRDE (F-DE) for 3 months. We then investigated the effect of NRDE exposure on non-spatial learning ability and the expression of genes related to glutamate neurotransmission using a novel object recognition test and a real-time RT-PCR analysis, respectively. We also examined microglia marker Iba1 immunoreactivity in the hippocampus using immunohistochemical analyses. Mice exposed to H-NRDE or F-DE could not discriminate between familiar and novel objects. The control and M-NRDE-exposed groups showed a significantly increased discrimination index, compared to the H-NRDE-exposed group. Although no significant changes in the expression levels of the NMDA receptor subunits were observed, the expression of glutamate transporter EAAT4 was decreased and that of glutamic acid decarboxylase GAD65 was increased in the hippocampus of H-NRDE-exposed mice, compared with the expression levels in control mice. We also found that microglia activation was prominent in the hippocampal area of the H-NRDE-exposed mice, compared with the other groups. These results indicated that exposure to NRDE for 3 months impaired the novel object recognition ability. The present study suggests that genes related to glutamate metabolism may be involved in the NRDE-induced neurotoxicity observed in the present mouse model. -- Highlights: ► The effects of nanoparticle-induced neurotoxicity remain unclear. ► We investigated the effect of exposure to

Infliximab ameliorates AD-associated object recognition memory impairment.

Science.gov (United States)

Kim, Dong Hyun; Choi, Seong-Min; Jho, Jihoon; Park, Man-Seok; Kang, Jisu; Park, Se Jin; Ryu, Jong Hoon; Jo, Jihoon; Kim, Hyun Hee; Kim, Byeong C

2016-09-15

Dysfunctions in the perirhinal cortex (PRh) are associated with visual recognition memory deficit, which is frequently detected in the early stage of Alzheimer's disease. Muscarinic acetylcholine receptor-dependent long-term depression (mAChR-LTD) of synaptic transmission is known as a key pathway in eliciting this type of memory, and Tg2576 mice expressing enhanced levels of Aβ oligomers are found to have impaired mAChR-LTD in this brain area at as early as 3 months of age. We found that the administration of Aβ oligomers in young normal mice also induced visual recognition memory impairment and perturbed mAChR-LTD in mouse PRh slices. In addition, when mice were treated with infliximab, a monoclonal antibody against TNF-α, visual recognition memory impaired by pre-administered Aβ oligomers dramatically improved and the detrimental Aβ effect on mAChR-LTD was annulled. Taken together, these findings suggest that Aβ-induced inflammation is mediated through TNF-α signaling cascades, disturbing synaptic transmission in the PRh, and leading to visual recognition memory deficits. Copyright © 2016 Elsevier B.V. All rights reserved.

Recognition of explosives fingerprints on objects for courier services using machine learning methods and laser-induced breakdown spectroscopy.

Science.gov (United States)

Moros, J; Serrano, J; Gallego, F J; Macías, J; Laserna, J J

2013-06-15

During recent years laser-induced breakdown spectroscopy (LIBS) has been considered one of the techniques with larger ability for trace detection of explosives. However, despite of the high sensitivity exhibited for this application, LIBS suffers from a limited selectivity due to difficulties in assigning the molecular origin of the spectral emissions observed. This circumstance makes the recognition of fingerprints a latent challenging problem. In the present manuscript the sorting of six explosives (chloratite, ammonal, DNT, TNT, RDX and PETN) against a broad list of potential harmless interferents (butter, fuel oil, hand cream, olive oil, …), all of them in the form of fingerprints deposited on the surfaces of objects for courier services, has been carried out. When LIBS information is processed through a multi-stage architecture algorithm built from a suitable combination of 3 learning classifiers, an unknown fingerprint may be labeled into a particular class. Neural network classifiers trained by the Levenberg-Marquardt rule were decided within 3D scatter plots projected onto the subspace of the most useful features extracted from the LIBS spectra. Experimental results demonstrate that the presented algorithm sorts fingerprints according to their hazardous character, although its spectral information is virtually identical in appearance, with rates of false negatives and false positives not beyond of 10%. These reported achievements mean a step forward in the technology readiness level of LIBS for this complex application related to defense, homeland security and force protection. Copyright © 2013 Elsevier B.V. All rights reserved.

Short-term blueberry-enriched diet prevents and reverses object recognition memory loss in aging rats.

Science.gov (United States)

Malin, David H; Lee, David R; Goyarzu, Pilar; Chang, Yu-Hsuan; Ennis, Lalanya J; Beckett, Elizabeth; Shukitt-Hale, Barbara; Joseph, James A

2011-03-01

Previously, 4 mo of a blueberry-enriched (BB) antioxidant diet prevented impaired object recognition memory in aging rats. Experiment 1 determined whether 1- and 2-mo BB diets would have a similar effect and whether the benefits would disappear promptly after terminating the diets. Experiment 2 determined whether a 1-mo BB diet could subsequently reverse existing object memory impairment in aging rats. In experiment 1, Fischer-344 rats were maintained on an appropriate control diet or on 1 or 2 mo of the BB diet before testing object memory at 19 mo postnatally. In experiment 2, rats were tested for object recognition memory at 19 mo and again at 20 mo after 1 mo of maintenance on a 2% BB or control diet. In experiment 1, the control group performed no better than chance, whereas the 1- and 2-mo BB diet groups performed similarly and significantly better than controls. The 2-mo BB-diet group, but not the 1-mo group, maintained its performance over a subsequent month on a standard laboratory diet. In experiment 2, the 19-mo-old rats performed near chance. At 20 mo of age, the rats subsequently maintained on the BB diet significantly increased their object memory scores, whereas the control diet group exhibited a non-significant decline. The change in object memory scores differed significantly between the two diet groups. These results suggest that a considerable degree of age-related object memory decline can be prevented and reversed by brief maintenance on BB diets. Copyright © 2011 Elsevier Inc. All rights reserved.

Category Specificity in Normal Episodic Learning: Applications to Object Recognition and Category-Specific Agnosia

Science.gov (United States)

Bukach, Cindy M.; Bub, Daniel N.; Masson, Michael E. J.; Lindsay, D. Stephen

2004-01-01

Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of object recognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

Science.gov (United States)

Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

2013-11-01

Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory. Copyright © 2013 Elsevier Inc. All rights

Target recognition based on convolutional neural network

Science.gov (United States)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Heterozygous Che-1 KO mice show deficiencies in object recognition memory persistence.

Science.gov (United States)

Zalcman, Gisela; Corbi, Nicoletta; Di Certo, Maria Grazia; Mattei, Elisabetta; Federman, Noel; Romano, Arturo

2016-10-06

Transcriptional regulation is a key process in the formation of long-term memories. Che-1 is a protein involved in the regulation of gene transcription that has recently been proved to bind the transcription factor NF-κB, which is known to be involved in many memory-related molecular events. This evidence prompted us to investigate the putative role of Che-1 in memory processes. For this study we newly generated a line of Che-1(+/-) heterozygous mice. Che-1 homozygous KO mouse is lethal during development, but Che-1(+/-) heterozygous mouse is normal in its general anatomical and physiological characteristics. We analyzed the behavioral characteristic and memory performance of Che-1(+/-) mice in two NF-κB dependent types of memory. We found that Che-1(+/-) mice show similar locomotor activity and thigmotactic behavior than wild type (WT) mice in an open field. In a similar way, no differences were found in anxiety-like behavior between Che-1(+/-) and WT mice in an elevated plus maze as well as in fear response in a contextual fear conditioning (CFC) and object exploration in a novel object recognition (NOR) task. No differences were found between WT and Che-1(+/-) mice performance in CFC training and when tested at 24h or 7days after training. Similar performance was found between groups in NOR task, both in training and 24h testing performance. However, we found that object recognition memory persistence at 7days was impaired in Che-1(+/-) heterozygous mice. This is the first evidence showing that Che-1 is involved in memory processes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Noradrenergic Activation of the Basolateral Amygdala Enhances Object Recognition Memory and Induces Chromatin Remodeling in the Insular Cortex

Directory of Open Access Journals (Sweden)

Hassiba eBeldjoud

2015-04-01

Full Text Available It is well established that arousal-induced memory enhancement requires noradrenergic activation of the basolateral complex of the amygdala (BLA and modulatory influences on information storage processes in its many target regions. While this concept is well accepted, the molecular basis of such BLA effects on neural plasticity changes within other brain regions remains to be elucidated. The present study investigated whether noradrenergic activation of the BLA after object recognition training induces chromatin remodeling through histone post-translational modifications in the insular cortex (IC, a brain region that is importantly involved in object recognition memory. Male Sprague–Dawley rats were trained on an object recognition task, followed immediately by bilateral microinfusions of norepinephrine (1.0 µg or saline administered into the BLA. Saline-treated control rats exhibited poor 24-h retention, whereas norepinephrine treatment induced robust 24-h object recognition memory. Most importantly, this memory-enhancing dose of norepinephrine induced a global reduction in the acetylation levels of histone H3 at lysine 14, H2B and H4 in the IC 1 h later, whereas it had no effect on the phosphorylation of histone H3 at serine 10 or tri-methylation of histone H3 at lysine 27. Norepinephrine administered into the BLA of non-trained control rats did not induce any changes in the histone marks investigated in this study. These findings indicate that noradrenergic activation of the BLA induces training-specific effects on chromatin remodeling mechanisms, and presumably gene transcription, in its target regions, which may contribute to the understanding of the molecular mechanisms of stress and emotional arousal effects on memory consolidation.

Channels as Objects in Concurrent Object-Oriented Programming

Directory of Open Access Journals (Sweden)

Joana Campos

2011-10-01

Full Text Available There is often a sort of a protocol associated to each class, stating when and how certain methods should be called. Given that this protocol is, if at all, described in the documentation accompanying the class, current mainstream object-oriented languages cannot provide for the verification of client code adherence against the sought class behaviour. We have defined a class-based concurrent object-oriented language that formalises such protocols in the form of usage types. Usage types are attached to class definitions, allowing for the specification of (1 the available methods, (2 the tests clients must perform on the result of methods, and (3 the object status - linear or shared - all of which depend on the object's state. Our work extends the recent approach on modular session types by eliminating channel operations, and defining the method call as the single communication primitive in both sequential and concurrent settings. In contrast to previous works, we define a single category for objects, instead of distinct categories for linear and for shared objects, and let linear objects evolve into shared ones. We introduce a standard sync qualifier to prevent thread interference in certain operations on shared objects. We formalise the language syntax, the operational semantics, and a type system that enforces by static typing that methods are called only when available, and by a single client if so specified in the usage type. We illustrate the language via a complete example.

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

Directory of Open Access Journals (Sweden)

Feng Lin

2007-11-01

Full Text Available Abstract Background Peptides binding to Major Histocompatibility Complex (MHC class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides. In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs. Results The proposed methods are intended for finding peptides binding to MHC class II I-Ag7 molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-Ag7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1 an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2 quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules. Conclusion We present two MOEA-based algorithms for finding motifs

MHC class I molecules with superenhanced CD8 binding properties bypass the requirement for cognate TCR recognition and nonspecifically activate CTLs

NARCIS (Netherlands)

L. Wooldridge (Linda); M. Clement (Mathew); A. Lissina (Anna); E.S.J. Edwards (Emily); K. Ladell (Kristin); J. Ekeruche (Julia); R.E. Hewitt (Rachel); B. Laugel (Bruno); E. Gostick (Emma); D.K. Cole (David); J.E.M.A. Debets (Reno); C.A. Berrevoets (Cor); J.J. Miles (John); S.R. Burrows (Scott); D.A. Price (David); A.K. Sewell (Andrew)

2010-01-01

textabstractCD8+CTLs are essential for effective immune defense against intracellular microbes and neoplasia. CTLs recognize short peptide fragments presented in association with MHC class I (MHCI) molecules on the surface of infected or dysregulated cells. Ag recognition involves the binding of

The impact of differences between subjective and objective social class on life satisfaction among the Korean population in early old age: Analysis of Korean longitudinal study on aging.

Science.gov (United States)

Choi, Young; Kim, Jae-Hyun; Park, Eun-Cheol

2016-01-01

Several previous studies have established the relationship between the effects of socioeconomic status or subjective social strata on life satisfaction. However, no previous study has examined the relationship between social class and life satisfaction in terms of a disparity between subjective and objective social status. To investigate the relationship between differences in subjective and objective social class and life satisfaction. Data from the Korean Longitudinal Study of Aging with 8252 participants aged 45 or older was used. Life satisfaction was measured by the question, "How satisfied are you with your quality of life?" The main independent variable was differences in objective (income and education) and subjective social class, which was classified according to nine categories (ranging from high-high to low-low). This association was investigated by linear mixed model due to two waves data nested within individuals. Lower social class (income, education, subjective social class) was associated with dissatisfaction. The impact of objective and subjective social class on life satisfaction varied according to the level of differences in objective and subjective social class. Namely, an individual's life satisfaction declined as objective social classes decreased at the same level of subjective social class (i.e., HH, MH, LH). In both dimensions of objective social class (education and income), an individual's life satisfaction declined as subjective social class decreased by one level (i.e., HH, HM, HL). Our findings indicated that social supports is needed to improve the life satisfaction among the population aged 45 or more with low social class. The government should place increased focus on policies that encourage not only the life satisfaction of the Korean elderly with low objective social class, but also subjective social class. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Investigations into the involvement of NMDA mechanisms in recognition memory.

Science.gov (United States)

Warburton, E Clea; Barker, Gareth R I; Brown, Malcom W

2013-11-01

This review will focus on evidence showing that NMDA receptor neurotransmission is critical for synaptic plasticity processes within brain regions known to be necessary for the formation of object recognition memories. The aim will be to provide evidence concerning NMDA mechanisms related to recognition memory processes and show that recognition memory for objects, places or associations between objects and places depends on NMDA neurotransmission within the perirhinal cortex, temporal association cortex medial prefrontal cortex and hippocampus. Administration of the NMDA antagonist AP5, selectively into each of these brain regions has revealed that the extent of the involvement NMDA receptors appears dependent on the type of information required to solve the recognition memory task; thus NMDA receptors in the perirhinal cortex are crucial for the encoding of long-term recognition memory for objects, and object-in-place associations, but not for short-term recognition memory or for retrieval. In contrast the hippocampus and medial prefrontal cortex are required for both long-term and short-term recognition memory for places or associations between objects and places, or for recognition memory tasks that have a temporal component. Such studies have therefore confirmed that the multiple brain regions make distinct contributions to recognition memory but in addition that more than one synaptic plasticity process must be involved. This article is part of the Special Issue entitled 'Glutamate Receptor-Dependent Synaptic Plasticity'. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Improving human object recognition performance using video enhancement techniques

Science.gov (United States)

Whitman, Lucy S.; Lewis, Colin; Oakley, John P.

2004-12-01

Atmospheric scattering causes significant degradation in the quality of video images, particularly when imaging over long distances. The principle problem is the reduction in contrast due to scattered light. It is known that when the scattering particles are not too large compared with the imaging wavelength (i.e. Mie scattering) then high spatial resolution information may be contained within a low-contrast image. Unfortunately this information is not easily perceived by a human observer, particularly when using a standard video monitor. A secondary problem is the difficulty of achieving a sharp focus since automatic focus techniques tend to fail in such conditions. Recently several commercial colour video processing systems have become available. These systems use various techniques to improve image quality in low contrast conditions whilst retaining colour content. These systems produce improvements in subjective image quality in some situations, particularly in conditions of haze and light fog. There is also some evidence that video enhancement leads to improved ATR performance when used as a pre-processing stage. Psychological literature indicates that low contrast levels generally lead to a reduction in the performance of human observers in carrying out simple visual tasks. The aim of this paper is to present the results of an empirical study on object recognition in adverse viewing conditions. The chosen visual task was vehicle number plate recognition at long ranges (500 m and beyond). Two different commercial video enhancement systems are evaluated using the same protocol. The results show an increase in effective range with some differences between the different enhancement systems.

Two diverse models of embedding class one

Science.gov (United States)

Kuhfittig, Peter K. F.

2018-05-01

Embedding theorems have continued to be a topic of interest in the general theory of relativity since these help connect the classical theory to higher-dimensional manifolds. This paper deals with spacetimes of embedding class one, i.e., spacetimes that can be embedded in a five-dimensional flat spacetime. These ideas are applied to two diverse models, a complete solution for a charged wormhole admitting a one-parameter group of conformal motions and a new model to explain the flat rotation curves in spiral galaxies without the need for dark matter.

In-the-wild facial expression recognition in extreme poses

Science.gov (United States)

Yang, Fei; Zhang, Qian; Zheng, Chi; Qiu, Guoping

2018-04-01

In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

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.

Memory consolidation and expression of object recognition are susceptible to retroactive interference.

Science.gov (United States)

Villar, María Eugenia; Martinez, María Cecilia; Lopes da Cunha, Pamela; Ballarini, Fabricio; Viola, Haydee

2017-02-01

With the aim of analyzing if object recognition long-term memory (OR-LTM) formation is susceptible to retroactive interference (RI), we submitted rats to sequential sample sessions using the same arena but changing the identity of a pair of objects placed in it. Separate groups of animals were tested in the arena in order to evaluate the LTM for these objects. Our results suggest that OR-LTM formation was retroactively interfered within a critical time window by the exploration of a new, but not familiar, object. This RI acted on the consolidation of the object explored in the first sample session because its OR-STM measured 3h after training was not affected, whereas the OR-LTM measured at 24h was impaired. This sample session also impaired the expression of OR memory when it took place before the test. Moreover, local inactivation of the dorsal Hippocampus (Hp) or the medial Prefrontal Cortex (mPFC) previous to the exploration of the second pair of objects impaired their consolidation restoring the LTM for the objects explored in the first session. This data suggests that both brain regions are involved in the processing of OR-memory and also that if those regions are engaged in another process before finishing the first consolidation process its LTM will be impaired by RI. Copyright © 2016 Elsevier Inc. All rights reserved.

Anomaly detection for medical images based on a one-class classification

Science.gov (United States)

Wei, Qi; Ren, Yinhao; Hou, Rui; Shi, Bibo; Lo, Joseph Y.; Carin, Lawrence

2018-02-01

Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. A conventional way to address this is to learn a discriminative model using training datasets of negative and positive samples. The learned model can be used to classify a testing sample into a positive or negative class. However, in medical applications, the high unbalance between negative and positive samples poses a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount of negative samples, i.e., normal medical images, we propose to learn an unsupervised model to characterize the negative class. To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large medical images. A testing image is decomposed into patches and then fed into the learned autoencoder to reconstruct these patches themselves. The reconstruction error of one patch is used to classify this patch into a binary class, i.e., a positive or a negative one, leading to a one-class classifier. The positive patches highlight the suspicious areas containing anomalies in a large medical image. The proposed method has been tested on InBreast dataset and achieves an AUC of 0.84. The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.

Attribute-based classification for zero-shot visual object categorization.

Science.gov (United States)

Lampert, Christoph H; Nickisch, Hannes; Harmeling, Stefan

2014-03-01

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.

Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods

Science.gov (United States)

Cyganek, Boguslaw; Smolka, Bogdan

2015-02-01

In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.

Impairment of object recognition memory by maternal bisphenol A exposure is associated with inhibition of Akt and ERK/CREB/BDNF pathway in the male offspring hippocampus.

Science.gov (United States)

Wang, Chong; Li, Zhihui; Han, Haijun; Luo, Guangying; Zhou, Bingrui; Wang, Shaolin; Wang, Jundong

2016-02-03

Bisphenol A (BPA) is a commonly used endocrine-disrupting chemical used as a component of polycarbonates plastics that has potential adverse effects on human health. Exposure to BPA during development has been implicated in memory deficits, but the mechanism of action underlying the effect is not fully understood. In this study, we investigated the effect of maternal exposure to BPA on object recognition memory and the expressions of proteins important for memory, especially focusing on the ERK/CREB/BDNF pathway. Pregnant Sprague-Dawley female rats were orally treated with either vehicle or BPA (0.05, 0.5, 5 or 50 mg/kg BW/day) during days 9-20 of gestation. Male offspring were tested on postnatal day 21 with the object recognition task. Recognition memory was assessed using the object recognition index (index=the time spent exploring the novel object/(the time spent exploring the novel object+the time spent exploring the familiar object)). In the test session performed 90 min after the training session, BPA-exposed male offspring not only spent more time in exploring the familiar object at the highest dose than the control, but also displayed a significantly decreased the object recognition index at the doses of 0.5, 5 and 50 mg/kg BW/day. During the test session performed 24h after the training session, BPA-treated males did not change the time spent exploring the familiar object, but had a decreased object recognition index at 5 and 50 mg/kg BW/day, when compared to control group. These findings indicate that object recognition memory was susceptible to maternal BPA exposure. Western blot analysis of hippocampi from BPA-treated male offspring revealed a decrease in Akt, phospho-Akt, p44/42 MAPK and phospho-p44/42 MAPK protein levels, compared to controls. In addition, BPA significantly inhibited the levels of phosphorylation of CREB and BDNF in the hippocampus. Our results show that maternal BPA exposure may full impair object recognition memory, and that

Bidirectional Modulation of Recognition Memory.

Science.gov (United States)

Ho, Jonathan W; Poeta, Devon L; Jacobson, Tara K; Zolnik, Timothy A; Neske, Garrett T; Connors, Barry W; Burwell, Rebecca D

2015-09-30

Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects. For example, animals and humans with perirhinal damage are unable to distinguish familiar from novel objects in recognition memory tasks. In the normal brain, perirhinal neurons respond to novelty and familiarity by increasing or decreasing firing rates. Recent work also implicates oscillatory activity in the low-beta and low-gamma frequency bands in sensory detection, perception, and recognition. Using optogenetic methods in a spontaneous object exploration (SOR) task, we altered recognition memory performance in rats. In the SOR task, normal rats preferentially explore novel images over familiar ones. We modulated exploratory behavior in this task by optically stimulating channelrhodopsin-expressing perirhinal neurons at various frequencies while rats looked at novel or familiar 2D images. Stimulation at 30-40 Hz during looking caused rats to treat a familiar image as if it were novel by increasing time looking at the image. Stimulation at 30-40 Hz was not effective in increasing exploration of novel images. Stimulation at 10-15 Hz caused animals to treat a novel image as familiar by decreasing time looking at the image, but did not affect looking times for images that were already familiar. We conclude that optical stimulation of PER at different frequencies can alter visual recognition memory bidirectionally. Significance statement: Recognition of novelty and familiarity are important for learning, memory, and decision making. Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects, but how novelty and familiarity are encoded and transmitted in the brain is not known. Perirhinal neurons respond to novelty and familiarity by changing firing rates, but recent work suggests that brain oscillations may also be important for recognition. In this study, we showed that stimulation of

Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms.

Science.gov (United States)

Schädler, Marc R; Warzybok, Anna; Kollmeier, Birger

2018-01-01

The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than -20 dB could not be predicted.

Geometric Edge Description and Classification in Point Cloud Data with Application to 3D Object Recognition

DEFF Research Database (Denmark)

Jørgensen, Troels Bo; Buch, Anders Glent; Kraft, Dirk

2015-01-01

descriptor allows for both fast computation and fast processing by having a low dimension, while still producing highly reliable edge detections. Lastly, we use our features in a 3D object recognition application using a well-established benchmark. We show that our edge features allow for significant...

People's Risk Recognition Preceding Evacuation and Its Role in Demand Modeling and Planning.

Science.gov (United States)

Urata, Junji; Pel, Adam J

2018-05-01

Evacuation planning and management involves estimating the travel demand in the event that such action is required. This is usually done as a function of people's decision to evacuate, which we show is strongly linked to their risk awareness. We use an empirical data set, which shows tsunami evacuation behavior, to demonstrate that risk recognition is not synonymous with objective risk, but is instead determined by a combination of factors including risk education, information, and sociodemographics, and that it changes dynamically over time. Based on these findings, we formulate an ordered logit model to describe risk recognition combined with a latent class model to describe evacuation choices. Our proposed evacuation choice model along with a risk recognition class can evaluate quantitatively the influence of disaster mitigation measures, risk education, and risk information. The results obtained from the risk recognition model show that risk information has a greater impact in the sense that people recognize their high risk. The results of the evacuation choice model show that people who are unaware of their risk take a longer time to evacuate. © 2017 Society for Risk Analysis.

An object-oriented class design for the generalized finite element method programming

Directory of Open Access Journals (Sweden)

Dorival Piedade Neto

Full Text Available The Generalized Finite Element Method (GFEM is a numerical method based on the Finite Element Method (FEM, presenting as its main feature the possibility of improving the solution by means of local enrichment functions. In spite of its advantages, the method demands a complex data structure, which can be especially benefited by the Object-Oriented Programming (OOP. Even though the OOP for the traditional FEM has been extensively described in the technical literature, specific design issues related to the GFEM are yet little discussed and not clearly defined. In the present article it is described an Object-Oriented (OO class design for the GFEM, aiming to achieve a computational code that presents a flexible class structure, circumventing the difficulties associated to the method characteristics. The proposed design is evaluated by means of some numerical examples, computed using a code implemented in Python programming language.

Teaching Object Permanence: An Action Research Study

Science.gov (United States)

Bruce, Susan M.; Vargas, Claudia

2013-01-01

"Object permanence," also known as "object concept" in the field of visual impairment, is one of the most important early developmental milestones. The achievement of object permanence is associated with the onset of representational thought and language. Object permanence is important to orientation, including the recognition of landmarks.…

Why recognition is rational

Directory of Open Access Journals (Sweden)

Clintin P. Davis-Stober

2010-07-01

Full Text Available The Recognition Heuristic (Gigerenzer and Goldstein, 1996; Goldstein and Gigerenzer, 2002 makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision strategy within a linear modeling framework. We identify conditions under which over-weighting a single predictor is a mini-max strategy among a class of a priori chosen weights based on decision heuristics with respect to a measure of statistical lack of fit we call ``risk''. These strategies, in turn, outperform standard multiple regression as long as the amount of data available is limited. We also show that, under related conditions, weighting only one variable and ignoring all others produces the same risk as ignoring the single variable and weighting all others. This approach has the advantage of generalizing beyond the original environment of the Recognition Heuristic to situations with more than two choice options, binary or continuous representations of recognition, and to other single variable heuristics. We analyze the structure of data used in some prior recognition tasks and find that it matches the sufficient conditions for optimality in our results. Rather than being a poor or adequate substitute for a compensatory model, the Recognition Heuristic closely approximates an optimal strategy when a decision maker has finite data about the world.

The role of the hippocampus in recognition memory.

Science.gov (United States)

Bird, Chris M

2017-08-01

Many theories of declarative memory propose that it is supported by partially separable processes underpinned by different brain structures. The hippocampus plays a critical role in binding together item and contextual information together and processing the relationships between individual items. By contrast, the processing of individual items and their later recognition can be supported by extrahippocampal regions of the medial temporal lobes (MTL), particularly when recognition is based on feelings of familiarity without the retrieval of any associated information. These theories are domain-general in that "items" might be words, faces, objects, scenes, etc. However, there is mixed evidence that item recognition does not require the hippocampus, or that familiarity-based recognition can be supported by extrahippocampal regions. By contrast, there is compelling evidence that in humans, hippocampal damage does not affect recognition memory for unfamiliar faces, whilst recognition memory for several other stimulus classes is impaired. I propose that regions outside of the hippocampus can support recognition of unfamiliar faces because they are perceived as discrete items and have no prior conceptual associations. Conversely, extrahippocampal processes are inadequate for recognition of items which (a) have been previously experienced, (b) are conceptually meaningful, or (c) are perceived as being comprised of individual elements. This account reconciles findings from primate and human studies of recognition memory. Furthermore, it suggests that while the hippocampus is critical for binding and relational processing, these processes are required for item recognition memory in most situations. Copyright © 2017 Elsevier Ltd. All rights reserved.

hemaClass.org: Online One-By-One Microarray Normalization and Classification of Hematological Cancers for Precision Medicine.

Science.gov (United States)

Falgreen, Steffen; Ellern Bilgrau, Anders; Brøndum, Rasmus Froberg; Hjort Jakobsen, Lasse; Have, Jonas; Lindblad Nielsen, Kasper; El-Galaly, Tarec Christoffer; Bødker, Julie Støve; Schmitz, Alexander; H Young, Ken; Johnsen, Hans Erik; Dybkær, Karen; Bøgsted, Martin

2016-01-01

Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.

Optical Pattern Recognition

Science.gov (United States)

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

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

A new kernel discriminant analysis framework for electronic nose recognition

International Nuclear Information System (INIS)

Zhang, Lei; Tian, Feng-Chun

2014-01-01

Graphical abstract: - Highlights: • This paper proposes a new discriminant analysis framework for feature extraction and recognition. • The principle of the proposed NDA is derived mathematically. • The NDA framework is coupled with kernel PCA for classification. • The proposed KNDA is compared with state of the art e-Nose recognition methods. • The proposed KNDA shows the best performance in e-Nose experiments. - Abstract: Electronic nose (e-Nose) technology based on metal oxide semiconductor gas sensor array is widely studied for detection of gas components. This paper proposes a new discriminant analysis framework (NDA) for dimension reduction and e-Nose recognition. In a NDA, the between-class and the within-class Laplacian scatter matrix are designed from sample to sample, respectively, to characterize the between-class separability and the within-class compactness by seeking for discriminant matrix to simultaneously maximize the between-class Laplacian scatter and minimize the within-class Laplacian scatter. In terms of the linear separability in high dimensional kernel mapping space and the dimension reduction of principal component analysis (PCA), an effective kernel PCA plus NDA method (KNDA) is proposed for rapid detection of gas mixture components by an e-Nose. The NDA framework is derived in this paper as well as the specific implementations of the proposed KNDA method in training and recognition process. The KNDA is examined on the e-Nose datasets of six kinds of gas components, and compared with state of the art e-Nose classification methods. Experimental results demonstrate that the proposed KNDA method shows the best performance with average recognition rate and total recognition rate as 94.14% and 95.06% which leads to a promising feature extraction and multi-class recognition in e-Nose

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

Science.gov (United States)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

Hidden neural networks: application to speech recognition

DEFF Research Database (Denmark)

Riis, Søren Kamaric

1998-01-01

We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

A contextual classifier that only requires one prototype pixel for each class

DEFF Research Database (Denmark)

Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær; Conradsen, Knut

2001-01-01

constructed with experimental data is used in this stage. The algorithm was tested with the Kappa coefficient k on synthetical images and compared with K-means (k~=0.41) and a similar scheme that uses spectral means (k~=0.75) instead of histograms (k~=0.90). Results are shown on a dermatological image......A three stage scheme for classification of multi-spectral images is proposed. In each stage, statistics of each class present in the image are estimated. The user is required to provide only one prototype pixel for each class to be seeded into a homogeneous region. The algorithm starts...... by generating optimum initial training sets, one for each class, maximizing the redundancy in the data sets. These sets are the realizations of the maximal discs centered on the prototype pixels for which it is true that all the elements belong to the same class as the center one. Afterwards a region growing...

A Contextual Classifier That Only Requires One Prototype Pixel for Each Class

DEFF Research Database (Denmark)

Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær; Conradsen, Knut

2002-01-01

constructed with experimental data is used in this stage. The algorithm was tested with the Kappa coefficient κ on synthetic images and compared with K-means (κ~=0.41) and a similar scheme that uses spectral means (κ~=0.75) instead of histograms (κ~=0.90). The results are shown on a dermatological image......A three-stage scheme for the classification of multispectral images is proposed. In each stage, the statistics of each class present in the image are estimated. The user is required to provide only one prototype pixel for each class to be seeded into a homogeneous region. The algorithm starts...... by generating optimum initial training sets, one for each class, maximizing the redundancy in the data sets. These sets are the realizations of the maximal discs centered on the prototype pixels for which it is true that all the elements belong to the same class as the center one. Afterwards, a region...

New automated procedure to assess context recognition memory in mice.

Science.gov (United States)

Reiss, David; Walter, Ondine; Bourgoin, Lucie; Kieffer, Brigitte L; Ouagazzal, Abdel-Mouttalib

2014-11-01

Recognition memory is an important aspect of human declarative memory and is one of the routine memory abilities altered in patients with amnestic syndrome and Alzheimer's disease. In rodents, recognition memory has been most widely assessed using the novel object preference paradigm, which exploits the spontaneous preference that animals display for novel objects. Here, we used nose-poke units instead of objects to design a simple automated method for assessing context recognition memory in mice. In the acquisition trial, mice are exposed for the first time to an operant chamber with one blinking nose-poke unit. In the choice session, a novel nonblinking nose-poke unit is inserted into an empty spatial location and the number of nose poking dedicated to each set of nose-poke unit is used as an index of recognition memory. We report that recognition performance varies as a function of the length of the acquisition period and the retention delay and is sensitive to conventional amnestic treatments. By manipulating the features of the operant chamber during a brief retrieval episode (3-min long), we further demonstrate that reconsolidation of the original contextual memory depends on the magnitude and the type of environmental changes introduced into the familiar spatial environment. These results show that the nose-poke recognition task provides a rapid and reliable way for assessing context recognition memory in mice and offers new possibilities for the deciphering of the brain mechanisms governing the reconsolidation process.

Computationally efficient SVM multi-class image recognition with confidence measures

International Nuclear Information System (INIS)

Makili, Lazaro; Vega, Jesus; Dormido-Canto, Sebastian; Pastor, Ignacio; Murari, Andrea

2011-01-01

Typically, machine learning methods produce non-qualified estimates, i.e. the accuracy and reliability of the predictions are not provided. Transductive predictors are very recent classifiers able to provide, simultaneously with the prediction, a couple of values (confidence and credibility) to reflect the quality of the prediction. Usually, a drawback of the transductive techniques for huge datasets and large dimensionality is the high computational time. To overcome this issue, a more efficient classifier has been used in a multi-class image classification problem in the TJ-II stellarator database. It is based on the creation of a hash function to generate several 'one versus the rest' classifiers for every class. By using Support Vector Machines as the underlying classifier, a comparison between the pure transductive approach and the new method has been performed. In both cases, the success rates are high and the computation time with the new method is up to 0.4 times the old one.

Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs

Science.gov (United States)

Levchuk, Georgiy; Bobick, Aaron; Jones, Eric

2010-04-01

In this paper, we describe results from experimental analysis of a model designed to recognize activities and functions of moving and static objects from low-resolution wide-area video inputs. Our model is based on representing the activities and functions using three variables: (i) time; (ii) space; and (iii) structures. The activity and function recognition is achieved by imposing lexical, syntactic, and semantic constraints on the lower-level event sequences. In the reported research, we have evaluated the utility and sensitivity of several algorithms derived from natural language processing and pattern recognition domains. We achieved high recognition accuracy for a wide range of activity and function types in the experiments using Electro-Optical (EO) imagery collected by Wide Area Airborne Surveillance (WAAS) platform.

Incremental Nonnegative Matrix Factorization for Face Recognition

Directory of Open Access Journals (Sweden)

Wen-Sheng Chen

2008-01-01

Full Text Available Nonnegative matrix factorization (NMF is a promising approach for local feature extraction in face recognition tasks. However, there are two major drawbacks in almost all existing NMF-based methods. One shortcoming is that the computational cost is expensive for large matrix decomposition. The other is that it must conduct repetitive learning, when the training samples or classes are updated. To overcome these two limitations, this paper proposes a novel incremental nonnegative matrix factorization (INMF for face representation and recognition. The proposed INMF approach is based on a novel constraint criterion and our previous block strategy. It thus has some good properties, such as low computational complexity, sparse coefficient matrix. Also, the coefficient column vectors between different classes are orthogonal. In particular, it can be applied to incremental learning. Two face databases, namely FERET and CMU PIE face databases, are selected for evaluation. Compared with PCA and some state-of-the-art NMF-based methods, our INMF approach gives the best performance.

On CSM classes via Chern-Fulton classes of f-schemes

OpenAIRE

Fullwood, James; Wang, Dongxu

2015-01-01

The Chern-Fulton class is a generalization of Chern class to the realm of arbitrary embeddable schemes. While Chern-Fulton classes are sensitive to non-reduced scheme structure, they are not sensitive to possible singularities of the underlying support, thus at first glance are not interesting from a singularity theory viewpoint. However, we introduce a class of formal objects which we think of as `fractional schemes', or f-schemes for short, and then show that when one broadens the domain of...

Objective and subjective indicators of happiness in Brazil: the mediating role of social class.

Science.gov (United States)

Islam, Gazi; Wills-Herrera, Eduardo; Hamilton, Marilyn

2009-04-01

The authors tested the proposition that monetary household income affects subjective well-being (E. Deiner, E. M. Suh, R. E. Lucas, & H. L. Smith, 1999) through the mediating mechanisms of objective and subjective social classes. The present authors drew a representative sample in a door-to-door survey format from a Brazilian urban center. Using a back-translated version of E. Diener, R. A. Emmons, R. J. Larson, and S. Griffin's (1985) Satisfaction With Life Scale, the present authors demonstrated a significant relation with income. However, this effect was mediated by objectively and subjectively measured social classes. These effects reinforce, extend, and internationally generalize the Person x Situation perspective elaborated by E. Diener et al. (1999).

Stability-class determination: a comparison for one site

International Nuclear Information System (INIS)

Bowen, B.M.; Dewart, J.M.; Chen, A.I.

1983-01-01

The Pasquill method of determining stability class at a site with irregular terrain with other commonly used methods: vertical temperature difference (#betta#T); Richardson number (Ri) and Bulk Richardson number (Ri/sub B/); and horizontal standard deviation of wind and vertical standard deviation of wind. Also, the indirect methods of measuring turbulence, such as the Pasquill method, are compared to direct measures of turbulence. The various methods for determining stability class are analyzed and compared with the Pasquill classification scheme at 2 sites with irregular terrain. The Pasquill categories were estimated objectively and compared with other stability indicators for 15-minute periods over a year. The results show that near-surface #betta#T distinguishes the neutral category very well. However, it does not differentiate the specific stable and unstable categories very well. Both the Ri and Ri/sub B/, indicators of both thermal and mechanical turbulence, correlate very well with and distinguish the different stability categories. Due to the irregular terrain, the various methods of determining stability may be even better indicators of turbulence and diffusion if the wind direction were taken into account. It is suggested that further study investigate the methods by wind direction

Impulsivity and novel object recognition test of rat model for vascular cognitive impairment after antipsychotics treatment

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Ronny T Wirasto

2016-12-01

Full Text Available ABSTRACT Vascular cognitive impairment (VCI is a common condition in which no standard treatment has been approved. VCI is often accompanied by behavioral problems which require psychiatric interventions. The common therapeutic agent used for the acute management is antipsychotic injections. Current findings showed that atypical antipsychotic possess better safety profile for treating behavioral problems related to VCI compared to typical antipsychotic. In this study, we induced VCI in Sprague Dawley rats between 6-8 weeks old using bilateral carotid communist artery occlusion technique. The subjects were divided into 4 treatment groups: sham, olanzapine, haloperidol, and risperidone groups. Subjects received intramuscular injections of subsequent drugs for 3 days post VCI induction. Impulsive behavior and object recognition were examined using cliff jumping test and novel object recognition test. The analyses results showed that impulsive behavior was lower in the olanzapine and haloperidol groups compared to sham group, although it was not statistically significant (p = 0.651. The results also showed that there were no significant differences in the time spent exploring old and novel objects in all groups (p = 0.945;0.637 respectively. In conclusion, antipsychotic injection might not be effective to control impulsive behavior post VCI induction.

Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms

Science.gov (United States)

Schädler, Marc R.; Warzybok, Anna; Kollmeier, Birger

2018-01-01

The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than −20 dB could not be predicted. PMID:29692200

STDP-based spiking deep convolutional neural networks for object recognition.

Science.gov (United States)

Kheradpisheh, Saeed Reza; Ganjtabesh, Mohammad; Thorpe, Simon J; Masquelier, Timothée

2018-03-01

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware

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.

Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

Directory of Open Access Journals (Sweden)

Xin Li

2014-06-01

Full Text Available Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians, especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach.

Excess influx of Zn(2+) into dentate granule cells affects object recognition memory via attenuated LTP.

Science.gov (United States)

Suzuki, Miki; Fujise, Yuki; Tsuchiya, Yuka; Tamano, Haruna; Takeda, Atsushi

2015-08-01

The influx of extracellular Zn(2+) into dentate granule cells is nonessential for dentate gyrus long-term potentiation (LTP) and the physiological significance of extracellular Zn(2+) dynamics is unknown in the dentate gyrus. Excess increase in extracellular Zn(2+) in the hippocampal CA1, which is induced with excitation of zincergic neurons, induces memory deficit via excess influx of Zn(2+) into CA1 pyramidal cells. In the present study, it was examined whether extracellular Zn(2+) induces object recognition memory deficit via excess influx of Zn(2+) into dentate granule cells. KCl (100 mM, 2 µl) was locally injected into the dentate gyrus. The increase in intracellular Zn(2+) in dentate granule cells induced with high K(+) was blocked by co-injection of CaEDTA and CNQX, an extracellular Zn(2+) chelator and an AMPA receptor antagonist, respectively, suggesting that high K(+) increases the influx of Zn(2+) into dentate granule cells via AMPA receptor activation. Dentate gyrus LTP induction was attenuated 1 h after KCl injection into the dentate gyrus and also attenuated when KCl was injected 5 min after the induction. Memory deficit was induced when training of object recognition test was performed 1 h after KCl injection into the dentate gyrus and also induced when KCl was injected 5 min after the training. High K(+)-induced impairments of LTP and memory were rescued by co-injection of CaEDTA. These results indicate that excess influx of Zn(2+) into dentate granule cells via AMPA receptor activation affects object recognition memory via attenuated LTP induction. Even in the dentate gyrus where is scarcely innervated by zincergic neurons, it is likely that extracellular Zn(2+) homeostasis is strictly regulated for cognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

Emerging technologies with potential for objectively evaluating speech recognition skills.

Science.gov (United States)

Rawool, Vishakha Waman

2016-01-01

Work-related exposure to noise and other ototoxins can cause damage to the cochlea, synapses between the inner hair cells, the auditory nerve fibers, and higher auditory pathways, leading to difficulties in recognizing speech. Procedures designed to determine speech recognition scores (SRS) in an objective manner can be helpful in disability compensation cases where the worker claims to have poor speech perception due to exposure to noise or ototoxins. Such measures can also be helpful in determining SRS in individuals who cannot provide reliable responses to speech stimuli, including patients with Alzheimer's disease, traumatic brain injuries, and infants with and without hearing loss. Cost-effective neural monitoring hardware and software is being rapidly refined due to the high demand for neurogaming (games involving the use of brain-computer interfaces), health, and other applications. More specifically, two related advances in neuro-technology include relative ease in recording neural activity and availability of sophisticated analysing techniques. These techniques are reviewed in the current article and their applications for developing objective SRS procedures are proposed. Issues related to neuroaudioethics (ethics related to collection of neural data evoked by auditory stimuli including speech) and neurosecurity (preservation of a person's neural mechanisms and free will) are also discussed.

Genetic Mapping in Mice Reveals the Involvement of Pcdh9 in Long-Term Social and Object Recognition and Sensorimotor Development.

Science.gov (United States)

Bruining, Hilgo; Matsui, Asuka; Oguro-Ando, Asami; Kahn, René S; Van't Spijker, Heleen M; Akkermans, Guus; Stiedl, Oliver; van Engeland, Herman; Koopmans, Bastijn; van Lith, Hein A; Oppelaar, Hugo; Tieland, Liselotte; Nonkes, Lourens J; Yagi, Takeshi; Kaneko, Ryosuke; Burbach, J Peter H; Yamamoto, Nobuhiko; Kas, Martien J

2015-10-01

Quantitative genetic analysis of basic mouse behaviors is a powerful tool to identify novel genetic phenotypes contributing to neurobehavioral disorders. Here, we analyzed genetic contributions to single-trial, long-term social and nonsocial recognition and subsequently studied the functional impact of an identified candidate gene on behavioral development. Genetic mapping of single-trial social recognition was performed in chromosome substitution strains, a sophisticated tool for detecting quantitative trait loci (QTL) of complex traits. Follow-up occurred by generating and testing knockout (KO) mice of a selected QTL candidate gene. Functional characterization of these mice was performed through behavioral and neurological assessments across developmental stages and analyses of gene expression and brain morphology. Chromosome substitution strain 14 mapping studies revealed an overlapping QTL related to long-term social and object recognition harboring Pcdh9, a cell-adhesion gene previously associated with autism spectrum disorder. Specific long-term social and object recognition deficits were confirmed in homozygous (KO) Pcdh9-deficient mice, while heterozygous mice only showed long-term social recognition impairment. The recognition deficits in KO mice were not associated with alterations in perception, multi-trial discrimination learning, sociability, behavioral flexibility, or fear memory. Rather, KO mice showed additional impairments in sensorimotor development reflected by early touch-evoked biting, rotarod performance, and sensory gating deficits. This profile emerged with structural changes in deep layers of sensory cortices, where Pcdh9 is selectively expressed. This behavior-to-gene study implicates Pcdh9 in cognitive functions required for long-term social and nonsocial recognition. This role is supported by the involvement of Pcdh9 in sensory cortex development and sensorimotor phenotypes. Copyright © 2015 Society of Biological Psychiatry. Published

Two speed factors of visual recognition independently correlated with fluid intelligence.

Science.gov (United States)

Tachibana, Ryosuke; Namba, Yuri; Noguchi, Yasuki

2014-01-01

Growing evidence indicates a moderate but significant relationship between processing speed in visuo-cognitive tasks and general intelligence. On the other hand, findings from neuroscience proposed that the primate visual system consists of two major pathways, the ventral pathway for objects recognition and the dorsal pathway for spatial processing and attentive analysis. Previous studies seeking for visuo-cognitive factors of human intelligence indicated a significant correlation between fluid intelligence and the inspection time (IT), an index for a speed of object recognition performed in the ventral pathway. We thus presently examined a possibility that neural processing speed in the dorsal pathway also represented a factor of intelligence. Specifically, we used the mental rotation (MR) task, a popular psychometric measure for mental speed of spatial processing in the dorsal pathway. We found that the speed of MR was significantly correlated with intelligence scores, while it had no correlation with one's IT (recognition speed of visual objects). Our results support the new possibility that intelligence could be explained by two types of mental speed, one related to object recognition (IT) and another for manipulation of mental images (MR).

Polyacene and a new class of quasi-one-dimensional conductors

International Nuclear Information System (INIS)

Kivelson, S.; Chapman, O.L.

1983-01-01

Most one-dimensional conductors are quite similar since the Fermi surface is a point and the electron energy dispersion relation near the Fermi surface is linear. It is pointed out that in polyacene the Fermi surface lies at the edge of the Brillouin zone, but that an accidental degeneracy between the valence and conduction bands makes it metallic nonetheless. The dispersion relation is therefore quadratic, and the density of states diverges at the Fermi surface. Thus, polyacene [(C 4 H 2 )/sub n/] and its possible derivatives represent a conceptually new class of quasi-one-dimensional conductors. Moreover, we find that this class of materials has the possibility of possessing interesting condensed phases including high-temperature superconductivity and ferromagnetism

Supervised Object Class Colour Normalisation

DEFF Research Database (Denmark)

Riabchenko, Ekatarina; Lankinen, Jukka; Buch, Anders Glent

2013-01-01

. In this work, we develop a such colour normalisation technique, where true colours are not important per se but where examples of same classes have photometrically consistent appearance. This is achieved by supervised estimation of a class specic canonical colour space where the examples have minimal variation......Colour is an important cue in many applications of computer vision and image processing, but robust usage often requires estimation of the unknown illuminant colour. Usually, to obtain images invariant to the illumination conditions under which they were taken, color normalisation is used...... in their colours. We demonstrate the effectiveness of our method with qualitative and quantitative examples from the Caltech-101 data set and a real application of 3D pose estimation for robot grasping....

Large-scale weakly supervised object localization via latent category learning.

Science.gov (United States)

Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

2015-04-01

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

Static Members of Classes in C#

Directory of Open Access Journals (Sweden)

Adrian LUPASC

2017-12-01

Full Text Available The C# language is object-oriented, which is why the declared member data must be part of a class. Thus, there is no possibility to declare certain variables that can be accessed from anywhere within the application, as it happens, for example, with global variables at the C language level. Making this work in C# is possible through static members of the class. Declaring a class implies defining some of its member data that later receive values when creating each object. A static member of the class can be interpreted as belonging only to the class, not to the objects subsequently created, which means that for the non-static data, there are as many children as there were objects created, while for the static ones there is only one copy, regardless of the number of created objects. In this regard, this paper presents the main aspects that characterize these abstract concepts of object oriented programming in general and C# language in particular, detailing how to develop an application that includes both static and non-static members. At the same time, particularities in the mirror for the two types of data, restrictions on use and potential limitations are presented.

Fine-grained recognition of plants from images.

Science.gov (United States)

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Reader error, object recognition, and visual search

Science.gov (United States)

Kundel, Harold L.

2004-05-01

Small abnormalities such as hairline fractures, lung nodules and breast tumors are missed by competent radiologists with sufficient frequency to make them a matter of concern to the medical community; not only because they lead to litigation but also because they delay patient care. It is very easy to attribute misses to incompetence or inattention. To do so may be placing an unjustified stigma on the radiologists involved and may allow other radiologists to continue a false optimism that it can never happen to them. This review presents some of the fundamentals of visual system function that are relevant to understanding the search for and the recognition of small targets embedded in complicated but meaningful backgrounds like chests and mammograms. It presents a model for visual search that postulates a pre-attentive global analysis of the retinal image followed by foveal checking fixations and eventually discovery scanning. The model will be used to differentiate errors of search, recognition and decision making. The implications for computer aided diagnosis and for functional workstation design are discussed.

Now you see it, now you don’t: The context dependent nature of category-effects in visual object recognition

DEFF Research Database (Denmark)

Gerlach, Christian; Toft, Kristian Olesen

2011-01-01

In two experiments, we test predictions regarding processing advantages/disadvantages for natural objects and artefacts in visual object recognition. Varying three important parameters*degree of perceptual differentiation, stimulus format, and stimulus exposure duration*we show how different......-effects are products of common operations which are differentially affected by the structural similarity among objects (with natural objects being more structurally similar than artefacts). The potentially most important aspect of the present study is the demonstration that category-effects are very context dependent...

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

Science.gov (United States)

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

Conversion of short-term to long-term memory in the novel object recognition paradigm.

Science.gov (United States)

Moore, Shannon J; Deshpande, Kaivalya; Stinnett, Gwen S; Seasholtz, Audrey F; Murphy, Geoffrey G

2013-10-01

It is well-known that stress can significantly impact learning; however, whether this effect facilitates or impairs the resultant memory depends on the characteristics of the stressor. Investigation of these dynamics can be confounded by the role of the stressor in motivating performance in a task. Positing a cohesive model of the effect of stress on learning and memory necessitates elucidating the consequences of stressful stimuli independently from task-specific functions. Therefore, the goal of this study was to examine the effect of manipulating a task-independent stressor (elevated light level) on short-term and long-term memory in the novel object recognition paradigm. Short-term memory was elicited in both low light and high light conditions, but long-term memory specifically required high light conditions during the acquisition phase (familiarization trial) and was independent of the light level during retrieval (test trial). Additionally, long-term memory appeared to be independent of stress-mediated glucocorticoid release, as both low and high light produced similar levels of plasma corticosterone, which further did not correlate with subsequent memory performance. Finally, both short-term and long-term memory showed no savings between repeated experiments suggesting that this novel object recognition paradigm may be useful for longitudinal studies, particularly when investigating treatments to stabilize or enhance weak memories in neurodegenerative diseases or during age-related cognitive decline. Copyright © 2013 Elsevier Inc. All rights reserved.

Structure and Evolution of Kuiper Belt Objects: The Case for Compositional Classes

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McKinnon, William B.; Prialnik, D.; Stern, S. A.

2007-10-01

Kuiper belt objects (KBOs) accreted from a mélange of ices, carbonaceous matter, and rock of mixed interstellar and solar nebular provenance. The transneptunian region, where this accretion took place, was likely more radially compact than today. This and the influence of gas drag during the solar nebula epoch argue for more rapid KBO accretion than usually considered. Early evolution of KBOs was largely the result of radiogenic heating, with both short-term and long-term contributions being potentially important. Depending on rock content and porous conductivity, KBO interiors may have reached relatively high temperatures. Models suggest that KBOs likely lost very volatile ices during early evolution, whereas less volatile ices should be retained in cold, less altered subsurface layers; initially amorphous ice may have crystallized in the interior as well, releasing trapped volatiles. Generally, KBOs should be stratified in terms of composition and porosity, albeit subject to impact disruption and collisional stripping. KBOs are thus unlikely to be "the most pristine objects in the Solar System.” Large (dwarf planet) KBOs may be fully differentiated. KBO surface color and compositional classes are usually discussed in terms of "nature vs. nurture,” i.e., a generic primordial composition vs. surface processing, but the true nature of KBOs also depends on how they have evolved. The broad range of albedos now found in the Kuiper belt, deep water-ice absorptions on some objects, evidence for differentiation of Pluto and 2003 EL61, and a range of densities incompatible with a single, primordial composition and variable porosity strongly imply significant, intrinsic compositional differences among KBOs. The interplay of formation zone (accretion rate), body size, and dynamical (collisional) history may yield KBO compositional classes (and their spectral correlates) that recall the different classes of asteroids in the inner Solar System, but whose members are

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

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Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

2018-04-01

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

Under what conditions is recognition spared relative to recall after selective hippocampal damage in humans?

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Holdstock, J S; Mayes, A R; Roberts, N; Cezayirli, E; Isaac, C L; O'Reilly, R C; Norman, K A

2002-01-01

The claim that recognition memory is spared relative to recall after focal hippocampal damage has been disputed in the literature. We examined this claim by investigating object and object-location recall and recognition memory in a patient, YR, who has adult-onset selective hippocampal damage. Our aim was to identify the conditions under which recognition was spared relative to recall in this patient. She showed unimpaired forced-choice object recognition but clearly impaired recall, even when her control subjects found the object recognition task to be numerically harder than the object recall task. However, on two other recognition tests, YR's performance was not relatively spared. First, she was clearly impaired at an equivalently difficult yes/no object recognition task, but only when targets and foils were very similar. Second, YR was clearly impaired at forced-choice recognition of object-location associations. This impairment was also unrelated to difficulty because this task was no more difficult than the forced-choice object recognition task for control subjects. The clear impairment of yes/no, but not of forced-choice, object recognition after focal hippocampal damage, when targets and foils are very similar, is predicted by the neural network-based Complementary Learning Systems model of recognition. This model postulates that recognition is mediated by hippocampally dependent recollection and cortically dependent familiarity; thus hippocampal damage should not impair item familiarity. The model postulates that familiarity is ineffective when very similar targets and foils are shown one at a time and subjects have to identify which items are old (yes/no recognition). In contrast, familiarity is effective in discriminating which of similar targets and foils, seen together, is old (forced-choice recognition). Independent evidence from the remember/know procedure also indicates that YR's familiarity is normal. The Complementary Learning Systems model can

Fast and flexible 3D object recognition solutions for machine vision applications

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Effenberger, Ira; Kühnle, Jens; Verl, Alexander

2013-03-01

In automation and handling engineering, supplying work pieces between different stages along the production process chain is of special interest. Often the parts are stored unordered in bins or lattice boxes and hence have to be separated and ordered for feeding purposes. An alternative to complex and spacious mechanical systems such as bowl feeders or conveyor belts, which are typically adapted to the parts' geometry, is using a robot to grip the work pieces out of a bin or from a belt. Such applications are in need of reliable and precise computer-aided object detection and localization systems. For a restricted range of parts, there exists a variety of 2D image processing algorithms that solve the recognition problem. However, these methods are often not well suited for the localization of randomly stored parts. In this paper we present a fast and flexible 3D object recognizer that localizes objects by identifying primitive features within the objects. Since technical work pieces typically consist to a substantial degree of geometric primitives such as planes, cylinders and cones, such features usually carry enough information in order to determine the position of the entire object. Our algorithms use 3D best-fitting combined with an intelligent data pre-processing step. The capability and performance of this approach is shown by applying the algorithms to real data sets of different industrial test parts in a prototypical bin picking demonstration system.

Constant Light Desynchronizes Olfactory versus Object and Visuospatial Recognition Memory Performance.

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Tam, Shu K E; Hasan, Sibah; Choi, Harry M C; Brown, Laurence A; Jagannath, Aarti; Hughes, Steven; Hankins, Mark W; Foster, Russell G; Vyazovskiy, Vladyslav V; Bannerman, David M; Peirson, Stuart N

2017-03-29

Circadian rhythms optimize physiology and behavior to the varying demands of the 24 h day. The master circadian clock is located in the suprachiasmatic nuclei (SCN) of the hypothalamus and it regulates circadian oscillators in tissues throughout the body to prevent internal desynchrony. Here, we demonstrate for the first time that, under standard 12 h:12 h light/dark (LD) cycles, object, visuospatial, and olfactory recognition performance in C57BL/6J mice is consistently better at midday relative to midnight. However, under repeated exposure to constant light ( r LL), recognition performance becomes desynchronized, with object and visuospatial performance better at subjective midday and olfactory performance better at subjective midnight. This desynchrony in behavioral performance is mirrored by changes in expression of the canonical clock genes Period1 and Period2 ( Per1 and Per2 ), as well as the immediate-early gene Fos in the SCN, dorsal hippocampus, and olfactory bulb. Under r LL, rhythmic Per1 and Fos expression is attenuated in the SCN. In contrast, hippocampal gene expression remains rhythmic, mirroring object and visuospatial performance. Strikingly, Per1 and Fos expression in the olfactory bulb is reversed, mirroring the inverted olfactory performance. Temporal desynchrony among these regions does not result in arrhythmicity because core body temperature and exploratory activity rhythms persist under r LL. Our data provide the first demonstration that abnormal lighting conditions can give rise to temporal desynchrony between autonomous circadian oscillators in different regions, with different consequences for performance across different sensory domains. Such a dispersed network of dissociable circadian oscillators may provide greater flexibility when faced with conflicting environmental signals. SIGNIFICANCE STATEMENT A master circadian clock in the suprachiasmatic nuclei (SCN) of the hypothalamus regulates physiology and behavior across the 24 h day by

Constant Light Desynchronizes Olfactory versus Object and Visuospatial Recognition Memory Performance

Science.gov (United States)

Tam, Shu K.E.; Hasan, Sibah; Brown, Laurence A.; Jagannath, Aarti; Hankins, Mark W.; Foster, Russell G.; Vyazovskiy, Vladyslav V.

2017-01-01

Circadian rhythms optimize physiology and behavior to the varying demands of the 24 h day. The master circadian clock is located in the suprachiasmatic nuclei (SCN) of the hypothalamus and it regulates circadian oscillators in tissues throughout the body to prevent internal desynchrony. Here, we demonstrate for the first time that, under standard 12 h:12 h light/dark (LD) cycles, object, visuospatial, and olfactory recognition performance in C57BL/6J mice is consistently better at midday relative to midnight. However, under repeated exposure to constant light (rLL), recognition performance becomes desynchronized, with object and visuospatial performance better at subjective midday and olfactory performance better at subjective midnight. This desynchrony in behavioral performance is mirrored by changes in expression of the canonical clock genes Period1 and Period2 (Per1 and Per2), as well as the immediate-early gene Fos in the SCN, dorsal hippocampus, and olfactory bulb. Under rLL, rhythmic Per1 and Fos expression is attenuated in the SCN. In contrast, hippocampal gene expression remains rhythmic, mirroring object and visuospatial performance. Strikingly, Per1 and Fos expression in the olfactory bulb is reversed, mirroring the inverted olfactory performance. Temporal desynchrony among these regions does not result in arrhythmicity because core body temperature and exploratory activity rhythms persist under rLL. Our data provide the first demonstration that abnormal lighting conditions can give rise to temporal desynchrony between autonomous circadian oscillators in different regions, with different consequences for performance across different sensory domains. Such a dispersed network of dissociable circadian oscillators may provide greater flexibility when faced with conflicting environmental signals. SIGNIFICANCE STATEMENT A master circadian clock in the suprachiasmatic nuclei (SCN) of the hypothalamus regulates physiology and behavior across the 24 h day by

Translation Ambiguity but Not Word Class Predicts Translation Performance

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Prior, Anat; Kroll, Judith F.; Macwhinney, Brian

2013-01-01

We investigated the influence of word class and translation ambiguity on cross-linguistic representation and processing. Bilingual speakers of English and Spanish performed translation production and translation recognition tasks on nouns and verbs in both languages. Words either had a single translation or more than one translation. Translation…

A Temporally Distinct Role for Group I and Group II Metabotropic Glutamate Receptors in Object Recognition Memory

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Brown, Malcolm Watson; Warburton, Elizabeth Clea; Barker, Gareth Robert Isaac; Bashir, Zafar Iqbal

2006-01-01

Recognition memory, involving the ability to discriminate between a novel and familiar object, depends on the integrity of the perirhinal cortex (PRH). Glutamate, the main excitatory neurotransmitter in the cortex, is essential for many types of memory processes. Of the subtypes of glutamate receptor, metabotropic receptors (mGluRs) have received…

Object Oriented Programming in Director

Directory of Open Access Journals (Sweden)

Marian DARDALA

2006-01-01

Full Text Available Director is one of the most popular authoring software. As software for developing multimedia applications, Director is an object oriented programming environment. A very important issue to develop multimedia applications is the designing of their own classes. This paper presents the particular aspects concerning the available facilities offered by Lingo to design classes and to generate objects.

Landsat TM band 431 combine on clustering analysis for pattern recognition land use using idrisi 4.2 software

International Nuclear Information System (INIS)

Wiweka, Arief H.; Izzawati, Tjahyaningsih A.

1997-01-01

The recognition of earth object's pattern which is recorded on remote sensing digital image can do by classification process based on the group of spectral pixel value. The spectral assessment on a spatial which represent the object characteristic can be helped through supervised or unsupervised. On certain case, there no media, such as maps, airborne, photo, the capability of field observation and the knowledge of object's location. Classification process can be done by clustering. The group of pixel based on the wide of the whole value interval of spectral image, then the class group base on the desired accuracy. The clustering method in Idris 4.2 software equipments are sequential method, statistic, iso data, and RGB. The clustering existence can help pre-process pattern recognition

Support Vector Data Descriptions and k-Means Clustering: One Class?

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Gornitz, Nico; Lima, Luiz Alberto; Muller, Klaus-Robert; Kloft, Marius; Nakajima, Shinichi

2017-09-27

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

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

Science.gov (United States)

Lee, Jae-Kyu