scholarly journals Recognition Model with Extension Fields

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 202-202
Author(s):  
P Kalocsai ◽  
W I Biederman
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Primary Visual Cortex ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Gabor Filters ◽  
Shape Similarity ◽  
Recognition Model ◽  
Connectivity Patterns ◽  
Human Object

A recognition model which defines a measure of shape similarity on the direct output of multiscale and multiorientation Gabor filters does not manifest qualitative aspects of human object recognition of contour-deleted images in that: (a) it recognises recoverable and nonrecoverable contour-deleted images equally well, whereas humans recognise recoverable images much better; (b) it distinguishes complementary feature-deleted images whereas humans do not. Adding some of the known connectivity patterns of the primary visual cortex to the model in the form of extension fields (connections between collinear and curvilinear units) among filters (a) increased the overall recognition performance of the model, (b) boosted the recognition rate of the recoverable images far more than of the nonrecoverable ones, (c) increased the similarity of complementary feature-deleted images, but not part-deleted ones. These correspond more closely to human psychophysical results. Interestingly, performance was approximately equivalent for narrow (±15 deg) and broad (±90 deg) extension fields.

Temporal Correlations in Presentation Order during Learning Affects Human Object Recognition

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 33-33
Author(s):  
G M Wallis ◽  
H H Bülthoff
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Recognition Performance ◽  
Presentation Order ◽  
Temporal Association ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Human Object ◽  
Network Simulations ◽  
Recognition Learning

The view-based approach to object recognition supposes that objects are stored as a series of associated views. Although representation of these views as combinations of 2-D features allows generalisation to similar views, it remains unclear how very different views might be associated together to allow recognition from any viewpoint. One cue present in the real world other than spatial similarity, is that we usually experience different objects in temporally constrained, coherent order, and not as randomly ordered snapshots. In a series of recent neural-network simulations, Wallis and Baddeley (1997 Neural Computation9 883 – 894) describe how the association of views on the basis of temporal as well as spatial correlations is both theoretically advantageous and biologically plausible. We describe an experiment aimed at testing their hypothesis in human object-recognition learning. We investigated recognition performance of faces previously presented in sequences. These sequences consisted of five views of five different people's faces, presented in orderly sequence from left to right profile in 45° steps. According to the temporal-association hypothesis, the visual system should associate the images together and represent them as different views of the same person's face, although in truth they are images of different people's faces. In a same/different task, subjects were asked to say whether two faces seen from different viewpoints were views of the same person or not. In accordance with theory, discrimination errors increased for those faces seen earlier in the same sequence as compared with those faces which were not ( p<0.05).

scholarly journals Fourier Descriptors Based on the Structure of the Human Primary Visual Cortex with Applications to Object Recognition

2016 ◽  
Vol 57 (1) ◽  
pp. 117-133 ◽  
Author(s):  
Amine Bohi ◽  
Dario Prandi ◽  
Vincente Guis ◽  
Frédéric Bouchara ◽  
Jean-Paul Gauthier
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Primary Visual Cortex ◽  
Fourier Descriptors

scholarly journals A performance-optimized model of neural responses across the ventral visual stream

2016 ◽  
Author(s):  
Darren Seibert ◽  
Daniel L Yamins ◽  
Diego Ardila ◽  
Ha Hong ◽  
James J DiCarlo ◽  
...  
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Object Recognition ◽  
Recognition Performance ◽  
Ventral Stream ◽  
Visual Object ◽  
Emergent Properties ◽  
Visual Stream ◽  
Response Properties ◽  
High Level

Human visual object recognition is subserved by a multitude of cortical areas. To make sense of this system, one line of research focused on response properties of primary visual cortex neurons and developed theoretical models of a set of canonical computations such as convolution, thresholding, exponentiating and normalization that could be hierarchically repeated to give rise to more complex representations. Another line or research focused on response properties of high-level visual cortex and linked these to semantic categories useful for object recognition. Here, we hypothesized that the panoply of visual representations in the human ventral stream may be understood as emergent properties of a system constrained both by simple canonical computations and by top-level, object recognition functionality in a single unified framework (Yamins et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Guclu and van Gerven, 2015). We built a deep convolutional neural network model optimized for object recognition and compared representations at various model levels using representational similarity analysis to human functional imaging responses elicited from viewing hundreds of image stimuli. Neural network layers developed representations that corresponded in a hierarchical consistent fashion to visual areas from V1 to LOC. This correspondence increased with optimization of the model's recognition performance. These findings support a unified view of the ventral stream in which representations from the earliest to the latest stages can be understood as being built from basic computations inspired by modeling of early visual cortex shaped by optimization for high-level object-based performance constraints.

Minimal Recognizable Configurations Elicit Category-selective Responses in Higher Order Visual Cortex

2019 ◽  
Vol 31 (9) ◽  
pp. 1354-1367
Author(s):  
Yael Holzinger ◽  
Shimon Ullman ◽  
Daniel Harari ◽  
Marlene Behrmann ◽  
Galia Avidan
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Visual Recognition ◽  
Recognition Performance ◽  
Occipital Cortex ◽  
Building Blocks ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Face Area ◽  
Early Visual Cortex

Visual object recognition is performed effortlessly by humans notwithstanding the fact that it requires a series of complex computations, which are, as yet, not well understood. Here, we tested a novel account of the representations used for visual recognition and their neural correlates using fMRI. The rationale is based on previous research showing that a set of representations, termed “minimal recognizable configurations” (MIRCs), which are computationally derived and have unique psychophysical characteristics, serve as the building blocks of object recognition. We contrasted the BOLD responses elicited by MIRC images, derived from different categories (faces, objects, and places), sub-MIRCs, which are visually similar to MIRCs, but, instead, result in poor recognition and scrambled, unrecognizable images. Stimuli were presented in blocks, and participants indicated yes/no recognition for each image. We confirmed that MIRCs elicited higher recognition performance compared to sub-MIRCs for all three categories. Whereas fMRI activation in early visual cortex for both MIRCs and sub-MIRCs of each category did not differ from that elicited by scrambled images, high-level visual regions exhibited overall greater activation for MIRCs compared to sub-MIRCs or scrambled images. Moreover, MIRCs and sub-MIRCs from each category elicited enhanced activation in corresponding category-selective regions including fusiform face area and occipital face area (faces), lateral occipital cortex (objects), and parahippocampal place area and transverse occipital sulcus (places). These findings reveal the psychological and neural relevance of MIRCs and enable us to make progress in developing a more complete account of object recognition.

scholarly journals Perceptual Expertise and Top–Down Expectation of Musical Notation Engages the Primary Visual Cortex

2014 ◽  
Vol 26 (8) ◽  
pp. 1629-1643 ◽  
Author(s):  
Yetta Kwailing Wong ◽  
Cynthia Peng ◽  
Kristyn N. Fratus ◽  
Geoffrey F. Woodman ◽  
Isabel Gauthier
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Reading Ability ◽  
Stimulus Onset ◽  
Perceptual Expertise ◽  
Stimulus Category ◽  
Music Reading ◽  
Musical Notation

Most theories of visual processing propose that object recognition is achieved in higher visual cortex. However, we show that category selectivity for musical notation can be observed in the first ERP component called the C1 (measured 40–60 msec after stimulus onset) with music-reading expertise. Moreover, the C1 note selectivity was observed only when the stimulus category was blocked but not when the stimulus category was randomized. Under blocking, the C1 activity for notes predicted individual music-reading ability, and behavioral judgments of musical stimuli reflected music-reading skill. Our results challenge current theories of object recognition, indicating that the primary visual cortex can be selective for musical notation within the initial feedforward sweep of activity with perceptual expertise and with a testing context that is consistent with the expertise training, such as blocking the stimulus category for music reading.

Haptic Shape Processing in Visual Cortex

2014 ◽  
Vol 26 (5) ◽  
pp. 1154-1167 ◽  
Author(s):  
Jacqueline C. Snow ◽  
Lars Strother ◽  
Glyn W. Humphreys
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Primary Visual Cortex ◽  
Shape Recognition ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Object Shape ◽  
Shape Processing ◽  
Shape Selective ◽  
Visual Cortical

Humans typically rely upon vision to identify object shape, but we can also recognize shape via touch (haptics). Our haptic shape recognition ability raises an intriguing question: To what extent do visual cortical shape recognition mechanisms support haptic object recognition? We addressed this question using a haptic fMRI repetition design, which allowed us to identify neuronal populations sensitive to the shape of objects that were touched but not seen. In addition to the expected shape-selective fMRI responses in dorsal frontoparietal areas, we observed widespread shape-selective responses in the ventral visual cortical pathway, including primary visual cortex. Our results indicate that shape processing via touch engages many of the same neural mechanisms as visual object recognition. The shape-specific repetition effects we observed in primary visual cortex show that visual sensory areas are engaged during the haptic exploration of object shape, even in the absence of concurrent shape-related visual input. Our results complement related findings in visually deprived individuals and highlight the fundamental role of the visual system in the processing of object shape.

Visual Noise Consisting of X-Junctions Has Only a Minimal Effect on Object Recognition

2019 ◽  
Author(s):  
Eshed Margalit ◽  
Sarah B. Herald ◽  
Emily X. Meschke ◽  
Isabel Irawan ◽  
Rafael Maarek ◽  
...  
Keyword(s):  
Object Recognition ◽  
Strong Evidence ◽  
Deleterious Effect ◽  
Recognition Performance ◽  
Affect Recognition ◽  
Minimal Effect ◽  
Shape Features ◽  
Line Drawings ◽  
Change Of Direction ◽  
Human Object

In 1968 Guzman showed how the myriad of surfaces composing a highly complex and novel assemblage of volumes can be readily assigned to their appropriate volumes in terms of the constraints offered by the vertices of coterminating edges. Of particular importance was the L-vertex, produced by the cotermination of two contours, which provides strong evidence for the termination of a 2D surface. An X-junction, formed by the crossing of two contours without a change of direction at the crossing, played no role in the segmentation of the scene. If the potency of noise elements to affect recognition performance reflected their relevancy to the segmentation of scenes, as suggested by Guzman, X-junctions would be expected to have little or no effect on shape-based object recognition whereas L-junctions would be expected to have a strong deleterious effect when disrupting the smooth continuation of contours. Guzman’s roles for the various vertices and junctions have never been put to systematic test with respect to human object recognition. By adding identical noise contours to line drawings of objects that produced either L-vertices or X-junctions, these shape features could be compared with respect to their disruption of object recognition. Guzman’s insights that irrelevant L-vertices should be disruptive and irrelevant X-vertices would have minimal effect were confirmed.

scholarly journals Developing a Modified HMAX Model Based on Combined with the Visual Featured Model

2017 ◽  
Vol 7 (3) ◽  
pp. 773
Author(s):  
Yaghoub Pourasad
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Visual System ◽  
Human Visual System ◽  
Hierarchical Models ◽  
Recognition Rate ◽  
Basic Operation ◽  
High Complexity ◽  
Proposed Model ◽  
Computational Speed

<p>Identify objects based on modeling the human visual system, as an effective method in intelligent identification, has attracted the attention of many researchers. Although the machines have high computational speed but are very weak as compared to humans in terms of diagnosis. Experience has shown that in many areas of image processing, algorithms that have biological backing had more simplicity and better performance. The human visual system, first select the main parts of the image which is provided by the visual featured model, then pays to object recognition which is a hierarchical operations according to this, HMAX model is also provided. HMAX object recognition model from the group of hierarchical models without feedback that its structure and parameters selected based on biological characteristics of the visual cortex. This model is a hierarchical model neural network with four layers, is composed of alternating layers that are simple and complex. Due to the high complexity of the human visual system is virtually impossible to replicate it. For each of the above, separate models have been proposed but in the human visual system, this operation is performed seamlessly, thus, by combining the principles of these models is expected to be closer to the human visual system and obtain a higher recognition rate. In this paper, we introduce an architecture to classify images based on a combination of previous work is based on the basic operation of the visual cortex. According to the results presented, the proposed model compared with the main HMAX model has a much higher recognition rate. Simulations was performed on the database of Caltech101.</p>

scholarly journals Double-Gabor Filters Are Independent Components of Small Translation-Invariant Image Patches

2013 ◽  
Vol 25 (4) ◽  
pp. 922-939 ◽  
Author(s):  
Saeed Saremi ◽  
Terrence J. Sejnowski ◽  
Tatyana O. Sharpee
Keyword(s):  
Visual Cortex ◽  
Independent Component Analysis ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Gabor Filters ◽  
Natural Images ◽  
Invariant Representation ◽  
Translation Invariant ◽  
Independent Components ◽  
Image Patches

The analysis of natural images with independent component analysis (ICA) yields localized bandpass Gabor-type filters similar to receptive fields of simple cells in visual cortex. We applied ICA on a subset of patches called position-centered patches, selected for forming a translation-invariant representation of small patches. The resulting filters were qualitatively different in two respects. One novel feature was the emergence of filters we call double-Gabor filters. In contrast to Gabor functions that are modulated in one direction, double-Gabor filters are sinusoidally modulated in two orthogonal directions. In addition the filters were more extended in space and frequency compared to standard ICA filters and better matched the distribution in experimental recordings from neurons in primary visual cortex. We further found a dual role for double-Gabor filters as edge and texture detectors, which could have engineering applications.

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