scholarly journals Online Learning for Object Recognition with a Hierarchical Visual Cortex Model

2005 ◽  
pp. 487-492 ◽  
Author(s):  
Stephan Kirstein ◽  
Heiko Wersing ◽  
Edgar Körner
Keyword(s):  
Online Learning ◽  
Visual Cortex ◽  
Object Recognition

Role of Layer 6 of V2 Visual Cortex in Object-Recognition Memory

Science ◽  
2009 ◽  
Vol 325 (5936) ◽  
pp. 87-89 ◽  
Author(s):  
Manuel F. López-Aranda ◽  
Juan F. López-Téllez ◽  
Irene Navarro-Lobato ◽  
Mariam Masmudi-Martín ◽  
Antonia Gutiérrez ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Recognition Memory ◽  
Object Recognition Memory

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.

scholarly journals The number sense is an emergent property of a deep convolutional neural network trained for object recognition

2019 ◽  
Author(s):  
Nicholas K. DeWind
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Number Sense ◽  
Emergent Property ◽  
Deep Convolutional Neural Network ◽  
Mathematical Concepts ◽  
Representational Space

SummaryHumans and many non-human animals have the “number sense,” an ability to estimate the number of items in a set without counting. This innate sense of number is hypothesized to provide a foundation for more complex numerical and mathematical concepts. Here I investigated whether we also share the number sense with a deep convolutional neural network (DCNN) trained for object recognition. These in silico networks have revolutionized machine learning over the last seven years, allowing computers to reach human-level performance on object recognition tasks for the first time. Their architecture is based on the structure of mammalian visual cortex, and after they are trained, they provide a highly predictive model of responses in primate visual cortex, suggesting deep homologies. I found that the DCNN demonstrates three key hallmarks of the number sense: numerosity-selective units (analogous to biological neurons), the behavioral ratio effect, and ordinality over representational space. Because the DCNN was not trained to enumerate, I conclude that the number sense is an emergent property of the network, the result of some combination of the network architecture and the constraint to develop the complex representational structure necessary for object recognition. By analogy I conclude that the number sense in animals was not necessarily the result of direct selective pressure to enumerate but might have “come for free” with the evolution of a complex visual system that evolved to identify objects and scenes in the real world.

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.

Visual Cortex Models for Object Recognition

2021 ◽  
pp. 1359-1363
Author(s):  
Tomaso Poggio ◽  
Shimon Ullman
Keyword(s):  
Visual Cortex ◽  
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.

scholarly journals Adaptive Computer Vision: Online Learning for Object Recognition

2004 ◽  
pp. 447-454 ◽  
Author(s):  
Holger Bekel ◽  
Ingo Bax ◽  
Gunther Heidemann ◽  
Helge Ritter
Keyword(s):  
Computer Vision ◽  
Online Learning ◽  
Object Recognition

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.

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