Invariant Recognition Of Visual Objects
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Invariant Recognition Of Visual Objects
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Author : Evgeniy Bart
language : en
Publisher: Frontiers E-books
Release Date :
Invariant Recognition Of Visual Objects written by Evgeniy Bart and has been published by Frontiers E-books this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
This Research Topic will focus on how the visual system recognizes objects regardless of variations in the viewpoint, illumination, retinal size, background, etc. Contributors are encouraged to submit articles describing novel results, models, viewpoints, perspectives and/or methodological innovations relevant to this topic. The issues we wish to cover include, but are not limited to, perceptual invariance under one or more of the following types of image variation: • Object shape • Task • Viewpoint (from the translation and rotation of the object relative to the viewer) • Illumination, shading, and shadows • Degree of occlusion • Retinal size • Color • Surface texture • Visual context, including background clutter and crowding • Object motion (including biological motion). Examples of questions that are particularly interesting in this context include, but are not limited to: • Empirical characterizations of properties of invariance: does invariance always exist? How wide is its range and how strong is the tolerance to viewing conditions within this range? • Invariance in naïve vs. experienced subjects: Is invariance built-in or learned? How can it be learned, under which conditions and how effectively? Is it learned incidentally, or are specific task and reward structures necessary for learning? How is generalizability and transfer of learning related to the generalizability/invariance of perception? • Invariance during inference: Are there conditions (e.g. fast presentation time or otherwise resource-constrained recognition) when invariance breaks? • What are some plausible computational or neural mechanisms by which invariance could be achieved?
Visual Object Recognition
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Author : Kristen Gauman
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2010-10-10
Visual Object Recognition written by Kristen Gauman and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-10 with Technology & Engineering categories.
The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
Integrating Computational And Neural Findings In Visual Object Perception
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Author : Judith C. Peters
language : en
Publisher: Frontiers Media SA
Release Date : 2016-06-29
Integrating Computational And Neural Findings In Visual Object Perception written by Judith C. Peters and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-29 with Electronic book categories.
The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.
The Dynamics Of Invariant Object And Action Recognition In The Human Visual System
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Author : Leyla Isik
language : en
Publisher:
Release Date : 2015
The Dynamics Of Invariant Object And Action Recognition In The Human Visual System written by Leyla Isik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Humans can quickly and effortlessly recognize objects, and people and their actions from complex visual inputs. Despite the ease with which the human brain solves this problem, the underlying computational steps have remained enigmatic. What makes object and action recognition challenging are identity-preserving transformations that alter the visual appearance of objects and actions, such as changes in scale, position, and viewpoint. The majority of visual neuroscience studies examining visual recognition either use physiology recordings, which provide high spatiotemporal resolution data with limited brain coverage, or functional MRI, which provides high spatial resolution data from across the brain with limited temporal resolution. High temporal resolution data from across the brain is needed to break down and understand the computational steps underlying invariant visual recognition. In this thesis I use magenetoencephalography, machine learning, and computational modeling to study invariant visual recognition. I show that a temporal association learning rule for learning invariance in hierarchical visual systems is very robust to manipulations and visual disputations that happen during development (Chapter 2). I next show that object recognition occurs very quickly, with invariance to size and position developing in stages beginning around 100ms after stimulus onset (Chapter 3), and that action recognition occurs on a similarly fast time scale, 200 ms after video onset, with this early representation being invariant to changes in actor and viewpoint (Chapter 4). Finally, I show that the same hierarchical feedforward model can explain both the object and action recognition timing results, putting this timing data in the broader context of computer vision systems and models of the brain. This work sheds light on the computational mechanisms underlying invariant object and action recognition in the brain and demonstrates the importance of using high temporal resolution data to understand neural computations.
Invariant Object Recognition Based On Elastic Graph Matching
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Author : Raymond S. T. Lee
language : en
Publisher:
Release Date : 2003
Invariant Object Recognition Based On Elastic Graph Matching written by Raymond S. T. Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computer vision categories.
The Role Of Viewpoint Invariant Properties In Visual Object Recognition
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Author : Harold John Hilton
language : en
Publisher:
Release Date : 1995
The Role Of Viewpoint Invariant Properties In Visual Object Recognition written by Harold John Hilton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
The Role Of Attention In Viewpoint Invariant Object Recognition
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Author : Brian John Stankiewicz
language : en
Publisher:
Release Date : 1997
The Role Of Attention In Viewpoint Invariant Object Recognition written by Brian John Stankiewicz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.
Object Recognition In Man Monkey And Machine
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Author : Michael J. Tarr
language : en
Publisher: MIT Press
Release Date : 1999-03-15
Object Recognition In Man Monkey And Machine written by Michael J. Tarr and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-03-15 with Psychology categories.
The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition. These interconnected essays on three-dimensional visual object recognition present cutting-edge research by some of the most creative neuroscientific, cognitive, and computational scientists in the field. Cassandra Moore and Patrick Cavanagh take a classic demonstration, the perception of "two-tone" images, and turn it into a method for understanding the nature of object representations in terms of surfaces and the interaction between bottom-up and top-down processes. Michael J. Tarr and Isabel Gauthier use computer graphics to study whether viewpoint-dependent recognition mechanisms can generalize between exemplars of perceptually defined classes. Melvyn A. Goodale and G. Keith Humphrey use innovative psychophysical techniques to investigate dissociable aspects of visual and spatial processing in brain-injured subjects. D.I. Perrett, M.W. Oram, and E. Ashbridge combine neurophysiological single-cell data from monkeys with computational analyses for a new way of thinking about the mechanisms that mediate viewpoint-dependent object recognition and mental rotation. Shimon Ullman also addresses possible mechanisms to account for viewpoint-dependent behavior, but from the perspective of machine vision. Finally, Philippe G. Schyns synthesizes work from many areas, to provide a coherent account of how stimulus class and recognition task interact. The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition.
Toward Category Level Object Recognition
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Author : Jean Ponce
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-12-22
Toward Category Level Object Recognition written by Jean Ponce and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-12-22 with Computers categories.
This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.
Representation And Recognition In Vision
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Author : Shimon Edelman
language : en
Publisher: MIT Press
Release Date : 1999
Representation And Recognition In Vision written by Shimon Edelman and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.
Shimon Edelman bases a comprehensive approach to visual representation on the notion of correspondence between proximal (internal) and distal similarities in objects. Researchers have long sought to understand what the brain does when we see an object, what two people have in common when they see the same object, and what a "seeing" machine would need to have in common with a human visual system. Recent neurobiological and computational advances in the study of vision have now brought us close to answering these and other questions about representation. In Representation and Recognition in Vision, Shimon Edelman bases a comprehensive approach to visual representation on the notion of correspondence between proximal (internal) and distal similarities in objects. This leads to a computationally feasible and formally veridical representation of distal objects that addresses the needs of shape categorization and can be used to derive models of perceived similarity. Edelman first discusses the representational needs of various visual recognition tasks, and surveys current theories of representation in this context. He then develops a theory of representation that is related to Shepard's notion of second-order isomorphism between representations and their targets. Edelman goes beyond Shepard by specifying the conditions under which the representations can be made formally veridical. Edelman assesses his theory's performance in identification and categorization of 3D shapes and examines it in light of psychological and neurobiological data concerning the object-processing stream in primate vision. He also discusses the connections between his theory and other efforts to understand representation in the brain.