Invariant Recognition of Visual Objects
Invariant Recognition of Visual Objects

Author: Evgeniy Bart

Publisher: Frontiers E-books

Published:

Total Pages: 195

ISBN-13: 2889190765

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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
Visual Object Recognition

Author: Kristen Grauman

Publisher: Morgan & Claypool Publishers

Published: 2011

Total Pages: 184

ISBN-13: 1598299689

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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

Computer Vision - ECCV 2008
Computer Vision - ECCV 2008

Author: David Hutchison

Publisher:

Published: 2008

Total Pages: 0

ISBN-13: 9788354088684

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The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed proceedings of the 10th European Conference on Computer Vision, ECCV 2008, held in Marseille, France, in October 2008. The 243 revised papers presented were carefully reviewed and selected from a total of 871 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, stereo, people and face recognition, object tracking, matching, learning and features, MRFs, segmentation, computational photography and active reconstruction.

Moments and Moment Invariants in Pattern Recognition
Moments and Moment Invariants in Pattern Recognition

Author: Jan Flusser

Publisher: John Wiley & Sons

Published: 2009-11-04

Total Pages: 312

ISBN-13: 9780470684764

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Moments as projections of an image’s intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging. Key features: Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments. Considers invariants to traditional transforms – translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants. Reviews and extends a recent field of invariants with respect to convolution/blurring. Introduces implicit moment invariants as a tool for recognizing elastically deformed objects. Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments. Offers comprehensive advice on the construction of various invariants illustrated with practical examples. Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course. Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.

Geometric Invariance in Computer Vision
Geometric Invariance in Computer Vision

Author: Joseph L. Mundy

Publisher:

Published: 1992

Total Pages: 568

ISBN-13:

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These twenty-three contributions focus on the most recent developments in the rapidly evolving field of geometric invariants and their application to computer vision. The introduction summarizes the basics of invariant theory, discusses how invariants are related to problems in computer vision, and looks at the future possibilities, particularly the notion that invariant analysis might provide a solution to the elusive problem of recognizing general curved 3D objects from an arbitrary viewpoint. The remaining chapters consist of original papers that present important developments as well as tutorial articles that provide useful background material. These chapters are grouped into categories covering algebraic invariants, nonalgebraic invariants, invariants of multiple views, and applications. An appendix provides an extensive introduction to projective geometry and its applications to basic problems in computer vision.

Perception of Faces, Objects, and Scenes
Perception of Faces, Objects, and Scenes

Author: Mary A. Peterson

Publisher: Oxford University Press

Published: 2003-05-22

Total Pages: 410

ISBN-13: 9780195347418

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From a barrage of photons, we readily and effortlessly recognize the faces of our friends, and the familiar objects and scenes around us. However, these tasks cannot be simple for our visual systems--faces are all extremely similar as visual patterns, and objects look quite different when viewed from different viewpoints. How do our visual systems solve these problems? The contributors to this volume seek to answer this question by exploring how analytic and holistic processes contribute to our perception of faces, objects, and scenes. The role of parts and wholes in perception has been studied for a century, beginning with the debate between Structuralists, who championed the role of elements, and Gestalt psychologists, who argued that the whole was different from the sum of its parts. This is the first volume to focus on the current state of the debate on parts versus wholes as it exists in the field of visual perception by bringing together the views of the leading researchers. Too frequently, researchers work in only one domain, so they are unaware of the ways in which holistic and analytic processing are defined in different areas. The contributors to this volume ask what analytic and holistic processes are like; whether they contribute differently to the perception of faces, objects, and scenes; whether different cognitive and neural mechanisms code holistic and analytic information; whether a single, universal system can be sufficient for visual-information processing, and whether our subjective experience of holistic perception might be nothing more than a compelling illusion. The result is a snapshot of the current thinking on how the processing of wholes and parts contributes to our remarkable ability to recognize faces, objects, and scenes, and an illustration of the diverse conceptions of analytic and holistic processing that currently coexist, and the variety of approaches that have been brought to bear on the issues.

Hierarchical Object Representations in the Visual Cortex and Computer Vision
Hierarchical Object Representations in the Visual Cortex and Computer Vision

Author: Antonio Rodríguez-Sánchez

Publisher: Frontiers Media SA

Published: 2016-06-08

Total Pages: 292

ISBN-13: 2889197980

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Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations.

Handbook Of Pattern Recognition And Computer Vision (2nd Edition)
Handbook Of Pattern Recognition And Computer Vision (2nd Edition)

Author: Chi Hau Chen

Publisher: World Scientific

Published: 1999-03-12

Total Pages: 1045

ISBN-13: 9814497649

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The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.

Vision
Vision

Author: Carlo Musio

Publisher: World Scientific

Published: 2001

Total Pages: 510

ISBN-13: 9812799974

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The light sense is conceivably the key sense in both the animal and the plant kingdom. Vision research, undoubtedly a fast-growing field, is providing impressive results OCo thanks to modern theoretical and methodological advances. The approach of biophysics and neuroscience seems to be of great benefit and, for this reason, the present book gives an outline of recent acquisitions and updated advanced methods concerning this approach. Visual mechanisms and processes are analysed at several (molecular, cellular, integrative, computational and cognitive) levels by different methodologies (from molecular biology to computation) applied to different living models (from protists to humans, via invertebrates and lower vertebrates). Contents: The Optics of Animal Eyes (M F Land); Rhodopsin-Like Proteins: The Universal and Probably Unique Proteins for Vision (P Gualtieri); The Molecular Design of a Visual Cascade: Molecular Stages of Phototransduction in Drosophila (R Paulsen et al.); Molecular Changes During Primary Visual Pathway Development (K L Moya et al.); Color Vision and Retinal Randomness of the Japanese Yellow Swallowtail Butterfly, Papilio Xuthus (K Arikawa et al.); Patch-Clamping Solitary Visual Cells to Understand the Cellular Mechanisms of Invertebrate Phototransduction (C Musio); Phototransduction in Retinal Rods and Cones (Y Koutalos et al.); Formation of OC ONOCO and OC OFFOCO Ganglion Cell Mosaics (L M Chalupa); Endogenous Nitric Oxide Modulates Signal Transmission from Photoreceptors to On-Center Bipolar Cells in the Rabbit Retina (B Lei & I Perlman); Now You See It, Now You Don't: Shunting Inhibition in Early Vision (L Borg-Graham et al.); Visual Perceptual Learning (N Berardi & A Fiorentini); Functions of the Primate Temporal Lobe Cortical Visual Areas in Invariant Visual Object and Face Recognition (E T Rolls); Vector Code in Neuronal Networks (E N Sokolov); and other papers. Readership: Scientists and postdoctoral students in neurosciences, biophysics and physiology."