How Humans Recognize Objects: Segmentation, Categorization and Individual Identification

How Humans Recognize Objects: Segmentation, Categorization and Individual Identification PDF

Author: Chris Fields

Publisher: Frontiers Media SA

Published: 2016-08-18

Total Pages: 267

ISBN-13: 2889199401

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Human beings experience a world of objects: bounded entities that occupy space and persist through time. Our actions are directed toward objects, and our language describes objects. We categorize objects into kinds that have different typical properties and behaviors. We regard some kinds of objects – each other, for example – as animate agents capable of independent experience and action, while we regard other kinds of objects as inert. We re-identify objects, immediately and without conscious deliberation, after days or even years of non-observation, and often following changes in the features, locations, or contexts of the objects being re-identified. Comparative, developmental and adult observations using a variety of approaches and methods have yielded a detailed understanding of object detection and recognition by the visual system and an advancing understanding of haptic and auditory information processing. Many fundamental questions, however, remain unanswered. What, for example, physically constitutes an “object”? How do specific, classically-characterizable object boundaries emerge from the physical dynamics described by quantum theory, and can this emergence process be described independently of any assumptions regarding the perceptual capabilities of observers? How are visual motion and feature information combined to create object information? How are the object trajectories that indicate persistence to human observers implemented, and how are these trajectory representations bound to feature representations? How, for example, are point-light walkers recognized as single objects? How are conflicts between trajectory-driven and feature-driven identifications of objects resolved, for example in multiple-object tracking situations? Are there separate “what” and “where” processing streams for haptic and auditory perception? Are there haptic and/or auditory equivalents of the visual object file? Are there equivalents of the visual object token? How are object-identification conflicts between different perceptual systems resolved? Is the common assumption that “persistent object” is a fundamental innate category justified? How does the ability to identify and categorize objects relate to the ability to name and describe them using language? How are features that an individual object had in the past but does not have currently represented? How are categorical constraints on how objects move or act represented, and how do such constraints influence categorization and the re-identification of individuals? How do human beings re-identify objects, including each other, as persistent individuals across changes in location, context and features, even after gaps in observation lasting months or years? How do human capabilities for object categorization and re-identification over time relate to those of other species, and how do human infants develop these capabilities? What can modeling approaches such as cognitive robotics tell us about the answers to these questions? Primary research reports, reviews, and hypothesis and theory papers addressing questions relevant to the understanding of perceptual object segmentation, categorization and individual identification at any scale and from any experimental or modeling perspective are solicited for this Research Topic. Papers that review particular sets of issues from multiple disciplinary perspectives or that advance integrative hypotheses or models that take data from multiple experimental approaches into account are especially encouraged.

How Humans Recognize Objects: Segmentation, Categorization and Individual Identification

How Humans Recognize Objects: Segmentation, Categorization and Individual Identification PDF

Author:

Publisher:

Published: 2016

Total Pages: 0

ISBN-13:

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Human beings experience a world of objects: bounded entities that occupy space and persist through time. Our actions are directed toward objects, and our language describes objects. We categorize objects into kinds that have different typical properties and behaviors. We regard some kinds of objects - each other, for example - as animate agents capable of independent experience and action, while we regard other kinds of objects as inert. We re-identify objects, immediately and without conscious deliberation, after days or even years of non-observation, and often following changes in the features, locations, or contexts of the objects being re-identified. Comparative, developmental and adult observations using a variety of approaches and methods have yielded a detailed understanding of object detection and recognition by the visual system and an advancing understanding of haptic and auditory information processing. Many fundamental questions, however, remain unanswered. What, for example, physically constitutes an "object"? How do specific, classically-characterizable object boundaries emerge from the physical dynamics described by quantum theory, and can this emergence process be described independently of any assumptions regarding the perceptual capabilities of observers? How are visual motion and feature information combined to create object information? How are the object trajectories that indicate persistence to human observers implemented, and how are these trajectory representations bound to feature representations? How, for example, are point-light walkers recognized as single objects? How are conflicts between trajectory-driven and feature-driven identifications of objects resolved, for example in multiple-object tracking situations? Are there separate "what" and "where" processing streams for haptic and auditory perception? Are there haptic and/or auditory equivalents of the visual object file? Are there equivalents of the visual object token? How are object-identification conflicts between different perceptual systems resolved? Is the common assumption that "persistent object" is a fundamental innate category justified? How does the ability to identify and categorize objects relate to the ability to name and describe them using language? How are features that an individual object had in the past but does not have currently represented? How are categorical constraints on how objects move or act represented, and how do such constraints influence categorization and the re-identification of individuals? How do human beings re-identify objects, including each other, as persistent individuals across changes in location, context and features, even after gaps in observation lasting months or years? How do human capabilities for object categorization and re-identification over time relate to those of other species, and how do human infants develop these capabilities? What can modeling approaches such as cognitive robotics tell us about the answers to these questions? Primary research reports, reviews, and hypothesis and theory papers addressing questions relevant to the understanding of perceptual object segmentation, categorization and individual identification at any scale and from any experimental or modeling perspective are solicited for this Research Topic. Papers that review particular sets of issues from multiple disciplinary perspectives or that advance integrative hypotheses or models that take data from multiple experimental approaches into account are especially encouraged.

Object Categorization

Object Categorization PDF

Author: Axel Pinz

Publisher: Now Publishers Inc

Published: 2006

Total Pages: 110

ISBN-13: 1933019131

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This article presents foundations, original research and trends in the field of object categorization by computer vision methods. The research goals in object categorization are to detect objects in images and to determine the object's categories. Categorization aims for the recognition of generic classes of objects, and thus has also been termed 'generic object recognition'. This is in contrast to the recognition of specific, individual objects. While humans are usually better in generic than in specific recognition, categorization is much harder to achieve for today's computer architectures.

Computer Vision - ECCV 2008

Computer Vision - ECCV 2008 PDF

Author: David Forsyth

Publisher: Springer

Published: 2008-10-11

Total Pages: 891

ISBN-13: 3540886931

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Welcome to the 2008EuropeanConference onComputer Vision. These proce- ings are the result of a great deal of hard work by many people. To produce them, a total of 871 papers were reviewed. Forty were selected for oral pres- tation and 203 were selected for poster presentation, yielding acceptance rates of 4.6% for oral, 23.3% for poster, and 27.9% in total. Weappliedthreeprinciples.First,sincewehadastronggroupofAreaChairs, the ?nal decisions to accept or reject a paper rested with the Area Chair, who wouldbeinformedbyreviewsandcouldactonlyinconsensuswithanotherArea Chair. Second, we felt that authors were entitled to a summary that explained how the Area Chair reached a decision for a paper. Third, we were very careful to avoid con?icts of interest. Each paper was assigned to an Area Chair by the Program Chairs, and each Area Chair received a pool of about 25 papers. The Area Chairs then identi?ed and rankedappropriatereviewersfor eachpaper in their pool, and a constrained optimization allocated three reviewers to each paper. We are very proud that every paper received at least three reviews. At this point, authors were able to respond to reviews. The Area Chairs then needed to reach a decision. We used a series of procedures to ensure careful review and to avoid con?icts of interest. ProgramChairs did not submit papers. The Area Chairs were divided into three groups so that no Area Chair in the group was in con?ict with any paper assigned to any Area Chair in the group.

Deep Learning for Computer Vision

Deep Learning for Computer Vision PDF

Author: Jason Brownlee

Publisher: Machine Learning Mastery

Published: 2019-04-04

Total Pages: 564

ISBN-13:

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Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Practical Machine Learning for Computer Vision

Practical Machine Learning for Computer Vision PDF

Author: Valliappa Lakshmanan

Publisher: "O'Reilly Media, Inc."

Published: 2021-07-21

Total Pages: 481

ISBN-13: 1098102339

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This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Object Categorization

Object Categorization PDF

Author: Sven J. Dickinson

Publisher: Cambridge University Press

Published: 2009-09-07

Total Pages: 553

ISBN-13: 0521887380

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A unique multidisciplinary perspective on the problem of visual object categorization.

Visual Object Recognition

Visual Object Recognition PDF

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

Encyclopedia of Perception

Encyclopedia of Perception PDF

Author: E. Bruce Goldstein

Publisher: SAGE

Published: 2009-10-15

Total Pages: 1281

ISBN-13: 1412940818

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Because of the ease with which we perceive, many people see perception as something that "just happens." However, even seemingly simple perceptual experiences involve complex underlying mechanisms, which are often hidden from our conscious experience. These mechanisms are being investigated by researchers and theorists in fields such as psychology, cognitive science, neuroscience, computer science, and philosophy. A few examples of the questions posed by these investigations are, What do infants perceive? How does perception develop? What do perceptual disorders reveal about normal functioning? How can information from one sense, such as hearing, be affected by information from another sense, such as vision? How is the information from all of our senses combined to result in our perception of a coherent environment? What are some practical outcomes of basic research in perception? These are just a few of the questions this encyclopedia will consider, as it presents a comprehensive overview of the field of perception for students, researchers, and professionals in psychology, the cognitive sciences, neuroscience, and related medical disciplines such as neurology and ophthalmology.