An Introduction To Object Recognition

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An Introduction to Object Recognition

Author : Marco Alexander Treiber
Publisher : Springer Science & Business Media
ISBN 13 : 1849962359
Total Pages : 210 pages
Book Rating : 4.8/5 (499 download)

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Book Synopsis An Introduction to Object Recognition by : Marco Alexander Treiber

Download or read book An Introduction to Object Recognition written by Marco Alexander Treiber and published by Springer Science & Business Media. This book was released on 2010-07-23 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum. Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters). Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses. Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly.

Object Detection and Recognition in Digital Images

Author : Boguslaw Cyganek
Publisher : John Wiley & Sons
ISBN 13 : 111861836X
Total Pages : 518 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Object Detection and Recognition in Digital Images by : Boguslaw Cyganek

Download or read book Object Detection and Recognition in Digital Images written by Boguslaw Cyganek and published by John Wiley & Sons. This book was released on 2013-05-20 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.

Object Recognition

Author : M. Bennamoun
Publisher : Springer Science & Business Media
ISBN 13 : 9781852333980
Total Pages : 376 pages
Book Rating : 4.3/5 (339 download)

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Book Synopsis Object Recognition by : M. Bennamoun

Download or read book Object Recognition written by M. Bennamoun and published by Springer Science & Business Media. This book was released on 2001-12-12 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatie object recognition is a multidisciplinary research area using con cepts and tools from mathematics, computing, optics, psychology, pattern recognition, artificial intelligence and various other disciplines. The purpose of this research is to provide a set of coherent paradigms and algorithms for the purpose of designing systems that will ultimately emulate the functions performed by the Human Visual System (HVS). Hence, such systems should have the ability to recognise objects in two or three dimensions independently of their positions, orientations or scales in the image. The HVS is employed for tens of thousands of recognition events each day, ranging from navigation (through the recognition of landmarks or signs), right through to communication (through the recognition of characters or people themselves). Hence, the motivations behind the construction of recognition systems, which have the ability to function in the real world, is unquestionable and would serve industrial (e.g. quality control), military (e.g. automatie target recognition) and community needs (e.g. aiding the visually impaired). Scope, Content and Organisation of this Book This book provides a comprehensive, yet readable foundation to the field of object recognition from which research may be initiated or guided. It repre sents the culmination of research topics that I have either covered personally or in conjunction with my PhD students. These areas include image acqui sition, 3-D object reconstruction, object modelling, and the matching of ob jects, all of which are essential in the construction of an object recognition system.

Visual Object Recognition

Author : Kristen Grauman
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1598299689
Total Pages : 184 pages
Book Rating : 4.5/5 (982 download)

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Book Synopsis Visual Object Recognition by : Kristen Grauman

Download or read book Visual Object Recognition written by Kristen Grauman and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Deep Learning for Computer Vision

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 564 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning for Computer Vision by : Jason Brownlee

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Object Recognition by Computer

Author : William Eric Leifur Grimson
Publisher : Mit Press
ISBN 13 : 9780262571883
Total Pages : 532 pages
Book Rating : 4.5/5 (718 download)

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Book Synopsis Object Recognition by Computer by : William Eric Leifur Grimson

Download or read book Object Recognition by Computer written by William Eric Leifur Grimson and published by Mit Press. This book was released on 2003-02-01 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes an extended series of experiments into the role of geometry in the critical area of object recognition.

2D Object Detection and Recognition

Author : Yali Amit
Publisher : MIT Press
ISBN 13 : 9780262011945
Total Pages : 334 pages
Book Rating : 4.0/5 (119 download)

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Book Synopsis 2D Object Detection and Recognition by : Yali Amit

Download or read book 2D Object Detection and Recognition written by Yali Amit and published by MIT Press. This book was released on 2002 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the computer detection and recognition of 2D objects in gray-level images.

Practical Machine Learning for Computer Vision

Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098102339
Total Pages : 481 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Practical Machine Learning for Computer Vision by : Valliappa Lakshmanan

Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

An Introduction to 3D Computer Vision Techniques and Algorithms

Author : Boguslaw Cyganek
Publisher : John Wiley & Sons
ISBN 13 : 1119964474
Total Pages : 485 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis An Introduction to 3D Computer Vision Techniques and Algorithms by : Boguslaw Cyganek

Download or read book An Introduction to 3D Computer Vision Techniques and Algorithms written by Boguslaw Cyganek and published by John Wiley & Sons. This book was released on 2011-08-10 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently: present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms; explain scale-space vision, as well as space reconstruction and multiview integration; demonstrate a variety of practical applications for 3D surface imaging and analysis; provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures. An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

Information Routing, Correspondence Finding, and Object Recognition in the Brain

Author : Philipp Wolfrum
Publisher : Springer Science & Business Media
ISBN 13 : 3642152538
Total Pages : 147 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Information Routing, Correspondence Finding, and Object Recognition in the Brain by : Philipp Wolfrum

Download or read book Information Routing, Correspondence Finding, and Object Recognition in the Brain written by Philipp Wolfrum and published by Springer Science & Business Media. This book was released on 2010-09-08 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: At?rstsight,thisbookisaboutfacerecognitioninthebrain.Itsmorelasting value, however,lies in the paradigmatic way in which this particular problem is treated. From the basic ideas that are worked out here in concrete detail, it is a naturaland simple next step to at leastimagine, if not realizein model form, much more generalstructures and processes,thus helping to bridge the still tremendous chasm between mind and brain. It is the purpose of this foreword to point out these generic traits. For centuries, thinking about the brain has been dominated by the most complexmechanisticdevicesofthetime,clockwork,communicatinghydraulic tubesor,today,thecomputer.Thecomputer,takenasincarnationoftheU- versal Turing Machine, can implement any conceivable process, so that also a functional brain can surely be simulated on it, an idea that, beginning in the ?fties of the last century, has been seducing scientists to create “art- cial intelligence” in the computer. As a result we now have an information technology that displays many functional capabilities formerly regarded as the exclusive domain of the mind. As fascinating as this is, doting on “int- ligent machines” is systematically diverting our attention awayfrom the true problems of understanding the working of the brain.

Optimization for Computer Vision

Author : Marco Alexander Treiber
Publisher : Springer Science & Business Media
ISBN 13 : 1447152832
Total Pages : 266 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Optimization for Computer Vision by : Marco Alexander Treiber

Download or read book Optimization for Computer Vision written by Marco Alexander Treiber and published by Springer Science & Business Media. This book was released on 2013-07-12 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical and authoritative text/reference presents a broad introduction to the optimization methods used specifically in computer vision. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation. These discussions are further supported by examples taken from important applications in computer vision. Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.

Representations and Techniques for 3D Object Recognition and Scene Interpretation

Author : Derek Hoiem
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608457281
Total Pages : 172 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Representations and Techniques for 3D Object Recognition and Scene Interpretation by : Derek Hoiem

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions

Feature Extraction and Image Processing for Computer Vision

Author : Mark Nixon
Publisher : Academic Press
ISBN 13 : 0123978246
Total Pages : 629 pages
Book Rating : 4.1/5 (239 download)

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Book Synopsis Feature Extraction and Image Processing for Computer Vision by : Mark Nixon

Download or read book Feature Extraction and Image Processing for Computer Vision written by Mark Nixon and published by Academic Press. This book was released on 2012-12-18 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. - Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews - Essential reading for engineers and students working in this cutting-edge field - Ideal module text and background reference for courses in image processing and computer vision - The only currently available text to concentrate on feature extraction with working implementation and worked through derivation

Elements of Deep Learning for Computer Vision

Author : Bharat Sikka
Publisher : BPB Publications
ISBN 13 : 9390684684
Total Pages : 224 pages
Book Rating : 4.3/5 (96 download)

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Book Synopsis Elements of Deep Learning for Computer Vision by : Bharat Sikka

Download or read book Elements of Deep Learning for Computer Vision written by Bharat Sikka and published by BPB Publications. This book was released on 2021-06-24 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries. KEY FEATURES ● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford. DESCRIPTION Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch. This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs. By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions. WHAT YOU WILL LEARN ● Get to know the mechanism of deep learning and how neural networks operate. ● Learn to develop a highly accurate neural network model. ● Access to rich Python libraries to address computer vision challenges. ● Build deep learning models using PyTorch and learn how to deploy using the API. ● Learn to develop Object Detection and Face Recognition models along with their deployment. WHO THIS BOOK IS FOR This book is for the readers who aspire to gain a strong fundamental understanding of how to infuse deep learning into computer vision and image processing applications. Readers are expected to have intermediate Python skills. No previous knowledge of PyTorch and Computer Vision is required. TABLE OF CONTENTS 1. An Introduction to Deep Learning 2. Supervised Learning 3. Gradient Descent 4. OpenCV with Python 5. Python Imaging Library and Pillow 6. Introduction to Convolutional Neural Networks 7. GoogLeNet, VGGNet, and ResNet 8. Understanding Object Detection 9. Popular Algorithms for Object Detection 10. Faster RCNN with PyTorch and YoloV4 with Darknet 11. Comparing Algorithms and API Deployment with Flask 12. Applications in Real World

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

Author : Chi Hau Chen
Publisher : World Scientific
ISBN 13 : 9814497649
Total Pages : 1045 pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Handbook Of Pattern Recognition And Computer Vision (2nd Edition) by : Chi Hau Chen

Download or read book Handbook Of Pattern Recognition And Computer Vision (2nd Edition) written by Chi Hau Chen and published by World Scientific. This book was released on 1999-03-12 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Pattern Recognition and Classification

Author : Geoff Dougherty
Publisher : Springer Science & Business Media
ISBN 13 : 1461453232
Total Pages : 203 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Pattern Recognition and Classification by : Geoff Dougherty

Download or read book Pattern Recognition and Classification written by Geoff Dougherty and published by Springer Science & Business Media. This book was released on 2012-10-28 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

Visual Agnosia

Author : Martha J. Farah
Publisher : MIT Press (MA)
ISBN 13 : 9780262560825
Total Pages : 190 pages
Book Rating : 4.5/5 (68 download)

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Book Synopsis Visual Agnosia by : Martha J. Farah

Download or read book Visual Agnosia written by Martha J. Farah and published by MIT Press (MA). This book was released on 1995 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain damage can lead to selective problems with visual perception, including visual agnosia-the inability to recognize objects even though elementary visual functions remain unimpaired. Visual Agnosia reviews all the recent records of this disorder, places these 100 or so case studies in the general context of current neuroscience, and draws relevant conclusions about the organization of normal visual processing.