An Introduction To Object Recognition

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An Introduction to Object Recognition

Marco Alexander Treiber
An Introduction to Object Recognition Book PDF

Author : Marco Alexander Treiber
Publisher : Springer Science & Business Media
Page : 210 pages
File Size : 45,7 Mb
Release : 2010-07-23
Category : Computers
ISBN : 9781849962353

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An Introduction to Object Recognition by Marco Alexander Treiber Pdf

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.

Visual Object Recognition

Kristen Grauman,Bastian Leibe
Visual Object Recognition Book PDF

Author : Kristen Grauman,Bastian Leibe
Publisher : Morgan & Claypool Publishers
Page : 184 pages
File Size : 50,6 Mb
Release : 2011
Category : Computers
ISBN : 9781598299687

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Visual Object Recognition by Kristen Grauman,Bastian Leibe Pdf

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

Object Detection and Recognition in Digital Images

Boguslaw Cyganek
Object Detection and Recognition in Digital Images Book PDF

Author : Boguslaw Cyganek
Publisher : John Wiley & Sons
Page : 518 pages
File Size : 54,9 Mb
Release : 2013-05-20
Category : Science
ISBN : 9781118618363

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Object Detection and Recognition in Digital Images by Boguslaw Cyganek Pdf

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.

Deep Learning for Computer Vision

Jason Brownlee
Deep Learning for Computer Vision Book PDF

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 564 pages
File Size : 41,6 Mb
Release : 2019-04-04
Category : Computers
ISBN : 8210379456XXX

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Deep Learning for Computer Vision by Jason Brownlee Pdf

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

An Introduction to Object Recognition

Marco Alexander Treiber
An Introduction to Object Recognition Book PDF

Author : Marco Alexander Treiber
Publisher : Springer
Page : 202 pages
File Size : 40,8 Mb
Release : 2013-01-02
Category : Computers
ISBN : 1849962367

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An Introduction to Object Recognition by Marco Alexander Treiber Pdf

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 Recognition

M. Bennamoun,G.J. Mamic
Object Recognition Book PDF

Author : M. Bennamoun,G.J. Mamic
Publisher : Springer Science & Business Media
Page : 376 pages
File Size : 53,5 Mb
Release : 2001-12-12
Category : Computers
ISBN : 1852333987

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Object Recognition by M. Bennamoun,G.J. Mamic Pdf

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.

Three-Dimensional Object Recognition from Range Images

Minsoo Suk,Suchendra M. Bhandarkar
Three Dimensional Object Recognition from Range Images Book PDF

Author : Minsoo Suk,Suchendra M. Bhandarkar
Publisher : Springer Science & Business Media
Page : 318 pages
File Size : 42,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9784431682134

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Three-Dimensional Object Recognition from Range Images by Minsoo Suk,Suchendra M. Bhandarkar Pdf

Computer Science Workbench is a monograph series which will provide you with an in-depth working knowledge of current developments in computer technology. Every volume in this series will deal with a topic of importance in computer science and elaborate on how you yourself can build systems related to the main theme. You will be able to develop a variety of systems, including computer software tools, computer graphics, computer animation, database management systems, and computer-aided design and manufacturing systems. Computer Science Workbench represents an important new contribution in the field of practical computer technology. T08iyasu L. Kunii PREFACE The primary aim of this book is to present a coherent and self-contained de scription of recent advances in three-dimensional object recognition from range images. Three-dimensional object recognition concerns recognition and localiza tion of objects of interest in a scene from input images. This problem is one of both theoretical and practical importance. On the theoretical side, it is an ideal vehicle for the study of the general area of computer vision since it deals with several important issues encountered in computer vision-for example, issues such as feature extraction, acquisition, representation and proper use of knowl edge, employment of efficient control strategies, coupling numerical and symbolic computations, and parallel implementation of algorithms. On the practical side, it has a wide range of applications in areas such as robot vision, autonomous navigation, automated inspection of industrial parts, and automated assembly.

Object Recognition by Computer

William Eric Leifur Grimson
Object Recognition by Computer Book PDF

Author : William Eric Leifur Grimson
Publisher : Mit Press
Page : 532 pages
File Size : 51,6 Mb
Release : 2003-02-01
Category : Computers
ISBN : 0262571889

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Object Recognition by Computer by William Eric Leifur Grimson Pdf

This book describes an extended series of experiments into the role of geometry in the critical area of object recognition.

Toward Category-Level Object Recognition

Jean Ponce,Martial Hebert,Cordelia Schmid,Andrew Zisserman
Toward Category Level Object Recognition Book PDF

Author : Jean Ponce,Martial Hebert,Cordelia Schmid,Andrew Zisserman
Publisher : Springer
Page : 622 pages
File Size : 52,8 Mb
Release : 2007-01-25
Category : Computers
ISBN : 9783540687955

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Toward Category-Level Object Recognition by Jean Ponce,Martial Hebert,Cordelia Schmid,Andrew Zisserman Pdf

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.

Representations and Techniques for 3D Object Recognition and Scene Interpretation

Derek Hoiem,Silvio Savarese
Representations and Techniques for 3D Object Recognition and Scene Interpretation Book PDF

Author : Derek Hoiem,Silvio Savarese
Publisher : Morgan & Claypool Publishers
Page : 172 pages
File Size : 52,7 Mb
Release : 2011
Category : Computers
ISBN : 9781608457281

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Representations and Techniques for 3D Object Recognition and Scene Interpretation by Derek Hoiem,Silvio Savarese Pdf

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

2D Object Detection and Recognition

Yali Amit
2D Object Detection and Recognition Book PDF

Author : Yali Amit
Publisher : MIT Press
Page : 334 pages
File Size : 50,9 Mb
Release : 2002
Category : Computers
ISBN : 0262011948

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2D Object Detection and Recognition by Yali Amit Pdf

A guide to the computer detection and recognition of 2D objects in gray-level images.

An Introduction to 3D Computer Vision Techniques and Algorithms

Boguslaw Cyganek,J. Paul Siebert
An Introduction to 3D Computer Vision Techniques and Algorithms Book PDF

Author : Boguslaw Cyganek,J. Paul Siebert
Publisher : John Wiley & Sons
Page : 485 pages
File Size : 44,7 Mb
Release : 2011-08-10
Category : Science
ISBN : 9781119964476

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An Introduction to 3D Computer Vision Techniques and Algorithms by Boguslaw Cyganek,J. Paul Siebert Pdf

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.

Practical Machine Learning for Computer Vision

Valliappa Lakshmanan,Martin Görner,Ryan Gillard
Practical Machine Learning for Computer Vision Book PDF

Author : Valliappa Lakshmanan,Martin Görner,Ryan Gillard
Publisher : "O'Reilly Media, Inc."
Page : 481 pages
File Size : 41,8 Mb
Release : 2021-07-21
Category : Computers
ISBN : 9781098102333

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Practical Machine Learning for Computer Vision by Valliappa Lakshmanan,Martin Görner,Ryan Gillard Pdf

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 Recognition

M. Bennamoun,G.J. Mamic
Object Recognition Book PDF

Author : M. Bennamoun,G.J. Mamic
Publisher : Springer Science & Business Media
Page : 352 pages
File Size : 52,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447137221

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Object Recognition by M. Bennamoun,G.J. Mamic Pdf

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.

Deep Learning in Object Detection and Recognition

Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng
Deep Learning in Object Detection and Recognition Book PDF

Author : Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng
Publisher : Springer
Page : 128 pages
File Size : 52,8 Mb
Release : 2018-09-11
Category : Computers
ISBN : 9811051518

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Deep Learning in Object Detection and Recognition by Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng Pdf

This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.