Author: Dimitris N. Metaxas
Publisher: Springer Science & Business Media
ISBN: 1461563356
Category : Science
Languages : en
Pages : 311
Book Description
Physics-Based Deformable Models presents a systematic physics-based framework for modeling rigid, articulated, and deformable objects, their interactions with the physical world, and the estimate of their shape and motion from visual data. This book presents a large variety of methods and associated experiments in computer vision, graphics and medical imaging that help the reader better to understand the presented material. In addition, special emphasis has been given to the development of techniques with interactive or close to real-time performance. Physics-Based Deformable Models is suitable as a secondary text for graduate level courses in Computer Graphics, Computational Physics, Computer Vision, Medical Imaging, and Biomedical Engineering. In addition, this book is appropriate as a reference for researchers and practitioners in the above-mentioned fields.
Physics-Based Deformable Models
Author: Dimitris N. Metaxas
Publisher: Springer Science & Business Media
ISBN: 1461563356
Category : Science
Languages : en
Pages : 311
Book Description
Physics-Based Deformable Models presents a systematic physics-based framework for modeling rigid, articulated, and deformable objects, their interactions with the physical world, and the estimate of their shape and motion from visual data. This book presents a large variety of methods and associated experiments in computer vision, graphics and medical imaging that help the reader better to understand the presented material. In addition, special emphasis has been given to the development of techniques with interactive or close to real-time performance. Physics-Based Deformable Models is suitable as a secondary text for graduate level courses in Computer Graphics, Computational Physics, Computer Vision, Medical Imaging, and Biomedical Engineering. In addition, this book is appropriate as a reference for researchers and practitioners in the above-mentioned fields.
Publisher: Springer Science & Business Media
ISBN: 1461563356
Category : Science
Languages : en
Pages : 311
Book Description
Physics-Based Deformable Models presents a systematic physics-based framework for modeling rigid, articulated, and deformable objects, their interactions with the physical world, and the estimate of their shape and motion from visual data. This book presents a large variety of methods and associated experiments in computer vision, graphics and medical imaging that help the reader better to understand the presented material. In addition, special emphasis has been given to the development of techniques with interactive or close to real-time performance. Physics-Based Deformable Models is suitable as a secondary text for graduate level courses in Computer Graphics, Computational Physics, Computer Vision, Medical Imaging, and Biomedical Engineering. In addition, this book is appropriate as a reference for researchers and practitioners in the above-mentioned fields.
Graph-Based Methods in Computer Vision: Developments and Applications
Author: Bai, Xiao
Publisher: IGI Global
ISBN: 1466618922
Category : Computers
Languages : en
Pages : 395
Book Description
Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data. Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.
Publisher: IGI Global
ISBN: 1466618922
Category : Computers
Languages : en
Pages : 395
Book Description
Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data. Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.
Computer Vision and Simulation
Author: Sherri Alexander
Publisher: Nova Science Publishers
ISBN: 9781634857901
Category : Computer simulation
Languages : en
Pages : 0
Book Description
This book provides current research on computer vision and simulation. Chapter One studies and compares the representation capability of several different layers in convolutional neural network (CNN) showing that they contain more accurate information about the face image than to believe. Chapter Two finds, empirically, the best methods for describing a given texture using an ensemble to harness the discriminative power of different texture approaches. Chapter Three provides a computer study of the interaction of mercury with graphene. Chapter Four discusses the influence of yttrium(III) ion on calcium(II) and zinc(II) biospeciation in human blood plasma by computer simulation. Chapter Five reviews the simualation of diffraction gratings in the Fresnel diffraction regime using the ab-initio iterative Fresnel Integral Method (IFIM). Chapter Six introduces an example of a simple visual feedback control system of a mobile robot with an axis-symmetric shape for mechatronics education.
Publisher: Nova Science Publishers
ISBN: 9781634857901
Category : Computer simulation
Languages : en
Pages : 0
Book Description
This book provides current research on computer vision and simulation. Chapter One studies and compares the representation capability of several different layers in convolutional neural network (CNN) showing that they contain more accurate information about the face image than to believe. Chapter Two finds, empirically, the best methods for describing a given texture using an ensemble to harness the discriminative power of different texture approaches. Chapter Three provides a computer study of the interaction of mercury with graphene. Chapter Four discusses the influence of yttrium(III) ion on calcium(II) and zinc(II) biospeciation in human blood plasma by computer simulation. Chapter Five reviews the simualation of diffraction gratings in the Fresnel diffraction regime using the ab-initio iterative Fresnel Integral Method (IFIM). Chapter Six introduces an example of a simple visual feedback control system of a mobile robot with an axis-symmetric shape for mechatronics education.
Physics-Based Vision: Principles and Practice
Author: Lawrence B. Wolff
Publisher: CRC Press
ISBN: 143986585X
Category : Computers
Languages : en
Pages : 424
Book Description
Commentaries by the editors to this comprehensive anthology in the area of physics-based vision put the papers in perspective and guide the reader to a thorough understanding of the basics of the field. Paper Topics Include: - Intensity Reflection Models - Polarization and Refraction - Camera Calibration - Quantization and Sampling - Depth from Opt
Publisher: CRC Press
ISBN: 143986585X
Category : Computers
Languages : en
Pages : 424
Book Description
Commentaries by the editors to this comprehensive anthology in the area of physics-based vision put the papers in perspective and guide the reader to a thorough understanding of the basics of the field. Paper Topics Include: - Intensity Reflection Models - Polarization and Refraction - Camera Calibration - Quantization and Sampling - Depth from Opt
Advanced Methods and Deep Learning in Computer Vision
Author: E. R. Davies
Publisher: Academic Press
ISBN: 0128221496
Category : Technology & Engineering
Languages : en
Pages : 584
Book Description
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. - Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field - Illustrates principles with modern, real-world applications - Suitable for self-learning or as a text for graduate courses
Publisher: Academic Press
ISBN: 0128221496
Category : Technology & Engineering
Languages : en
Pages : 584
Book Description
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. - Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field - Illustrates principles with modern, real-world applications - Suitable for self-learning or as a text for graduate courses
Active Lighting and Its Application for Computer Vision
Author: Katsushi Ikeuchi
Publisher: Springer Nature
ISBN: 3030565777
Category : Computers
Languages : en
Pages : 309
Book Description
This book describes active illumination techniques in computer vision. We can classify computer vision techniques into two classes: passive and active techniques. Passive techniques observe the scene statically and analyse it as is. Active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene. Traditional passive techniques have a fundamental limitation. The external world surrounding us is three-dimensional; the image projected on a retina or an imaging device is two-dimensional. That is, reduction of one dimension has occurred. Active illumination techniques compensate for the dimensional reduction by actively controlling the illumination. The demand for reliable vision sensors is rapidly increasing in many application areas, such as robotics and medical image analysis. This book explains this new endeavour to explore the augmentation of reduced dimensions in computer vision. This book consists of three parts: basic concepts, techniques, and applications. The first part explains the basic concepts for understanding active illumination techniques. In particular, the basic concepts of optics are explained so that researchers and engineers outside the field can understand the later chapters. The second part explains currently available active illumination techniques, covering many techniques developed by the authors. The final part shows how such active illumination techniques can be applied to various domains, describing the issue to be overcome by active illumination techniques and the advantages of using these techniques. This book is primarily aimed at 4th year undergraduate and 1st year graduate students, and will also help engineers from fields beyond computer vision to use active illumination techniques. Additionally, the book is suitable as course material for technical seminars.
Publisher: Springer Nature
ISBN: 3030565777
Category : Computers
Languages : en
Pages : 309
Book Description
This book describes active illumination techniques in computer vision. We can classify computer vision techniques into two classes: passive and active techniques. Passive techniques observe the scene statically and analyse it as is. Active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene. Traditional passive techniques have a fundamental limitation. The external world surrounding us is three-dimensional; the image projected on a retina or an imaging device is two-dimensional. That is, reduction of one dimension has occurred. Active illumination techniques compensate for the dimensional reduction by actively controlling the illumination. The demand for reliable vision sensors is rapidly increasing in many application areas, such as robotics and medical image analysis. This book explains this new endeavour to explore the augmentation of reduced dimensions in computer vision. This book consists of three parts: basic concepts, techniques, and applications. The first part explains the basic concepts for understanding active illumination techniques. In particular, the basic concepts of optics are explained so that researchers and engineers outside the field can understand the later chapters. The second part explains currently available active illumination techniques, covering many techniques developed by the authors. The final part shows how such active illumination techniques can be applied to various domains, describing the issue to be overcome by active illumination techniques and the advantages of using these techniques. This book is primarily aimed at 4th year undergraduate and 1st year graduate students, and will also help engineers from fields beyond computer vision to use active illumination techniques. Additionally, the book is suitable as course material for technical seminars.
Computer Vision – ECCV 2020
Author: Andrea Vedaldi
Publisher: Springer Nature
ISBN: 3030585778
Category : Computers
Languages : en
Pages : 501
Book Description
The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Publisher: Springer Nature
ISBN: 3030585778
Category : Computers
Languages : en
Pages : 501
Book Description
The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Advances in Computer Vision and Information Technology
Author:
Publisher: I. K. International Pvt Ltd
ISBN: 8189866745
Category : Computers
Languages : en
Pages : 1688
Book Description
The latest trends in information technology represent a new intellectual paradigm for scientific exploration and the visualization of scientific phenomena. This title covers the emerging technologies in the field. Academics, engineers, industrialists, scientists and researchers engaged in teaching, and research and development of computer science and information technology will find the book useful for their academic and research work.
Publisher: I. K. International Pvt Ltd
ISBN: 8189866745
Category : Computers
Languages : en
Pages : 1688
Book Description
The latest trends in information technology represent a new intellectual paradigm for scientific exploration and the visualization of scientific phenomena. This title covers the emerging technologies in the field. Academics, engineers, industrialists, scientists and researchers engaged in teaching, and research and development of computer science and information technology will find the book useful for their academic and research work.
Template Matching Techniques in Computer Vision
Author: Roberto Brunelli
Publisher: John Wiley & Sons
ISBN: 9780470744048
Category : Science
Languages : en
Pages : 348
Book Description
The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms. Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.
Publisher: John Wiley & Sons
ISBN: 9780470744048
Category : Science
Languages : en
Pages : 348
Book Description
The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms. Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.
Energy Minimization Methods in Computer Vision and Pattern Recognition
Author: Anand Rangarajan
Publisher: Springer Science & Business Media
ISBN: 3540404988
Category : Computers
Languages : en
Pages : 540
Book Description
This book constitutes the refereed proceedings of the 4th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2003, held in Lisbon, Portugal in July 2003. The 33 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on unsupervised learning and matching, probabilistic modeling, segmentation and grouping, shape modeling, restoration and reconstruction, and graphs and graph-based methods.
Publisher: Springer Science & Business Media
ISBN: 3540404988
Category : Computers
Languages : en
Pages : 540
Book Description
This book constitutes the refereed proceedings of the 4th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2003, held in Lisbon, Portugal in July 2003. The 33 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on unsupervised learning and matching, probabilistic modeling, segmentation and grouping, shape modeling, restoration and reconstruction, and graphs and graph-based methods.