Foundations Of Computer Vision
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| Author | : James F. Peters |
| Publisher | : Springer |
| Total Pages | : 443 |
| Release | : 2017-03-17 |
| Genre | : Technology & Engineering |
| ISBN | : 3319524836 |
This book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classification of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classification of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of CV make the book an invaluable resource for advanced undergraduate and first year graduate students in Engineering, Computer Science or Applied Mathematics. It offers insights into the design of CV experiments, inclusion of image processing methods in CV projects, as well as the reconstruction and interpretation of recorded natural scenes.
| Author | : Simon J. D. Prince |
| Publisher | : Cambridge University Press |
| Total Pages | : 599 |
| Release | : 2012-06-18 |
| Genre | : Computers |
| ISBN | : 1107011795 |
A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
| Author | : Wesley E. Snyder |
| Publisher | : Cambridge University Press |
| Total Pages | : 395 |
| Release | : 2017-09-28 |
| Genre | : Computers |
| ISBN | : 1316885828 |
Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications.
| Author | : Antonio Torralba |
| Publisher | : MIT Press |
| Total Pages | : 981 |
| Release | : 2024-04-16 |
| Genre | : Computers |
| ISBN | : 0262048973 |
An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances. Machine learning has revolutionized computer vision, but the methods of today have deep roots in the history of the field. Providing a much-needed modern treatment, this accessible and up-to-date textbook comprehensively introduces the foundations of computer vision while incorporating the latest deep learning advances. Taking a holistic approach that goes beyond machine learning, it addresses fundamental issues in the task of vision and the relationship of machine vision to human perception. Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process. To emphasize intuitive learning, concepts are presented in short, lucid chapters alongside extensive illustrations, questions, and examples. Written by leaders in the field and honed by a decade of classroom experience, this engaging and highly teachable book offers an essential next-generation view of computer vision. Up-to-date treatment integrates classic computer vision and deep learning Accessible approach emphasizes fundamentals and assumes little background knowledge Student-friendly presentation features extensive examples and images Proven in the classroom Instructor resources include slides, solutions, and source code
| Author | : Walter Kropatsch |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 260 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 3709165865 |
Computer Vision is a rapidly growing field of research investigating computational and algorithmic issues associated with image acquisition, processing, and understanding. It serves tasks like manipulation, recognition, mobility, and communication in diverse application areas such as manufacturing, robotics, medicine, security and virtual reality. This volume contains a selection of papers devoted to theoretical foundations of computer vision covering a broad range of fields, e.g. motion analysis, discrete geometry, computational aspects of vision processes, models, morphology, invariance, image compression, 3D reconstruction of shape. Several issues have been identified to be of essential interest to the community: non-linear operators; the transition between continuous to discrete representations; a new calculus of non-orthogonal partially dependent systems.
| Author | : David A. Forsyth |
| Publisher | : Pearson Higher Ed |
| Total Pages | : 793 |
| Release | : 2015-01-23 |
| Genre | : Computers |
| ISBN | : 1292014083 |
Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
| Author | : Reinhard Klette |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 441 |
| Release | : 2014-01-04 |
| Genre | : Computers |
| ISBN | : 1447163206 |
This textbook provides an accessible general introduction to the essential topics in computer vision. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter. Features: provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for mapping an image into an image; explains the topologic and geometric basics for analysing image regions and distributions of image values and discusses identifying patterns in an image; introduces optic flow for representing dense motion and various topics in sparse motion analysis; describes special approaches for image binarization and segmentation of still images or video frames; examines the basic components of a computer vision system; reviews different techniques for vision-based 3D shape reconstruction; includes a discussion of stereo matchers and the phase-congruency model for image features; presents an introduction into classification and learning.
| Author | : Christoph H. Lampert |
| Publisher | : Now Publishers Inc |
| Total Pages | : 113 |
| Release | : 2009 |
| Genre | : Computer vision |
| ISBN | : 1601982682 |
Few developments have influenced the field of computer vision in the last decade more than the introduction of statistical machine learning techniques. Particularly kernel-based classifiers, such as the support vector machine, have become indispensable tools, providing a unified framework for solving a wide range of image-related prediction tasks, including face recognition, object detection and action classification. By emphasizing the geometric intuition that all kernel methods rely on, Kernel Methods in Computer Vision provides an introduction to kernel-based machine learning techniques accessible to a wide audience including students, researchers and practitioners alike, without sacrificing mathematical correctness. It covers not only support vector machines but also less known techniques for kernel-based regression, outlier detection, clustering and dimensionality reduction. Additionally, it offers an outlook on recent developments in kernel methods that have not yet made it into the regular textbooks: structured prediction, dependency estimation and learning of the kernel function. Each topic is illustrated with examples of successful application in the computer vision literature, making Kernel Methods in Computer Vision a useful guide not only for those wanting to understand the working principles of kernel methods, but also for anyone wanting to apply them to real-life problems.
| Author | : Richard Hartley |
| Publisher | : Cambridge University Press |
| Total Pages | : 676 |
| Release | : 2004-03-25 |
| Genre | : Computers |
| ISBN | : 1139449141 |
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
| Author | : Sebastian Nowozin |
| Publisher | : Now Publishers Inc |
| Total Pages | : 195 |
| Release | : 2011 |
| Genre | : Computers |
| ISBN | : 1601984561 |
Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.
