Foundations of Computer Vision (Adaptive Computation and Machine Learning series) (1版)

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【簡介】 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 【目錄】

Healthcare Industry 4.0: Computer Vision-Aided Data Analytics (Computational Intelligence Techniques) 2024

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This book covers computer vision-based applications in digital healthcare industry 4.0, including different computer vision techniques, image classification, image segmentations, and object detection. Various application case studies from domains such as science, engineering, and social networking are introduced, along with their architecture and how they leverage various technologies, such as edge computing and cloud computing. It also covers applications of computer vision in tumor detection, cancer detection, combating COVID-19, and patient monitoring. Features: Provides a state-of-the-art computer vision application in the digital health care industry Reviews advances in computer vision and data science technologies for analyzing information on human function and disability Includes practical implementation of computer vision application using recent tools and software Explores computer vision-enabled medical/clinical data security in the cloud Includes case studies from the leading computer vision integrated vendors like Amazon, Microsoft, IBM, and Google This book is aimed at researchers and graduate students in bioengineering, intelligent systems, and computer science and engineering.

Linear Algebra and Optimization with Applications to Machine Learning Vol.I: Linear Algebra for Computer Vision, Robotics, and Machine Learning

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This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields. Sample Chapter(s) Preface Chapter 1: Introduction Request Inspection Copy Contents: Introduction Vector Spaces, Bases, Linear Maps Matrices and Linear Maps Haar Bases, Haar Wavelets, Hadamard Matrices Direct Sums, Rank-Nullity Theorem, Affine Maps Determinants Gaussian Elimination, LU-Factorization, Cholesky Factorization, Reduced Row Echelon Form Vector Norms and Matrix Norms Iterative Methods for Solving Linear Systems The Dual Space and Duality Euclidean Spaces QR-Decomposition for Arbitrary Matrices Hermitian Spaces Eigenvectors and Eigenvalues Unit Quaternions and Rotations in SO(3) Spectral Theorems in Euclidean and Hermitian Spaces Computing Eigenvalues and Eigenvectors Graphs and Graph Laplacians; Basic Facts Spectral Graph Drawing Singular Value Decomposition and Polar Form Applications of SVD and Pseudo-Inverses Annihilating Polynomials and the Primary Decomposition Bibliography Index Readership: Undergraduate and graduate students interested in mathematical fundamentals of linear algebra in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.