Machine Learning in Computer Vision (Computational Imaging and Vision)

Annotation "This book comes right on time ... It is amazing so early in a new field that a book appears which connects theory to algorithms and through them to convincing applications ... This book will surely be with us for quite some time to come."€ € € € € € € € € € From the foreword by Arnold Smeulders The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models. This book is intended for computer vision, machine learning, and pattern recognition researchers as well as for graduate students in computer science and electrical engineering. € € It Started Withimageprocessing Inthesixties. Back Then, It Took Ages To Digitize A Landsat Image And Then Process It With A Mainframe Computer. P- Cessing Was Inspired On Theachievements Of Signal Processing And Was Still Very Much Oriented Towards Programming. In The Seventies, Image Analysis Spun Off Combining Image Measurement With Statistical Pattern Recognition. Slowly, Computational Methods Detached Themselves From The Sensor And The Goal To Become More Generally Applicable. In Theeighties, Model-drivencomputervision Originated When Arti?cial- Telligence And Geometric Modelling Came Together With Image Analysis Com- Nents. The Emphasis Was On Precise Analysiswithlittleorno Interaction, Still Very Much An Art Evaluated By Visual Appeal. The Main Bottleneck Was In The Amount Of Data Using An Average Of 5 To 50 Pictures To Illustrate The Point. At The Beginning Of The Nineties, Vision Became Available To Many With The Advent Of Suf?ciently Fast Pcs. The Internet Revealed The Interest Of The G- Eral Public Im Images, Eventually Introducingcontent-basedimageretrieval. Combining Independent (informal) Archives, As The Web Is, Urges For Inter- Tive Evaluation Of Approximate Results Andhence Weak Algorithms And Their Combination In Weak Classi?ers. Theory: Probabilistic Classifiers -- Theory: Generalization Bounds -- Theory: Semi-supervised Learning -- Algorithm: Maximum Likelihood Minimum Entropy Hmm -- Algorithm: Margin Distribution Optimization -- Algorithm: Learning The Structure Of Bayesian Network Classifiers -- Application: Office Activity Recognition -- Application: Multimodal Event Detection -- Application: Facial Expression Recognition -- Application: Bayesian Network Classifiers For Face Detection. By N. Sebe, Ira Cohen, Ashutosh Garg, Thomas S. Huang. "The goal of this book is to address the use of several important machine learning techniques into computer vision applications. All innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (import) and learned (internal) entities of the system." "In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all design models." "This book is intended for computer vision, machine learning, and pattern recognition researchers as well as for graduate students in computer science and electrical engineering."--Jacket

Written for undergraduates in their final year and graduate students in their initial year, this overview includes both theories and applications, including those in particle physics, cosmology, condensed matter, statistical mechanics and critical phenomena. Maggiore (physics, U. of Geneva) covers Lorentz and Poincare symmetries in quantum field theory, classical field theory, quantization of free fields, perturbation theory and Feynman diagrams, cross-section and decay rates, quantum electrodynamics, the low-energy limit of the electroweak theory, path integral quantization, non- abelian gauge theories, and spontaneous symmetry breaking. He provides lists of further reading and exercises for each chapter, with solutions. He also includes a very helpful full bibliography. Annotation © 2006 Book News, Inc., Portland, OR

The importance and the beauty of modern quantum field theory resides in the power and variety of its methods and ideas, which find application in domains as different as particle physics, cosmology, condensed matter, statistical mechanics and critical phenomena. This book introduces the reader to the modern developments, assuming no previous knowledge of quantum field theory. Along with standard topics like Feynman diagrams, the book discusses effective lagrangians, renormalization group equations, the path integral formulation, spontaneous symmetry breaking and non-abelian gauge theories. The inclusion of more advanced topics will also make this a most useful book for graduate students and researchers. "This book introduces the reader to the modern developments, assuming no previous knowledge of quantum field theory. Along with standard topics like Feynman diagrams, the book discusses effective lagrangians, renormalization group equations, the path integral formulation, spontaneous symmetry breaking and non-abelian gauge theories. The inclusion of more advanced topics will also make this a most useful book for graduate students and researchers."--Jacket Computer vision has grown rapidly within the past decade, producing tools that enable the understanding of visual information, especially for scenes with no accompanying structural, administrative, or descriptive text information.