Springer Advanced Topics in Computer Vision (Advances in Computer Vision and Pattern Recognition)

Product Description This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. The text describes novel algorithms designed to infer the semantic content of images and videos. Review From the book reviews: “The goal of this book is to provide an overview of recent works in computer vision. … The book is intended more for engineers and researchers who will use it as a relevant source of knowledge in the computer vision field, and benefit from the presence of recent and representative methods that are among the best existing solutions to solve the problems reviewed in the book.” (Sebastien Lefevre, Computing Reviews, June, 2014) From the Back Cover Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout.  This unique text/reference presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of the three main areas in computer vision: reconstruction, registration, and recognition. The book provides an in-depth overview of challenging areas, in addition to descriptions of novel algorithms that exploit machine learning and pattern recognition techniques to infer the semantic content of images and videos.  Topics and features: Investigates visual features, trajectory features, and stereo matching Reviews the main challenges of semi-supervised object recognition, and a novel method for human action categorization Presents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimization Examines solutions to the co-recognition problem, and distance-based classifiers for large-scale image classification Describes how the four-color theorem can be used in early computer vision for solving MRF problems where an energy is to be minimized Introduces a Bayesian generative model for understanding indoor environments, and a boosting approach for generalizing the k-NN rule Discusses the issue of scene-specific object detection, and an approach for making temporal super resolution video from a single input image sequence  This must-read collection will be of great value to advanced undergraduate and graduate students of computer vision, pattern recognition and machine learning. Researchers and practitioners will also find the book useful for understanding and reviewing current approaches in computer vision. About the Author Dr. Giovanni Maria Farinella is Adjunct Professor of Computer Science at the University of Catania, Italy, and Contract Professor of Computer Vision at the School of Arts of Catania, Italy.  Dr.  Sebastiano Battiato is Associate Professor at the University of Catania, Italy.  Dr. Roberto Cipolla is Professor of Information Engineeringat the University of Cambridge, UK.