This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. Features: investigates visual features, trajectory features, and stereo matching;

Visual Features: From Early Concepts to Modern Computer Vision.- Where Next in Object Recognition and How Much Supervision Do We Need?.- Recognizing Human Actions by Using Effective Codebooks and Tracking.- Evaluating and Extending Trajectory Features for Activity Recognition.- Co-Recognition of Images and Videos: Unsupervised Matching of Identical Object Patterns and its Applications.- Stereo Matching: State-of-the-Art and Research Challenges.- Visual Localization for Micro Aerial Vehicles in Urban Outdoor Environments.- Moment Constraints in Convex Optimization for Segmentation and Tracking.- Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets.- Top-Down Bayesian Inference of Indoor Scenes.- Efficient Loopy Belief Propagation Using the Four Color Theorem.- Boosting k-Nearest Neighbors Classification.- Learning Object Detectors in Stationary Environments.- Video Temporal Super-Resolution Based on Self-Similarity.

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 matchingReviews the main challenges of semi-supervised object recognition, and a novel method for human action categorizationPresents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimizationExamines solutions to the co-recognition problem, and distance-based classifiers for large-scale image classificationDescribes 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 ruleDiscusses 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.

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)

Presents a broad selection of cutting-edge research from internationally-recognized computer vision groups Covers both theoretical and practical aspects of reconstruction, registration, and recognition Provides an overview of challenging areas, and describes novel algorithms designed to infer the semantic content of images and videos Includes supplementary material: sn.pub/extras

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