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Full Description
This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. The text provides an overview of challenging areas and descriptions of novel algorithms. 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 for solving MRF problems; 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.
Contents
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.
