Beschreibung:
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.
Presents a broad selection of cutting-edge research from internationally-recognized computer vision groups
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.
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.
