Advanced Topics in Computer Vision
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Beschreibung
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 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.
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
Autor*in
Giovanni Maria Farinella
Themen in »Advanced Topics in Computer Vision«
3D Reconstruction Boosting Computer Vision Convex Optimization Event and Activity Recognition Graph Cuts Image Segmentation Image and Video Analysis Large Scale Image Classification Learning Objects Loopy Belief Propagation Message Parsing Metric Learning Object Detection Scene Understanding
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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)
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