Advanced Topics in Computer Vision, 1st Edition

  • Published By:
  • ISBN-10: 1447155203
  • ISBN-13: 9781447155201
  • DDC: 006.6
  • Grade Level Range: College Freshman - College Senior
  • 433 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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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.

Table of Contents

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