Advanced Topics in Computer Vision

  • Giovanni Maria Farinella
  • Sebastiano Battiato
  • Roberto Cipolla

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Lamberto Ballan, Lorenzo Seidenari, Giuseppe Serra, Marco Bertini, Alberto Del Bimbo
    Pages 65-93
  3. Ross Messing, Atousa Torabi, Aaron Courville, Chris Pal
    Pages 95-111
  4. Michael Bleyer, Christian Breiteneder
    Pages 143-179
  5. Maria Klodt, Frank Steinbrücker, Daniel Cremers
    Pages 215-242
  6. Thomas Mensink, Jakob Verbeek, Florent Perronnin, Gabriela Csurka
    Pages 243-276
  7. Luca Del Pero, Kobus Barnard
    Pages 277-311
  8. Radu Timofte, Luc Van Gool
    Pages 313-339
  9. Paolo Piro, Richard Nock, Wafa Bel Haj Ali, Frank Nielsen, Michel Barlaud
    Pages 341-375
  10. Peter M. Roth, Sabine Sternig, Horst Bischof
    Pages 377-409
  11. Mihoko Shimano, Takahiro Okabe, Imari Sato, Yoichi Sato
    Pages 411-430
  12. Back Matter
    Pages 431-433

About this book


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 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.


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 Stereo Matching Super Resolution Visual Features

Editors and affiliations

  • Giovanni Maria Farinella
    • 1
  • Sebastiano Battiato
    • 2
  • Roberto Cipolla
    • 3
  1. 1.Dipartimento di Matematica e InformaticaUniversità di CataniaCataniaItaly
  2. 2.Dipartimento di Matematica e InformaticaUniversità di CataniaCataniaItaly
  3. 3.Department of EngineeringUniversity of CambridgeCambridgeUnited Kingdom

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London 2013
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-5519-5
  • Online ISBN 978-1-4471-5520-1
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • Buy this book on publisher's site