Machine Learning for Computer Vision

1. Front Matter

   Pages 1-18

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2. Throwing Down the Visual Intelligence Gauntlet

   • Cheston Tan, Joel Z. Leibo, Tomaso Poggio

   Pages 1-15

3. Actionable Information in Vision

   • Stefano Soatto

   Pages 17-48

4. Learning Binary Hash Codes for Large-Scale Image Search

   • Kristen Grauman, Rob Fergus

   Pages 49-87

5. Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition

   • Jeroen C. Chua, Inmar E. Givoni, Ryan P. Adams, Brendan J. Frey

   Pages 89-117

6. Real-Time Human Pose Recognition in Parts from Single Depth Images

   • Jamie Shotton, Andrew Fitzgibbon, Mat Cook, Toby Sharp, Mark Finocchio, Richard Moore et al.

   Pages 119-135

7. Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

   • Minh-Tri Pham, Oliver J. Woodford, Frank Perbet, Atsuto Maki, Riccardo Gherardi, Björn Stenger et al.

   Pages 137-162

8. Multiple Classifier Boosting and Tree-Structured Classifiers

   • Tae-Kyun Kim, Roberto Cipolla

   Pages 163-196

9. Simultaneous Detection and Tracking with Multiple Cameras

   • Murtaza Taj, Andrea Cavallaro

   Pages 197-214

10. Applications of Computer Vision to Vehicles: An Extreme Test

    • Alberto Broggi, Stefano Cattani, Paolo Medici, Paolo Zani

    Pages 215-250

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. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.

• Computational Intelligence
• Computer Vision
• Image Analyis
• Machine Learning
• Video Analysis

From the reviews:

“This book should … be of interest to anybody involved in computer vision or image and video analysis, as it presents many challenging scenarios to the machine learning community. … this book presents a snapshot of key research in the areas of computer vision and machine learning. On this level, the book succeeds, with many first-class papers. I recommend the book to practitioners in the field, as well as those pursuing PhD-level studies.” (David Marshall, Computing Reviews, April, 2013)

• Department of Engineering, University of Cambridge, Cambridge, United Kingdom

  Roberto Cipolla

• Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy

  Sebastiano Battiato, Giovanni Maria Farinella

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