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Cipolla R., Battiato S., Farinella G.M. (eds.) Machine Learning for Computer Vision

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Cipolla R., Battiato S., Farinella G.M. (eds.) Machine Learning for Computer Vision
Springer, 2013. — 264 p.
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 recent editions of the school and covering some of the key topics of machine learning for computer vision. The chapters provide both an in-depth overview of challenging areas and key references to the existing literature. The book starts with two chapters devoted to introducing the reader to the exciting fields of Machine Learning and Computer Vision. In Chapter 1 the problem of producing truly intelligent machines is discussed and the neuromorphic approach is introduced. Chapter 2 introduces the notion of visual information and its relevant properties useful to accomplish visual inference tasks. Chapter 3 reviews algorithms to rapidly search images or videos in large collections, whereas Chapter 4 discusses the problem of object recognition and introduces generative models able to take into account transformations of geometry and reflectance. Chapter 5 describes a method to quickly and accurately predict 3D positions of body joints from a single depth image. Chapter 6 describes a fast vote-based approach for 3D shape recognition and registration. Chapter 7 introduces novel multi-classifier boosting algorithms for object detection, tracking and segmentation tasks, whereas in Chapter 8 the problem of tracking objects using multiple cameras is described together with related
algorithms. Finally, Chapter 9 complete the book by presenting a vision system for the challenging task of autonomous driving vehicles. It is our hope that graduate students, young and senior researchers, and academic/industrial professionals will find the book useful for understanding and reviewing current approaches in Computer Vision, thereby continuing the mission of the International Computer Vision Summer School.
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