Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.

Although research in computer vision for recognizing 3D objects in photographs dates back to the 1960s, progress was relatively slow until the turn of the millennium, and only now do we see the emergence of effective techniques for recognizing object categories with different appearances under large variations in the observation conditions. Tremendous progress has been achieved in the past five years, thanks largely to the integration of new data representations, such as invariant semi-local features, developed in the computer vision community with the effective models of data distribution and classification procedures developed in the statistical machine-learning community.

This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The main goals of these two workshops were to promote the creation of an international object recognition community, with common datasets and evaluation procedures, to map the state of the art and identify the main open problems and opportunities for synergistic research, and to articulate the industrial and societal needs and opportunities for object recognition research worldwide.

The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.

This book constitutes the strictly refereed post-workshop proceedings of the second International Workshop on Object Representation in Computer Vision, held in conjunction with ECCV '96 in Cambridge, UK, in April 1996.
The 15 revised full papers contained in the book were selected from 45 submissions for presentation at the workshop. Also included are three invited contributions based on the talks by Takeo Kanade, Jan Koenderink, and Ram Nevatia as well as a workshop report by the volume editors summarizing several panel discussions and the general state of the art in the area.

This book documents the scientific outcome of the International NSF-ARPA Workshop on Object Representation in Computer Vision, held in New York City in December 1994 with invited participants chosen among the recognized experts in the field.
The volume presents the complete set of papers in revised full-length versions. In addition, the first paper is a report on the workshop in which the panel discussions as well as the conclusions and recommendations reached by the workshop participants are summarized.
Altogether the volume provides an excellent, in-depth view of the state of the art in this active area of research and applications.

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection-that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. Sparse Modeling for Image and Vision Processing provides the reader with a self-contained view of sparse modeling for visual recognition and image processing. More specifically, the work focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts. It reviews a large number of applications of dictionary learning in image processing and computer vision and presents basic sparse estimation tools. It starts with a historical tour of sparse estimation in signal processing and statistics, before moving to more recent concepts such as sparse recovery and dictionary learning. Subsequently, it shows that dictionary learning is related to matrix factorization techniques, and that it is particularly effective for modeling natural image patches. As a consequence, it has been used for tackling several image processing problems and is a key component of many state-of-the-art methods in visual recognition. Sparse Modeling for Image and Vision Processing concludes with a presentation of optimization techniques that should make dictionary learning easy to use for researchers that are not experts in the field.