David G. Stork - Böcker

PIXELS & PAINTINGS “The discussion is firmly grounded in established art historical practices, such as close visual analysis and an understanding of artists’ working methods, and real-world examples demonstrate how computer-assisted techniques can complement traditional approaches.”—Dr. Emilie Gordenker, Director of the Van Gogh Museum The pioneering presentation of computer-based image analysis of fine art, forging a dialog between art scholars and the computer vision community In recent years, sophisticated computer vision, graphics, and artificial intelligence algorithms have proven to be increasingly powerful tools in the study of fine art. These methods—some adapted from forensic digital photography and others developed specifically for art—empower a growing number of computer-savvy art scholars, conservators, and historians to answer longstanding questions as well as provide new approaches to the interpretation of art. Pixels & Paintings provides the first and authoritative overview of the broad range of these methods, which extend from image processing of palette, marks, brush strokes, and shapes up through analysis of objects, poses, style, composition, to the computation of simple interpretations of artworks. This book stresses that computer methods for art analysis must always incorporate the cultural contexts appropriate to the art studies at hand—a blend of humanistic and scientific expertise. Describes powerful computer image analysis methods and their application to problems in the history and interpretation of fine artDiscusses some of the art historical lessons and revelations provided by the use of these methodsClarifies the assumptions and applicability of methods and the role of cultural contexts in their useShows how computation can be used to analyze tens of thousands of artworks to reveal trends and anomalies that could not be found by traditional non-computer methodsPixels & Paintings is essential reading for computer image analysts and graphics specialists, conservators, historians, students, psychologists and the general public interested in the study and appreciation of art.

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.

Computer Manual to Accompany Pattern Classification and its associated MATLAB software is an excellent companion to Duda: Pattern Classfication, 2nd ed, (DH&S). The code contains all algorithms described in Duda as well as supporting algorithms for data generation and visualization. The Manual uses the same terminology as the DH&S text and contains step-by-step worked examples, including many of the examples and figures in the textbook.The Manual is accompanied by software that is available electronically. The software contains all algorithms in DH&S, indexed to the textbook, and uses symbols and notation as close as possible to the textbook. The code is self-annotating so the user can easily navigate, understand and modify the code.

This is the first comprehensive book on the interdisciplinary study of speechreading (lipreading) -- production, perception and learning -- by both humans and machines. It is the product of the largest-ever gathering of scientists devoted to the subject, at a NATO workshop held in France in 1995. The research presented explores and promotes the incorporation of visual information into automatic speech recognizers for improved recognition accuracy (especially in noisy environments), and draws on and further elucidates knowledge of the psychology of speechreading by humans. The book is a milestone along the path to more accurate speech based interfaces, as well as to deeper understanding of fundamental mechanisms of human perception and speech recognition.