Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering.
This long anticipated book is 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.
David A. Forsyth received the D.Phil. degree in computer science from Oxford University. He is currently a Professor in the Computer Science Division at the University of California at Berkeley. He has co-authored over eighty technical papers on computer vision, computer graphics and machine learning and has co-edited two books.
Jean Ponce received the Ph.D. degree in Computer Science from the University of Paris Orsay. He is currently a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana Champaign. Professor Ponce has written over a hundred conference and journal papers and co-edited two books on a range of subjects including computer vision and robotics.
I. IMAGE FORMATION AND IMAGE MODELS.1. Cameras.
II. EARLY VISION: JUST ONE IMAGE.7. Linear Filters.
III. EARLY VISION: MULTIPLE IMAGES.10. The Geometry of Multiple Views.
IV. MID-LEVEL VISION.14. Segmentation By Clustering.
V. HIGH-LEVEL VISION: GEOMETRIC MODELS.18. Model-Based Vision.
VI. HIGH-LEVEL VISION: PROBABILISTIC AND INFERENTIAL METHODS.22. Finding Templates Using Classifiers.
VII. APPLICATIONS.25. Application: Finding in Digital Libraries.