Computer Vision

Computer Vision : A Modern Approach: United States Edition

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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 more

Product details

  • Hardback | 693 pages
  • 205.74 x 256.54 x 40.64mm | 1,587.57g
  • Pearson Education (US)
  • Pearson
  • United States
  • English
  • 0130851981
  • 9780130851987

Back cover copy

Computer Vision: A Modern ApproachThis extraordinary book gives a uniquely modern view of computer vision. Offering a general survey of the whole computer vision enterprise along with sufficient detail for readers to be able to build useful applications, this book is invaluable in providing a strategic overview of computer vision. With extensive use of probabalistic methods-- topics have been selected for their importance, both practically and theoretically--the book gives the most coherent possible synthesis of current views, emphasizing techniques that have been successful in building applications. Readers engaged in computer graphics, robotics, image processing, and imaging in general will find this text an informative reference.KEY FEATURESApplication Surveys--Numerous examples, including Image Based Rendering and Digital LibrariesBoxed Algorithms--Key algorithms broken out and illustrated in pseudo codeSelf-Contained--No need for other referencesExtensive, Detailed Illustrations--Examples of inputs and outputs for current methodsProgramming Assignments--50 programming assignments and 150 exercisesshow more

Table of contents

I. IMAGE FORMATION AND IMAGE MODELS. 1. Cameras. 2. Geometric Camera Models. 3. Geometric Camera Calibration. 4. Radiometry - Measuring Light. 5. Sources, Shadows and Shading. 6. Color. II. EARLY VISION: JUST ONE IMAGE. 7. Linear Filters. 8. Edge Detection. 9. Texture. III. EARLY VISION: MULTIPLE IMAGES. 10. The Geometry of Multiple Views. 11. Stereopsis. 12. Affine Structure from Motion. 13. Projective Structure from Motion. IV. MID-LEVEL VISION. 14. Segmentation By Clustering. 15. Segmentation By Fitting a Model. 16. Segmentation and Fitting Using Probabilistic Methods. 17. Tracking with Linear Dynamic Models. V. HIGH-LEVEL VISION: GEOMETRIC MODELS. 18. Model-Based Vision. 19. Smooth Surfaces and Their Outlines. 20. Aspect Graphs. 21. Range Data. VI. HIGH-LEVEL VISION: PROBABILISTIC AND INFERENTIAL METHODS. 22. Finding Templates Using Classifiers. 23. Recognition By Relations Between Templates. 24. Geometric Templates From Spatial Relations. VII. APPLICATIONS. 25. Application: Finding in Digital Libraries. 26. Application: Image-Based more

About David A. Forsyth

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 more

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61 ratings
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1 2% (1)
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