This textbook provides an accessible general introduction to the essential topics in computer vision. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter. Features: provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for mapping an image into an image; explains the topologic and geometric basics for analysing image regions and distributions of image values and discusses identifying patterns in an image; introduces optic flow for representing dense motion and various topics in sparse motion analysis; describes special approaches for image binarization and segmentation of still images or video frames; examines the basic components of a computer vision system; reviews different techniques for vision-based 3D shape reconstruction; includes a discussion of stereo matchers and the phase-congruency model for image features; presents an introduction into classification and learning.

About the Author

Dr. Reinhard Klette, Fellow of the Royal Society of New Zealand, is a Professor at the University of Auckland. His numerous other publications include the Springer title Euclidean Shortest Paths: Exact or Approximate Algorithms, co-authored by Fajie Li, the Morgan Kaufmann title Digital Geometry, co-authored by the late Azriel Rosenfeld, and the Wiley title Image Processing Operators, co-authored by the late Piero Zamperoni.

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