Concise Computer Vision

An Introduction into Theory and Algorithms

Authors: Klette, Reinhard

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Presents an accessible general introduction to the essential topics in computer vision
Provides classroom-tested programming exercises and review questions at the end of each chapter
Includes supporting information on historical context, suggestions for further reading and hints on mathematical subjects under discussion

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About this Textbook: 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.

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About the authors: Dr. Reinhard Klette, FRSNZ, is a Professor at the Tamaki Innovation Campus of The University of Auckland, New Zealand. His numerous other publications include the Springer title Euclidean Shortest Paths: Exact or Approximate Algorithms.

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