Download An Introduction to Object Recognition: Selected Algorithms by Marco Alexander Treiber PDF

By Marco Alexander Treiber

ISBN-10: 1849962340

ISBN-13: 9781849962346

Rapid improvement of computing device has enabled utilization of computerized item popularity in increasingly more functions, starting from business snapshot processing to clinical purposes, in addition to projects brought on via the frequent use of the net. each one region of software has its particular standards, and hence those can't all be tackled effectively through a unmarried, general-purpose set of rules.

This easy-to-read text/reference offers a accomplished advent to the sphere of item acceptance (OR). The booklet offers an summary of the varied purposes for OR and highlights vital set of rules sessions, offering consultant instance algorithms for every classification. The presentation of every set of rules describes the fundamental set of rules circulation intimately, whole with graphical illustrations. Pseudocode implementations also are integrated for lots of of the equipment, and definitions are provided for phrases that may be unexpected to the beginner reader. helping a transparent and intuitive instructional type, the use of arithmetic is saved to a minimum.

Topics and features:

  • Presents instance algorithms overlaying international techniques, transformation-search-based equipment, geometrical version pushed equipment, 3D item reputation schemes, versatile contour becoming algorithms, and descriptor-based methods
  • Explores each one approach in its entirety, instead of concentrating on person steps in isolation, with a close description of the move of every set of rules, together with graphical illustrations
  • Explains the $64000 thoughts at size in a simple-to-understand variety, with a minimal utilization of mathematics
  • Discusses a huge spectrum of purposes, together with a few examples from advertisement products
  • Contains appendices discussing themes regarding OR and standard within the algorithms, (but now not on the center of the equipment defined within the chapters)

Practitioners of business snapshot processing will locate this easy creation and evaluate to OR a necessary reference, as will graduate scholars in machine imaginative and prescient courses.

Marco Treiber is a software program developer at ASM meeting structures, Munich, Germany, the place he's Technical Lead in snapshot Processing for the imaginative and prescient method of SiPlace placement machines, utilized in SMT assembly.

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Additional info for An Introduction to Object Recognition: Selected Algorithms for a Wide Variety of Applications

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Pattern Classification”, Wiley, New York, 2000, ISBN 0-471-05669-3 7. , “The Use of Multiple Measures in Taxonomic Problems”, Annals of Eugenics, 7:179–188, 1936 8. Flusser, J. , “Pattern Recognition by Affine Moment Invariants”, Pattern Recognition, 26(1):167–174, 1993 9. , Kobayashi, K. , “An Effective Approach for Iris Recognition Using Phase-Based Image Matching”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(10):1741–1756, 2008 10. Murase, H. , “Visual Learning and Recognition of 3-D Objects from Appearance”, International Journal Computer Vision, 14:5–24, 1995 11.

The maxima are a bit sharper compared to the standard method. , Ballard and Brown [1], a book which also gives an excellent introduction to and overview of many aspects of computer vision). The bottom level 0 of the pyramid consists of the original image whereas the higher levels are built by subsampling or averaging the intensity values of adjacent pixels of the level below. Therefore at each level the image size is reduced (see Fig. 4). Correlation initially takes place in a high level of the pyramid generating some hypotheses about coarse object locations.

Black) Scene image containing seven toys with different appearance and particularly size Magnitude of gray value gradient of the scene image (high-gradient values are shown in black) Correlation result when using raw gray values: flat maxima, low discriminative power. 2 Global Feature Vectors Another possibility to model object properties are so-called global feature vectors. Each element of the vector describes a global characteristic of the object. , the number of pixels covered by the object), perimeter, circularity (perimeter2 /area), moments, mean gray value, Fourier descriptors, etc.

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