By Marco Alexander Treiber

ISBN-10: 1849962340

ISBN-13: 9781849962346

Rapid improvement of machine has enabled utilization of automated item reputation in more and more functions, starting from business photo processing to scientific purposes, in addition to projects prompted through the common use of the web. each one region of program has its particular specifications, and as a result those can't all be tackled adequately via a unmarried, general-purpose set of rules.

This easy-to-read text/reference presents a finished advent to the sphere of item reputation (OR). The publication provides an summary of the varied functions for OR and highlights very important set of rules sessions, featuring consultant instance algorithms for every classification. The presentation of every set of rules describes the elemental set of rules move intimately, entire with graphical illustrations. Pseudocode implementations also are integrated for plenty of of the tools, and definitions are provided for phrases that may be surprising to the amateur reader. assisting a transparent and intuitive instructional type, using arithmetic is saved to a minimum.

Topics and features:

  • Presents instance algorithms overlaying international ways, transformation-search-based tools, geometrical version pushed tools, 3D item acceptance schemes, versatile contour becoming algorithms, and descriptor-based methods
  • Explores each one technique in its entirety, instead of concentrating on person steps in isolation, with an in depth description of the circulation of every set of rules, together with graphical illustrations
  • Explains the $64000 techniques at size in a simple-to-understand sort, with a minimal utilization of mathematics
  • Discusses a vast spectrum of purposes, together with a few examples from advertisement products
  • Contains appendices discussing subject matters relating to OR and general within the algorithms, (but now not on the center of the tools defined within the chapters)

Practitioners of business snapshot processing will locate this straightforward advent and evaluate to OR a worthwhile reference, as will graduate scholars in desktop imaginative and prescient courses.

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

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

Example text

A basic and easy to implement scheme used for the classification of global feature vectors is nearest neighbor classification, where a feature vector, which was derived from an input image, is compared to feature vectors of known class type which were calculated during a training stage. The object is classified based on the class labels of the most similar training vectors (the nearest neighbors, see Appendix B). 3 Rating Compared to correlation, classification in the feature space typically is faster.

Fig. 10 The cloud in the left part illustrates the region containing the training “images” in a 3D space. Clearly, a 2D representation signalized by the blue arrows is better suited than the original 3D representation signalized by the black arrows. The effect of dimensionality reduction is illustrated in the right part: its geometric interpretation is a projection of a sample point (3D, blue arrow) onto a subspace (here a 2D plane, red arrow). 3 Principal Component Analysis (PCA) 33 With a good choice of the projection parameters, the error En2 of this estimation should be minimized.

Fourier descriptors can be computed and matched fast and have the desirable property to incorporate global and local information. If the number of coefficients is chosen sufficiently large, Fourier descriptors can overcome the disadvantage of a rather weak discrimination ability, which is a common problem of global feature vectors such as moments. The characterization of objects by means of Fourier descriptors is not restricted to the object boundary (as it is the case with the centroid distance function).

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An Introduction to Object Recognition: Selected Algorithms for a Wide Variety of Applications by Marco Alexander Treiber


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