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

By Marco Alexander Treiber

Rapid improvement of computing device has enabled utilization of computerized item popularity in a growing number of functions, starting from commercial snapshot processing to scientific purposes, in addition to initiatives caused by way of the common use of the web. every one sector of software has its particular specifications, and accordingly those can't all be tackled adequately by way of a unmarried, general-purpose set of rules.

This easy-to-read text/reference offers a complete advent to the sector of item popularity (OR). The e-book offers an outline of the various purposes for OR and highlights vital set of rules sessions, offering consultant instance algorithms for every category. The presentation of every set of rules describes the elemental set of rules circulate intimately, whole with graphical illustrations. Pseudocode implementations also are incorporated for lots of of the equipment, and definitions are provided for phrases that could be unusual to the beginner reader. helping a transparent and intuitive instructional sort, the use of arithmetic is saved to a minimum.

Topics and features:

  • Presents instance algorithms protecting worldwide methods, transformation-search-based tools, geometrical version pushed equipment, 3D item reputation 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 a close description of the circulation of every set of rules, together with graphical illustrations
  • Explains the $64000 strategies at size in a simple-to-understand variety, with a minimal utilization of mathematics
  • Discusses a vast spectrum of functions, together with a few examples from advertisement products
  • Contains appendices discussing themes on the topic of OR and regularly occurring within the algorithms, (but no longer on the center of the equipment defined within the chapters)

Practitioners of commercial picture processing will locate this easy advent and assessment to OR a beneficial reference, as will graduate scholars in computing device imaginative and prescient courses.

Marco Treiber is a software program developer at ASM meeting platforms, Munich, Germany, the place he's Technical Lead in photo 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

Example text

A suitable transformation is based on the principal component analysis (PCA), which aims at representing the variations of the object appearance with as few dimensions as possible. It has been suggested by 32 2 Global Methods Turk and Pentland [14] for face recognition, Murase and Nayar [10] presented an algorithm for recognition of more general 3D objects in a single 2D image, which were taken from a wide rage of viewpoints. The goal is to perform a linear transformation in order to rearrange a data set of sample images such that most of its information is concentrated in as few coefficients as possible.

8) (x,y)∈R The second-order central moments are of special interest as they help to define the dimensions and orientation of the object. In order to achieve this we approximate the object region by an ellipse featuring moments of order 1 and 2 which are identical to the moments of the object region. An ellipse is defined by five parameters which can be derived form the moments. Namely these parameters are the center of gravity defined by n10 and n01 , the major and minor axes a and b as well as the orientation φ.

Hough [7] or Duda and Hart [4]), but can be generalized to the detection of arbitrarily shaped objects if the object shape is known in advance. Now let’s have a look at the basic idea of the Hough transform: given a set of points P, every pixel p = x, y ∈ p could possibly be part of a line. In order to detect all lines contained in P, each p “votes” for all lines which pass through that pixel. 4) each of those lines can be characterized by two parameters r and α. A 2D accumulator space covering all possible [r, α], which is divided into cells, accounts for the votes.

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