By Marco Alexander Treiber
Rapid improvement of machine has enabled utilization of automated item attractiveness in progressively more functions, starting from business picture processing to clinical purposes, in addition to projects caused through the common use of the net. each one region of program has its particular requisites, and therefore those can't all be tackled adequately by means of a unmarried, general-purpose set of rules.
This easy-to-read text/reference offers a entire advent to the sphere of item popularity (OR). The e-book provides an summary of the various functions for OR and highlights very important set of rules sessions, featuring consultant instance algorithms for every type. The presentation of every set of rules describes the fundamental set of rules circulation intimately, entire with graphical illustrations. Pseudocode implementations also are integrated for plenty of of the tools, and definitions are provided for phrases that could be unexpected to the beginner reader. assisting a transparent and intuitive educational kind, using arithmetic is stored to a minimum.
Topics and features:
- Presents instance algorithms overlaying international techniques, transformation-search-based equipment, geometrical version pushed tools, 3D item acceptance schemes, versatile contour becoming algorithms, and descriptor-based methods
- Explores each one procedure in its entirety, instead of targeting person steps in isolation, with an in depth description of the move of every set of rules, together with graphical illustrations
- Explains the real options at size in a simple-to-understand kind, with a minimal utilization of mathematics
- Discusses a large spectrum of functions, together with a few examples from advertisement products
- Contains appendices discussing themes on the topic of OR and time-honored within the algorithms, (but no longer on the middle of the equipment defined within the chapters)
Practitioners of business photograph processing will locate this straightforward creation and evaluate to OR a worthwhile reference, as will graduate scholars in laptop imaginative and prescient courses.
Marco Treiber is a software program developer at ASM meeting platforms, Munich, Germany, the place he's Technical Lead in picture Processing for the imaginative and prescient approach of SiPlace placement machines, utilized in SMT assembly.
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Extra info for An Introduction to Object Recognition: Selected Algorithms for a Wide Variety of Applications
Moments can also be combined in such a way that the resulting value is invariant with respect to certain coordinate transforms like translation, rotation, or scaling, which is a very desirable property. An example of these so-called moment invariants is given later in this book (in Chapter 7). , in the spectral domain. The feature vector calculated from a data representation in the transformed domain, which is called fourier descriptor, is considered to be more robust with respect to noise or minor boundary modifications.
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.
The moment m00 , for example, defines the area of the region. In order to calculate moments as characteristic features of objects independent of their size, normalized moments npq are utilized. 7) 26 2 Global Methods The first-order normalized moments n10 and n01 define the center of gravity of the object, which can be interpreted as the position of the object. In most applications requiring a classification of objects, the moments should be independent of the object position. 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.
An Introduction to Object Recognition: Selected Algorithms for a Wide Variety of Applications by Marco Alexander Treiber