By Ayres F., et al.
The presence of orientated positive factors in photos frequently conveys very important information regarding the scene or the gadgets contained; the research of orientated styles is a vital job within the normal framework of snapshot realizing. As in lots of different purposes of machine imaginative and prescient, the final framework for the knowledge of orientated gains in pictures should be divided into low- and high-level research. within the context of the learn of orientated gains, low-level research contains the detection of orientated beneficial properties in photographs; a degree of the neighborhood significance and orientation of orientated beneficial properties over the complete zone of research within the snapshot is termed the orientation box. High-level research pertains to the invention of styles within the orientation box, frequently by means of associating the constitution perceived within the orientation box with a geometric version. This booklet provides an research of a number of very important tools for the detection of orientated positive aspects in photographs, and a dialogue of the part portrait procedure for high-level research of orientation fields. in an effort to illustrate the techniques built in the course of the ebook, an software is gifted of the section portrait approach to computer-aided detection of architectural distortion in mammograms. desk of Contents: Detection of orientated good points in photographs / research of orientated styles utilizing part pics / Optimization recommendations / Detection of web sites of Architectural Distortion in Mammograms
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Additional resources for Analysis of oriented texture with applications to the detection of architectural
2. ANALYSIS OF ORIENTATION FIELDS USING PHASE PORTRAITS 35 The vector v is an affine function of the coordinates (x, y). 2. 2 by associating the vector v with the state velocity (p(t), ˙ q(t)), ˙ and the position (x, y) with the state (p(t), q(t)). 8) which is the angle of the vector v with the x axis. 1 lists the three phase portraits and the corresponding orientation fields generated by a system of linear first-order differential equations. 2. Let us denote by M(x, y) and θ(x, y) the magnitude and angle components of the orientation field of I (x, y).
DISCUSSION 23 by the steerable filter, the quadrature steerable filter, the real Gabor filter, and the complex Gabor filter. 8a. 20 GHz Pentium 4 processor, 2 MB of cache, and 2 GB of RAM). 75 DISCUSSION The aim of the analysis described in this chapter was to develop design criteria and conduct performance analysis of five oriented feature detectors, in terms of their ability in the detection of oriented features and accuracy in the determination of the angle of the oriented features of interest.
Journal of Electronic Imaging, 16(2):12 pages, April 2007. article number 023007. 6. 12. The orientation field was obtained using a bank of 180 Gabor filters. Line segments are drawn every 10 pixels, indicating the orientation field angle. 1. Reproduced with permission from F. J. Ayres and R. M. Rangayyan. “Design and performance analysis of oriented feature detectors”. Journal of Electronic Imaging, 16(2):12 pages, April 2007. article number 023007. © SPIE 31 CHAPTER 2 Analysis of Oriented Patterns Using Phase Portraits Consider a system of two linear, first-order, differential equations.