By Ayres F., et al.
The presence of orientated good points 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 common framework of photograph realizing. As in lots of different functions of machine imaginative and prescient, the final framework for the certainty of orientated positive factors in photographs may be divided into low- and high-level research. within the context of the learn of orientated beneficial properties, low-level research contains the detection of orientated good points in pictures; a degree of the neighborhood importance and orientation of orientated positive aspects over the total area of study within the snapshot is named the orientation box. High-level research pertains to the invention of styles within the orientation box, often via associating the constitution perceived within the orientation box with a geometric version. This publication offers an research of numerous vital equipment for the detection of orientated positive aspects in photographs, and a dialogue of the section portrait strategy for high-level research of orientation fields. with a view to illustrate the ideas constructed in the course of the publication, an software is gifted of the part portrait approach to computer-aided detection of architectural distortion in mammograms. desk of Contents: Detection of orientated positive aspects in pictures / research of orientated styles utilizing section photographs / Optimization options / Detection of web sites of Architectural Distortion in Mammograms
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Additional info for Analysis of oriented texture with applications to the detection of architectural
Rangayyan. “Design and performance analysis of oriented feature detectors”. 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.
Let Dj be an N × N diagonal matrix, which we shall call as the correction matrix, with the diagonal elements defined as di,i = 1 (vx )2i + (vy )2i . 14) Let M be the number of iterations (predetermined empirically as M = 5). 3. OPTIMIZATION PROCEDURES 47 1. Set D0 = I, the identity matrix. 2. For j = 1 to M (a) Apply the correction matrix to the data matrices: replace U by Dj −1 U, and V by Dj −1 V. (b) Estimate the parameters [a, b, c, d, e, f ]. (c) Evaluate Dj with the new estimated parameters.
2, three synthetic test images exhibiting oriented texture were created. Each image is associated with a specific type of phase portrait. The images were created as follows: 1. 6. Each vector was subsequently normalized to unit length. 2. A blank image of size 512 × 512 pixels was created, and subsequently filled with randomly placed salt noise , that is, white pixels. The amount of salt noise was 5% of the total number of pixels in the image. The noisy image was blurred using a Gaussian filter of standard deviation 2 pixels, resulting in an image Iorig (x, y).
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