Analysis of Oriented Texture with Applications to the by Fábio J. Ayres, Rangaraj M. Rangayyan, J.E. Leo Desautels

By Fábio J. Ayres, Rangaraj M. Rangayyan, J.E. Leo Desautels

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 knowing. As in lots of different purposes of desktop imaginative and prescient, the overall framework for the knowledge of orientated positive aspects in photos will be divided into low- and high-level research. within the context of the learn of orientated good points, low-level research comprises the detection of orientated positive factors in pictures; a degree of the neighborhood significance and orientation of orientated beneficial properties over the total sector of research within the photo is named the orientation box. High-level research pertains to the invention of styles within the orientation box, often by way of associating the constitution perceived within the orientation box with a geometric version. This publication offers an research of a number of very important tools for the detection of orientated positive aspects in pictures, and a dialogue of the section portrait approach for high-level research of orientation fields. so as to illustrate the ideas constructed during the e-book, an program is gifted of the part portrait technique to computer-aided detection of architectural distortion in mammograms. desk of Contents: Detection of orientated gains in photographs / research of orientated styles utilizing part pics / Optimization recommendations / Detection of websites of Architectural Distortion in Mammograms

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Extra resources for Analysis of Oriented Texture with Applications to the Detection of Architectural (Synthesis Lectures on Biomedical Engineering)

Sample text

Observe that all of the oriented feature detectors provide a stronger detection at the middle of the multiscale test image than at its ends, as expected. For thinner lines, the oriented feature detectors exhibit a less significant response than those for the lines in the middle of the test image. 11c). It can be observed that, in the case of the quadrature steerable filter and the complex Gabor filter, only the edges of the thicker line features have been identified as oriented features. 2. 3. ) 21 22 1.

After all votes are cast, the phase portrait maps may be analyzed to detect the presence of patterns in the given image or orientation field. If a portion of the orientation field is comprised of orientations radiating from a central point, in a manner similar to a node pattern, it is expected that the node map will contain an agglomeration of votes close to the geometrical focus of the observed pattern. Likewise, the presence of patterns that are similar to saddle or spiral patterns will cause agglomerations of votes in the respective phase portrait maps.

A) Steerable filter. (b) Quadrature steerable filter. (c) Real Gabor filter. (d) Complex Gabor filter. (e) Line operator. 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. 3. 1: Robustness measure, best scale, and detection range for each oriented feature detector. 1 in the test image. 5] in standard deviation was used to derive the measure of robustness.

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