By Ayres F., et al.
The presence of orientated positive aspects in pictures frequently conveys vital information regarding the scene or the gadgets contained; the research of orientated styles is a crucial activity within the basic framework of picture knowing. As in lots of different functions of desktop imaginative and prescient, the overall framework for the certainty of orientated positive aspects in photos might be divided into low- and high-level research. within the context of the research of orientated good points, low-level research contains the detection of orientated positive aspects in pictures; a degree of the neighborhood value and orientation of orientated positive aspects over the total zone of research within the photo is named the orientation box. High-level research pertains to the invention of styles within the orientation box, frequently by way of associating the constitution perceived within the orientation box with a geometric version. This ebook provides an research of numerous very important tools for the detection of orientated positive aspects in photos, and a dialogue of the section portrait strategy for high-level research of orientation fields. with a purpose to illustrate the options built during the e-book, an software is gifted of the section portrait technique to computer-aided detection of architectural distortion in mammograms. desk of Contents: Detection of orientated beneficial properties in pictures / research of orientated styles utilizing section pics / Optimization ideas / Detection of websites of Architectural Distortion in Mammograms
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Additional resources for Analysis of oriented texture with applications to the detection of architectural
The nonlinear least-squares algorithm is a local-minimum search procedure where the nonlinear function to be minimized is formed by the sum of squared functions. A general formulation of the nonlinear least-squares procedure is as follows. || denotes the L2 -norm of the vector. All nonlinear least-squares methods proceed from an initial guess z0 of the optimal parameters, and at each iteration, the following linearized optimization problem is solved: 1 ||f (zi ) + J z|| , 2 where J is the Jacobian matrix of f (z) computed at zi , and z is a step in the parameter space towards an improved estimate.
3. 2. The detection performance is defined in terms of the ability of each filter to detect linear structures in the presence of noise and imprecision in the specification of scale. The orientation accuracy is given in terms of the cumulative angle error for the pixels belonging to the lines in the test pattern used. 1 PERFORMANCE IN TERMS OF DETECTION AND ANGULAR ACCURACY A test image of size 512 × 512 pixels was employed in the investigation of the detection performance and orientation accuracy of each oriented feature detector.
2. The filter bank is composed of 180 filters, spanning the range of orientations 0◦ to 179◦ in steps of 1◦ . The parameters of each individual filter were τ = 4 pixels and l = 8. The textured image and the orientation field components were trimmed to a size of 256 × 256 pixels, by retaining only the central portion of each component. This step was performed in order to avoid border effects at the edges of M(x, y) and θ(x, y). 2. 2. The size of the analysis window was 10 × 10 pixels, which was slid one pixel per step.
Analysis of oriented texture with applications to the detection of architectural by Ayres F., et al.