By A. Ravishankar Rao
A relevant factor in machine imaginative and prescient is the matter of sign to image transformation. on the subject of texture, that's a massive visible cue, this challenge has hitherto got little or no recognition. This e-book provides an answer to the sign to image transformation challenge for texture. The symbolic de- scription scheme comprises a unique taxonomy for textures, and is predicated on acceptable mathematical versions for other kinds of texture. The taxonomy classifies textures into the extensive periods of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are defined via statistical mea- sures, strongly ordered textures by means of the location of primitives, and weakly ordered textures by way of an orientation box. Compositional textures are produced from those 3 sessions of texture through the use of sure principles of composition. The unifying topic of this booklet is to supply standardized symbolic descriptions that function a descriptive vocabulary for textures. The algorithms constructed within the publication were utilized to a wide selection of textured pictures bobbing up in semiconductor wafer inspection, stream visualization and lumber processing. The taxonomy for texture can function a scheme for the id and outline of floor flaws and defects taking place in quite a lot of functional applications.
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Additional info for A Taxonomy for Texture Description and Identification
We propose to extend our work in this direction in the future, where we will employ a bank of filters, tuned at different scales in order perform orientation estimation. One can then use measures based on the coherence of the output to determine the scales at 2. 13. (a) Analyzing the behaviour of the texture at varying scales. This figure shows the angle and coherence maps obtained by using a filter of window size 5 pixels to compute the gradient and a filter of window size 7 pixels to smooth the gradient vectors.
4. The original image of the orange peel defect. The problem here is to come up with a quantitative measure for the amount of wrinkle present on the wafer. In chapter 2 we give one plausible measure 1. 5. An image of resist gel defect, obtained from [32, pg. 254] (Reproduced with permission from Integrated Circuit Mask Technology by D. J. Elliott, pg. 254, ©1985 McGraw Hill). In chapter 3 we analyze this defect, and come up with a symbolic description. 5. A taxonomy for texture A taxonomy for texture Most of the research done in texture is concerned with either statistical or structural approaches.
12 (a). The coherence measure that we propose incorporates the gradient magnitude and hence places more weight on regions that have higher visual contrast. 3 and G*(i,j) is the smoothed gradient magnitude, obtained by smoothing the gradient magnitude with a Gaussian filter. 12. 1. 16. We shall restrict our analysis to the ideal case first. Consider again the example of a sine wave grating, say S(x, y) which is an ideal oriented texture. 16. This equation has embedded in it two smoothing operations: (1) smoothing with a Gaussian with variance 0'1; (2) a smoothing with a box filter of width 0'2.