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A Taxonomy for Texture Description and Identification by A. Ravishankar Rao

By A. Ravishankar Rao

A important factor in laptop imaginative and prescient is the matter of sign to image transformation. in relation to texture, that is an immense visible cue, this challenge has hitherto got little or no consciousness. This booklet offers an answer to the sign to image transformation challenge for texture. The symbolic de- scription scheme comprises a singular 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 through statistical mea- sures, strongly ordered textures by way of the situation of primitives, and weakly ordered textures through an orientation box. Compositional textures are made out of those 3 sessions of texture by utilizing yes ideas of composition. The unifying topic of this e-book is to supply standardized symbolic descriptions that function a descriptive vocabulary for textures. The algorithms built within the ebook were utilized to a large choice of textured photographs bobbing up in semiconductor wafer inspection, movement visualization and lumber processing. The taxonomy for texture can function a scheme for the identity and outline of floor flaws and defects taking place in a variety of functional applications.

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The estimated orientation angle at (i, j) is then Btj + 7r /2. 5 FLOW ORIENTATION COHERENCE Let B(x, y) denote the estimated orientation angle at point (x, y), found in the earlier step. 3. To find the coherence at point (xo, Yo), consider the point (Xi, Yj), where i and j are chosen so that they fall within a window W of prescribed size around the point (xo, yo). Project the gradient magnitude G(Xi' Yj) taken in the direction B(Xi' Yj) onto the unit vector in the direction B(xo,Yo). This will be G(Xi,Yj) cos(B(xo, Yo) - B(Xi,Yj)).

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.

Problem: Given vectors VI, ",vK' estimate the average orientation when the sign of Vi is ignored. ) T designates the transpose. Proof: Let v be the unit vector along the average orientation to be estimated. ) denotes the inner product. 18) 2. 19) This quadratic form in v is maximized under the constraint I v 1= 1. The solution is given by the eigenvector v of M corresponding to the maximium eigenvalue, a well known result in linear algebra. From this result, the following observations can be made.

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