By A. Ravishankar Rao
A valuable factor in desktop imaginative and prescient is the matter of sign to image transformation. on the subject of texture, that's an enormous visible cue, this challenge has hitherto bought little or no awareness. This booklet offers an answer to the sign to image transformation challenge for texture. The symbolic de- scription scheme involves a unique taxonomy for textures, and relies on applicable mathematical types for other kinds of texture. The taxonomy classifies textures into the huge sessions 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 by means of an orientation box. Compositional textures are made from those 3 periods of texture through the use of yes ideas of composition. The unifying subject matter of this publication 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 photos bobbing up in semiconductor wafer inspection, circulate visualization and lumber processing. The taxonomy for texture can function a scheme for the identity and outline of floor flaws and defects happening in a variety of functional applications.
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Extra info for A Taxonomy for Texture Description and Identification
8a shows a knot in a piece of wood, scanned from a book of textures . The orientation field swirls around the knot, though the fluid flow analogy is not entirely correct as there is no directionality to the vectors: it is an orientation field, not a vector field. 8. The orientation vectors are drawn without arrow heads since the flow structure is non-directional. 9 shows a herringbone weave texture, obtained from. 20. Filter sizes used were 0"1 = 5 pixels and 0"2 = 7 pixels for both schemes.
Hence, both quantitative and qualitative techniques are required for the description and measurement of texture. This is precisely the focus of the book. Thus, the book fulfills a very practical need imposed on computer vision systems. 4. 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.
Consider the 2. 3. Illustration of the method used to compute the coherence of the texture flow field. 4. 4. 22 to the flow image on the left. (Photograph courtesy M. 22) (i,j)EW This measure is related to the dispersion of directional data [82J. Assume that we have n data points, where the ith point Pi is at an angle (}i, and lies on the unit circle. Let 0: be a fixed direction. 23) i=l Thus if the measure D is generalized to data that does not necessarily lie on the unit circle, then it follows that ~ = 1 - D.
A Taxonomy for Texture Description and Identification by A. Ravishankar Rao