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

By A. Ravishankar Rao

A principal factor in laptop imaginative and prescient is the matter of sign to image transformation. on the subject of texture, that is an incredible visible cue, this challenge has hitherto bought little or no awareness. This publication provides an answer to the sign to image transformation challenge for texture. The symbolic de- scription scheme contains a singular taxonomy for textures, and is predicated on acceptable mathematical types 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 location of primitives, and weakly ordered textures via an orientation box. Compositional textures are made from those 3 periods of texture by utilizing yes principles of composition. The unifying topic of this booklet is to supply standardized symbolic descriptions that function a descriptive vocabulary for textures. The algorithms built within the e-book were utilized to a large choice of textured photographs coming up in semiconductor wafer inspection, circulate visualization and lumber processing. The taxonomy for texture can function a scheme for the id and outline of floor flaws and defects happening in quite a lot of sensible applications.

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Their work is directed towards computing an interpretation of the texture. They compute edge repetition arrays, which are similar to co-occurrence matrices, from the edge detector outputs. The algorithm presented in this paper does not require the explicit computation of the edge repetition structure as the coherence and angle intrinsic images playa similar role, but the algorithm presented in this chapter is restricted to flow-like textures that can be modeled by coherence and angle alone. Witkin [127] presents an algorithm for computing surface orientation from texture the possible directions for the tangents of markings on the surface are equally likely, and surface orientation and tangent direction are independent.

5. (a) An image of a flow pattern induced by an oscillating cylinder (Photograph courtesy M. Van Dyke). 16) are represented by line segments overlayed on the original image. Filter sizes used were 0"1 = 5 and 0"2 = 7. The length of each line segment is proportional to the coherence at that point. Thus this image directly encodes the information about flow direction and flow coherence. (b) The coherence map. 24) is encoded as an intensity value. Filter sizes used were 0"1 = 5 and 0"2 = 7. Unit vectors representing the estimated flow directions are superimposed on the coherence map.

We present a precise technique for providing symbolic descriptions, based on the equivalence classes of the frieze groups and the wallpaper groups. The aim of this chapter is to take a critical look at the research done in the structural analysis of textures, with an eye for ultimately deriving accurate symbolic descriptors for ordered textures. Thus, we provide directions for future research in ordered textures based on existing research. 6. Outline CHAPTER 5 In chapter 5 we analyze disordered textures.

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