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

By A. Ravishankar Rao

A principal factor in desktop imaginative and prescient is the matter of sign to image transformation. in relation to texture, that is a major visible cue, this challenge has hitherto got little or no awareness. This booklet provides an answer to the sign to image transformation challenge for texture. The symbolic de- scription scheme includes a singular taxonomy for textures, and is predicated on applicable mathematical types for other kinds of texture. The taxonomy classifies textures into the large sessions of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are defined through statistical mea- sures, strongly ordered textures through the situation of primitives, and weakly ordered textures by means of an orientation box. Compositional textures are produced from those 3 periods of texture through the use of definite principles of composition. The unifying topic of this ebook is to supply standardized symbolic descriptions that function a descriptive vocabulary for textures. The algorithms constructed within the ebook were utilized to a wide selection of textured pictures coming up in semiconductor wafer inspection, stream 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 sensible applications.

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Sample text

In most edge detection algorithms, the angle of the edge covers the full range of the unit circle; but in estimating the orientation field, it is necessary to reflect orientation vectors that lie along the same line to a canonical orientation. In edge detection, the gradient angle is computed using the arctangent function of two arguments [109]; but if this function were used to compute the orientation angle in this problem, then there would not be a unique angle for each texture orientation. The texture field orientations would have to be post-processed to reflect orientation vectors into a canonical range as is apparently done by Kass and Witkin [65].

11. This figure shows how the arctangent of one argument can be used to map vectors in the xv-plane onto a half-plane. Thus, VI gets mapped onto V2. The darkly shaded area represents those regions where vectors can point in opposite directions, and can still cancel each other out. 6. 12. 24. 25, which is shown in the figure on the right. 13 proves that our algorithm indeed exhibits this behaviour. 13(a) shows the result of applying the orientation estimation algorithm at a scale of 5 to compute the gradient vectors, and a scale of 7 to smooth the gradient vectors.

This figure shows a SEM image of a GaAs wafer. The surface consists of metal deposited via evaporation on a substrate after an RIE etch process. The surface appears rough due to a poor process. The problem is to come up with a quantitative measurement for the visual appearance of surface roughness. This is provided in chapter 5 process or a deposition process. 2 shows an example of a texture that was produced by a poor deposition process. The problem here is to characterize the surface texture in a meaningful quantitative manner, which is a prevalent problem in many inspection tasks.

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