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. on the subject of texture, that is a major visible cue, this challenge has hitherto obtained 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 singular taxonomy for textures, and relies on acceptable mathematical versions for other kinds of texture. The taxonomy classifies textures into the vast sessions of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are defined via statistical mea- sures, strongly ordered textures through the situation of primitives, and weakly ordered textures by means of an orientation box. Compositional textures are made out of those 3 periods of texture by utilizing definite 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 wide selection of textured photos bobbing 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 a variety of functional applications.

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The gradient of the smoothed image is computed using finite differences. Let Gx(i, j) and Gy(i,j) be the x and y components of the gradient vector at point (i, j). In order to calculate the orientation at a point, one needs to combine the gradient orientations in the neighborhood of that point. As Kass and Witkin pointed out [65], one cannot smooth the gradient vectors, as they tend to cancel each other out at intensity ridges. There are several methods that one can use to avoid such cancellation, and we shall present a best estimate for the dominant orientation within a neighborhood.

16 provides the same estimate. In fact, this result holds for any texture that can be expressed as a linear combination of sine wave patterns. e. textures that cannot be expressed as a linear combination of sine wave patterns), as illustrated in the next section. 16. However, the behaviour of the orientation estimation algorithm is not critically dependent on 0" 1 or 0"2. It is only when these parameters are varied significantly that different responses could result, as shown in the next section.

Thus, the coherence image combined with the overlayed angle image provides a good description of the underlying flowlike texture. The filter sizes of 5 and 7 have been arbitrarily chosen at this point. 5(d) show the application of the orientation estimation algorithm at different scales to the same texture. The nature of the orientation field does not change significantly even though the filter sizes have been increased significantly. The results indicate that for the texture in question, the choice of scale does not play an important role in the description of the texture.

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