By A. Ravishankar Rao
A principal factor in machine imaginative and prescient is the matter of sign to image transformation. when it comes to texture, that is a huge visible cue, this challenge has hitherto acquired little or no consciousness. This e-book provides an answer to the sign to image transformation challenge for texture. The symbolic de- scription scheme involves a unique taxonomy for textures, and is predicated on acceptable mathematical types for other kinds of texture. The taxonomy classifies textures into the wide periods of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are defined by way of statistical mea- sures, strongly ordered textures via the location of primitives, and weakly ordered textures through an orientation box. Compositional textures are made from those 3 periods of texture through the use of sure 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 constructed within the e-book were utilized to a large choice of textured pictures 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 quite a lot of useful applications.
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Extra info for A Taxonomy for Texture Description and Identification
Computing oriented texture fields 33 along the direction of flow. Hence the resulting pattern looks much like the original texture. 24) encoded as an image. The coherence at each point is quantized into one of 256 gray levels, and then displayed as an image. The angle vectors are then overlayed on the coherence image. The coherence image shows how orientation specificity varies over the original image - the brighter points indicating strong coherence of flow. Note that the coherence at the center of the image is low, indicating that there is no dominant flow direction in that area.
Instead of an array of disks, subjective brightness stripes are seen, which are vertically oriented. However, the control pattern does not undergo any qualitative change as a function of orientation. 14(g) through (1) show the orientation field for the rotated patterns. The results obtained by running the orientation estimation algorithm do not indicate the presence of subjective brightness stripes in the rotated pattern. This is because the coherence in the areas away from the line segments is very low, and hence the estimated direction of orientation within these areas has little meaning.
Computing oriented texture fields 43 algorithm does not exhibit the subjective effects that humans do in this case. This is to be expected, because if the algorithm did exhibit the illusion, then it would not be independent of orientation of the texture, which is contrary to the fact that it is mathematically so. 7 Processing of the intrinsic images In keeping with the philosophy of Barrow and Tenenbaum , the angle and coherence images can be regarded as intrinsic images obtained from the original oriented texture image.
A Taxonomy for Texture Description and Identification by A. Ravishankar Rao