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

By A. Ravishankar Rao

A important factor in machine imaginative and prescient is the matter of sign to image transformation. in terms of texture, that is an enormous visible cue, this challenge has hitherto got little or no realization. This booklet provides 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 large sessions of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are defined by means of statistical mea- sures, strongly ordered textures by means of the location of primitives, and weakly ordered textures by means of an orientation box. Compositional textures are made from those 3 sessions of texture through the use of definite principles of composition. The unifying subject 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 photos coming up in semiconductor wafer inspection, move 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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Extra resources for A Taxonomy for Texture Description and Identification

Sample text

One may think that it is sufficient to sum up these line segments vectorially and find the resultant direction. However, this will not work for two reasons. Firstly, any given line segment does not have a unique direction, since it could be taken to point either in the direction e or e + 7r. Secondly, even if the line segments were assigned directions, there is the danger that segments pointing in opposite directions will cancel each other out, instead of influencing the choice of dominant orientation as they should.

4. After computing the local orientation of the texture field, the orientation estimate must be smoothed to compute the average orientation over a neighborhood of significant size. 1. Let 0'1 be the width of the first Gaussian smoothing filter used to compute the local texture orientation and 0'2 be the width of the second smoothing filter used to average the orientation estimate. The averaging filter must be large enough to average the orientation from several of the local estimates and so 0'2 ¬Ľ 0'1, but the averaging filter should be smaller than the distance over which the orientation of the texture field undergoes major changes; in other words, the averaging filter should not blur changes in the texture field.

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. Note that the coherence is low within the cylinder. 2. 5. 16) using filter sizes of 0"1 = 9 and 0"2 = 13.

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