By A. Ravishankar Rao
A important factor in desktop imaginative and prescient is the matter of sign to image transformation. when it comes to texture, that's a tremendous visible cue, this challenge has hitherto got little or no recognition. This e-book provides an answer to the sign to image transformation challenge for texture. The symbolic de- scription scheme contains a singular taxonomy for textures, and relies on applicable 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 by means of the location of primitives, and weakly ordered textures by way of an orientation box. Compositional textures are produced from those 3 sessions of texture through the use of sure 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 photos 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 taking place in a variety of functional applications.
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Extra info for A Taxonomy for Texture Description and Identification
Structural approaches try to describe a repetitive texture in terms of the primitive elements and placement rules that describe geometrical relationships between these elements. We will refer to those textures that are amenable to a structural description as being strongly ordered textures. An important category of textures which can neither be modeled statistically nor structurally is that of weakly ordered, or oriented textures. Such textures are characterized by a dominant local orientation at each point of the texture, which can vary arbitrarily.
Let the gradient vector at point (m, n) in the image have the polar representation Rmnei8mn. 16) R~ncos2(}mn m=l n=l The estimated orientation angle at (m, n) is then () mn + 1r /2, since the gradient vector is perpendicular to the direction of anisotropy. 16. In all our results we display the estimated orientation angle overlayed on the original image. 2 DERIVATION USING THE MOMENT METHOD The derivation presented above is for the two dimensional case, and is equivalent to the moment method, which holds for N dimensions.
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. Unit vectors representing the estimated flow directions are superimposed on the coherence map. , , I. I " I I J I - ... - I ~"\ , ,..... I \.. I I ,. -/' ~ 1 ,, ..... I. \ f~ \ ..... - ,\ \ II \ I. • ..... , ... ," \0 " ~\ .....
A Taxonomy for Texture Description and Identification by A. Ravishankar Rao