By Fumiaki Tomita, Saburo Tsuji
This publication provides theories and methods for notion of textures by means of desktop. Texture is a homogeneous visible trend that we understand in surfaces of gadgets corresponding to textiles, tree barks or stones. Texture research is without doubt one of the first vital steps in desktop imaginative and prescient due to the fact that texture offers vital cues to acknowledge real-world gadgets. a tremendous a part of the publication is dedicated to two-dimensional research of texture styles through extracting statistical and structural good points. It additionally offers with the shape-from-texture challenge which addresses restoration of the three-d floor shapes according to the geometry of projection of the outside texture to the picture airplane. belief continues to be mostly mysterious. figuring out a working laptop or computer imaginative and prescient approach that may paintings within the genuine international calls for extra examine and ex periment. potential of textural notion is a key part. we are hoping this e-book will give a contribution to the development of laptop imaginative and prescient towards powerful, precious structures. vVe wish to convey our appreciation to Professor Takeo Kanade at Carnegie Mellon college for his encouragement and assist in penning this booklet; to the individuals of desktop imaginative and prescient part at Electrotechni cal Laboratory for supplying an exceptional examine atmosphere; and to Carl W. Harris at Kluwer educational Publishers for his assist in getting ready the manuscript.
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Extra resources for Computer Analysis of Visual Textures
The following texture properties are derived from first-oder statistics of the distributions of the edge elements. 1. Coarseness: The density of edge elements is a measure of texture coarseness. The finer the texture, the higher the edge density. 2. Contrast: The mean of edge strengths is a measure of contrast. 3. Randomness: The entropy of the histogram of edge strengths is a measure of randomness. 4. Directivity: The directivity is detected from the histogram of edge directions. The entropy of the histogram gives a rough measure of directivity.
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2. Generate an image Ik by merging adjacent points whose intensity difference is less than k into regions and averaging the intensities in the regions. 3. Compute the intensity histogram Hk of the image h. 3: Texture edge detection: (a) Input image; (b) Edge image; (c) Nonmaximal edge suppression. 43 4. Test whether separable clusters exist in the histogram Hk. If so, go to step 5, or else set k=k+1 (the increment may be more than 1) and go to step 2. 5. Threshold the image Io by the intensities between the clusters in the histogram Hk in order to extract regions.
Computer Analysis of Visual Textures by Fumiaki Tomita, Saburo Tsuji