Journal ArticleDOI
Gray-Level Manipulation Experiments for Texture Analysis
E. B. Troy,E. S. Deutsch,Azriel Rosenfeld +2 more
- Vol. 3, Iss: 1, pp 91-98
TLDR
Some gray-level manipulation techniques are described, the first of which involves changing thegray-level distribution within the picture, and a method for extracting relatively noise-free objects from a noisy background is described.Abstract:
Some gray-level manipulation techniques are described, the first of which involves changing the gray-level distribution within the picture. Thereafter a method for extracting relatively noise-free objects from a noisy background is described. The purpose of these techniques is to preprocess a textural scene for subsequent analysis or classification. The present state of the art of texture analysis in general does not enable the parameters associated with the techniques to be generalized. Results for a range of parameters for both real and computer-generated pictures are therefore given.read more
Citations
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Texture analysis using gray level run lengths
TL;DR: In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.
Texture analysis using grey level run lengths
TL;DR: A set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a set of samples representing nine terrain types.
Book
Digital Picture Processing, Volume 1
Azriel Rosenfeld,Avinash C. Kak +1 more
TL;DR: The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition as discussed by the authors.
Journal ArticleDOI
Statistical feature matrix for texture analysis
Chung-Ming Wu,Yung-Chang Chen +1 more
TL;DR: A new approach using the statistical feature matrix, which measures the statistical properties of pixel pairs at several distances, within an image, is proposed for texture analysis, which is better than the spatial gray-level dependence method and the spatial frequency-based method.
References
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Visual texture analysis
Azriel Rosenfeld,E.B. Troy +1 more
TL;DR: In this article, differentiating between the coarsenesses of samples of a given texture may be successfully effected using any of the following measures: (1) amount of edge per unit area, (2) self-match (as measured by sum of absolute differences) over a unit shift, (3) Gray value dependency, and (4) number of relative extrema per area.