Journal ArticleDOI
A Comparative Study of Texture Measures for Terrain Classification
Joan S. Weszka,Charles R. Dyer,Azriel Rosenfeld +2 more
- Vol. 6, Iss: 4, pp 269-285
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TLDR
In this paper, three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively.Abstract:
Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively. Feature sets of these types, all designed analogously, were used to classify two sets of terrain samples. It was found that the Fourier features generally performed more poorly, while the other feature sets all performned comparably.read more
Citations
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Journal ArticleDOI
Computational models for search and discrimination
TL;DR: An experimental framework for evaluating metrics for the search and discrimination of a natural texture pattern from its background is presented, and a metric based on a model of image texture was the most effective.
Journal ArticleDOI
Texture classification using the cortex transform
C. Goresnic,Stanley R. Rotman +1 more
TL;DR: A new and simple algorithm for implementing the cortex transform is developed and a texture classification system based on it is evaluated; the initial classification results are promising and appear to be robust with respect to a noisy environment.
Proceedings ArticleDOI
Weed Classification Using Angular Cross Sectional Intensities for Real-Time Selective Herbicide Applications
TL;DR: This paper deals with the development of an algorithm which calculates angular cross sectional intensity of an image that is used for the weed classification, specifically developed to classify images into broad and narrow class for real-time selective herbicide application.
Journal ArticleDOI
Segmenting Geometric Reliefs from Textured Background Surfaces
TL;DR: To describe geometric textures, the first classifies parts of a surface mesh as relief or background, and then uses a snake which moves inwards towards the desired relief boundary, which is coarsely located using an energy based on the classification.
Proceedings ArticleDOI
Efficient textile recognition via decomposition of co-occurrence matrices
Kar Seng Loke,Marc Cheong +1 more
TL;DR: It is shown in this work that the number of attributes can be reduced down to 2% without significantly reducing the classification rate, indicating that with the appropriate attribute reduction, fast recognition and classification of Batik and Songket textiles can be achieved.
References
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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.
Proceedings Article
Computer description of textured surfaces
TL;DR: This work deals with computer analysis of textured surfaces with descriptions of textures formalized from natural language descriptions obtained from the directional and non-directional components of the Fourier transform power spectrum.
Spectral and textural processing of ERTS imagery
R. M. Haralick,R. J. Bosley +1 more
TL;DR: In this article, a procedure is developed to simultaneously extract textural features from all bands of ERTS multispectral scanner imagery for automatic analysis, and an ellipsoidally symmetric functional form is assumed for the co-occurrence distribution of multiimage greytone N-tuple differences.