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
Breast cancer Ki67 expression prediction by DCE-MRI radiomics features.
TL;DR: The present study showed that quantitative radiomics imaging features of breast tumour extracted from DCE-MRI are associated with breast cancer Ki67 expression.
Journal ArticleDOI
Computerized identification of pollen grains by texture analysis
TL;DR: In this paper, a co-occurrence matrix of grey levels was established for each sample, and texture measures were calculated and used as input to a classification program, with a leave-one-out strategy and a variable selection procedure.
Journal ArticleDOI
Filtering methods for texture discrimination
Chien-Chang Chen,Chaur-Chin Chen +1 more
TL;DR: Experimental results on both natural textures and synthesized Markov random field textures indicate that the wavelet features achieve almost the same recognition rate with the Gabor features, which is higher than the other two methods, whereas the computation time shows that theWavelet features are preferred.
Proceedings ArticleDOI
Gray Level Co-Occurrence Matrix Computation Based On Haar Wavelet
Musa Mohd Mokji,S. A. Abu Bakar +1 more
TL;DR: The proposed computation is tested with the classification performance of the Brodatz texture images and gives a slightly better performance compare to the original GLCM computation.
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.