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
Optical and Sonar Image Classification
Xiaoou Tang,W. Kenneth Stewart +1 more
TL;DR: In this article, a multilevel dominant eigenvector estimation algorithm and statistical distance measures were used to combine and select frequency channel features of greater discriminatory power for noise-insensitive texture classification for both optical and underwater sidescan sonar images.
Proceedings ArticleDOI
Recognition methods for 3D textured surfaces
Oana G. Cula,Kristin J. Dana +1 more
TL;DR: In this article, a hybrid approach that employs both feature grouping and dimensionality reduction was proposed for 3D textured surface recognition, which was tested using the Columbia-Utrecht texture database and provided excellent recognition rates.
Journal ArticleDOI
Optical–digital processing of directional terrain textures invariant under translation, rotation, and change of scale
TL;DR: An oblique basis of signatures matched to the three classes of textures is proposed, which is invariant under translation, rotation, and change of scale, and is compared with either the direct signatures or the two proposed bases.
Journal ArticleDOI
Surface Roughness Detection of Arteries via Texture Analysis of Ultrasound Images for Early Diagnosis of Atherosclerosis
Lili Niu,Ming Qian,Wei Yang,Long Meng,Yang Xiao,Kelvin K. L. Wong,Derek Abbott,Xin Liu,Hairong Zheng +8 more
TL;DR: The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall and this method is shown to be useful for early detection and diagnosis of atherosclerosis.
Journal ArticleDOI
Texture Characterization and Texture-Based Image Partitioning Using Two-Dimensional Linear Estimation Techniques
TL;DR: A new approach to texture characterization and texture-based image partitioning is presented, in which the gray level of each pixel of an image is estimated from a weighted sum of gray levels of its neighbor pixels.
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.