Wezska,J.S., Dyer, C. R., Rosenfeld, A. " A Comparative Study of Texture Measures for Terrain Classification " IEEE Trans On. SMC,vol Smc - 6no 4 pp 269 - 285 Home/Search Document Not in Database Summary Related Articles Check |
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A Semantic Event-Detection Approach and Its Application to .. - Haering, Qian, Sezan (2000) (14 citations) (Correct)
....vary smoothly in their local neighborhood Con The other GLCM based measures we use for our texture analysis are the Inverse Difference Moment, Mean, Entropy, Sum Entropy, Difference Entropy, Difference Variance, Correlation, Shade, and Prominence. These features are described in [3] 16] [30]. Note that the directionality of a texture can be measured by comparing the values obtained for a number of the above measures as is changed. The above measures were computed at and using . For further discussion of these GLCM measures (see [3] 16] 30] 3) Fractal Dimension (FD) Measures: ....
....features are described in [3] 16] 30] Note that the directionality of a texture can be measured by comparing the values obtained for a number of the above measures as is changed. The above measures were computed at and using . For further discussion of these GLCM measures (see [3] 16] [30]) 3) Fractal Dimension (FD) Measures: The underlying assumption for the use of the fractal dimension for texture classification and segmentation is that images or parts of images are self similar at some scale. Various methods that estimate the FD of an image have been suggested. ....
J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. Syst. Man Cybern. , vol. SMC-6, pp. 269--285, 1976.
Wavelet Image Extension for Analysis and.. - Mojsilovic.. (1997) (2 citations) (Correct)
.... [8] 12] and local linear transforms [13] 16] Recently, some modern methods have been developed, such as multichannel methods, multiresolution analysis, Gabor filters, and the wavelet transform [17] 27] Many of these approaches have provided good results in different fields of application [3], 4] 8] 9] but a large number of them have shown very low classification rate (or could not be implemented at all) when texture samples are of small dimensions [28] 29] Texture characterization of the myocardial tissue is not an easy task to perform, due to several reasons. First, it is ....
....about the primitive orientation and texture directionality. VI. COMPARISON WITH OTHER TRANSFORM BASED METHODS We have compared the wavelet image extension technique with other transform based and filtering based methods such as Laws texture energy measures [13] Fourier power spectrum measures [3], 9] and the pyramid structured wavelet transform [22] 26] For this comparison we have used following texture measures: L5, S5, R5, E5, and W5 masks for Laws texture energy measures [9] 13] ring and wedge statistics derived from the Fourier power spectrum [3] 9] calculated for the ....
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J. S. Weszka, C. R. Dryer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. Syst., Man, Cybern., vol. SMC-6, pp. 269--285, Apr. 1976.
Performance Evaluation of Image Segmentation and Texture.. - Sharma (1998) (3 citations) (Correct)
....error not being a function only of the features but also of the choice of the classifier. The figure of merit approach has the advantage that it does not depend on any classification scheme, and in addition, error is bounded by the figure of merit for some classif iers. Weszka et al.[223] compared texture measures for terrain classification. The features used for comparison include those categorised into four sets as Fourier power spectrum, second order grey level statistics, grey level difference statistics, and grey level run length statistics. The authors compute a total of 64 ....
....of all classes and the second study only the classification of the first three classes. The study used a total of 45 spectral features including mean, standard deviation, and features based on cumulative frequencies. The textural features computed are based on those proposed by Weszka et al.[223]. These features measure factors such as the amount of total variation within the cloud sample and the overall homogeneity of the sample data. A joint probability distribution of the difference in grey levels separated in direction q and distance r can be generated. On the basis of this, four ....
J.S. Weszka, C. R. Dyer and A. Rosenfeld, A comparative study of texture measures for terrain classification, IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 4, pp. 269-285, 1976.
Combining Belief Networks and Neural Networks for Scene.. - Feng, al. (2001) (5 citations) (Correct)
....of overall intensity in the region, the colour hue angle (sine and cosine) 1] R B) and (2G R B) 2 14 (as used by [34] where R, G and B indicate the means of the red, green and blue components respectively. The texture features are the grey level di erence vectors (GLDV) textural features [48, 47] of contrast, entropy, local homogeneity, angular second moment, mean, standard deviation, cluster shade and cluster prominence. The GLDV features were extracted based on the absolute di erence between pairs of gray levels at a distance d = 1 apart at four angles = 0 ; 45 ; 90 ; 135 ....
J. S. Weszka, C. R. Dyer, and A. Rosenfeld. A comparative study of texture measures for terrain classi cation. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(4):269-285, 1976.
Texture Analysis Experiments with Meastex and Vistex Benchmarks - Singh, Sharma (Correct)
....signal processing features [17] Van Gool et al. 18] and Reed and Buf [13] present a detailed survey of the various texture methods used in image analysis studies. Randen and Hus y [12] conclude that most studies deal with statistical, model based and signal processing techniques. Weszka et al. [20] compared the Fourier spectrum, second order gray level statistics, co occurrence statistics and gray level run length statistics and found the cooccurrence were the best. Similarly, Ohanian and Dubes [8] compare Markov Random Field parameters, multi channel filtering features, fractal based ....
J.S. Weszka, C. R. Dyer and A. Rosenfeld, A comparative study of texture measures for terrain classification, IEEE Transactions on Systems, Man and Cybernetics, 6:269-285, 1976.
Spatial Texture Analysis: A comparative Study - Singh, Singh (2002) (1 citation) (Correct)
....signal processing features [21] Van Gool et al. 22] and Reed and Buf [16] present a detailed survey of the various texture methods used in image analysis studies. Randen and Husy [15] conclude that most studies deal with statistical, model based and signal processing techniques. Weszka et al. [24] compared the Fourier spectrum, second order grey level statistics, co occurrence statistics and grey level run length statistics and found the co occurrence were the best. Similarly, Ohanian and Dubes [11] compare Markov Random Field parameters, multi channel filtering features, fractal based ....
J.S. Weszka, C. R. Dyer and A. Rosenfeld, A comparative study of texture measures for terrain classification, IEEE Transactions onSystems, Man and Cybernetics, 6:269-285, 1976. Fig. 1 (a) Samples of Meastex data including asphalt, concrete, grass and rock; (b) Samples of Vistex data including bark, fabric, food, metal, sand, tile and water
Evaluation of Texture Methods for Image Analysis - Sharma, Markou, Singh (1980) (2 citations) (Correct)
....signal processing features [17] Van Gool et al. 18] and Reed and Buf [13] present a detailed survey of the various texture methods used in image analysis studies. Randen and Husoy [12] conclude that most studies deal with statistical, model based and signal processing techniques. Weszka et al. [19] compared the Fourier spectrum, second order gray level statistics, co occurrence statistics and gray level run length statistics and found the co occurrence were the best. Similarly, Ohanian and Dubes [8] compare Markov Random Field parameters, multi channel filtering features, fractal based ....
J.S. Weszka, C. R. Dyer and A. Rosenfeld, "A comparative study of texture measures for terrain classification", IEEE Transactions on Systems, man and Cybernetics, vol. 6, pp. 269-285, 1976. Evaluation of texture measures _______________________ Pattern Recognition Letters
Image Retrieval: Current Techniques, Promising Directions.. - Rui, Huang, Chang (1999) (39 citations) (Correct)
....analysis. There also existed quite a few review papers in this area. An early review paper, by Weszka et al. compared the texture classi cation performance of Fourier power spectrum, second order gray level statistics (cooccurrence matrix) and rst order statistics of gray level di erences [164]. They tested the three methods on two sets of terrain samples and concluded that Fourier method performed poorly while the other two were comparable. In [99] Ohanian and Dubes compared and evaluated four types of texture representations, namely Markov Random Field representation [39] ....
Joan Weszka, Charles Dyer, and Azeril Roseneld. A comparative study of texture measures for terrain classication. IEEE Trans. on Sys, Man, and Cyb, SMC-6(4):269-285, 1976.
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Wezska,J.S., Dyer, C. R., Rosenfeld, A. " A Comparative Study of Texture Measures for Terrain Classification " IEEE Trans On. SMC,vol Smc - 6no 4 pp 269 - 285
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Wezska,J.S., Dyer, C. R., Rosenfeld, A. " A Comparative Study of Texture Measures for Terrain Classification " IEEE Trans On. SMC,vol Smc - 6no 4 pp 269 - 285
Case Study: - Steel Surface Classification (Correct)
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Wezska,J.S., Dyer, C. R., Rosenfeld, A. " A Comparative Study of Texture Measures for Terrain Classification " IEEE Trans On. SMC, vol Smc -- 6, no 4 pp 269 -- 285
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J.S. Weszka, C. R. Dyer and A. Rosenfeld, "A comparative study of texture measures for terrain classification", IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 4, pp. 269-285, 1976.
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J.S. Weszka, C. R. Dyer and A. Rosenfeld, "A comparative study of texture measures for terrain classification", IEEE Transactions on Systems, man and Cybernetics, vol. 6, pp. 269-285, 1976. 4
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J.S. Weszka, C. R. Dyer and A. Rosenfeld, "A comparative study of texture measures for terrain classification", IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 4, pp. 269-285, 1976.
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J.S. Weszka, C. R. Dyer and A. Rosenfeld, "A comparative study of texture measures for terrain classification", IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 4, pp. 269-285, 1976.
The Evaluation of Segmentation and Texture - Algorithm Combinations For (Correct)
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J.S. Weszka, C. R. Dyer and A. Rosenfeld, "A comparative study of texture measures for terrain classification", IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 4, pp. 269-285, 1976.
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J.S. Weszka, C. R. Dyer and A. Rosenfeld, "A comparative study of texture measures for terrain classification", IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 4, pp. 269-285, 1976.
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Weszka JS, Dyer CR, Rosenfeld A. A comparative study of texture measures for terrain classification. IEEE Tr. Sys. Man and Cybernetics 1976; 6: 269-285.
Wavelet-Based Texture Retrieval and Modeling Visual Texture.. - Shaohua (2000) (Correct)
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J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. on Systems, Man. Cybernet., Vol. 6, pp.269-285, 1976.
A Review of Content-Based Image Retrieval Systems.. - Müller, Michoux.. (Correct)
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J. S. Weszka, C. R. Dyer, A. Rosenfeld, A comparative study of texture measures for terrain classification, IEEE Transactions on Systems, Man and Cybernetics 6 (4) (1976) 269--285.
Texture Synthesis Using Gray-Level Co-Occurrence.. - Copeland.. (2001) (Correct)
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J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. Syst. Man Cybern. 6, 269--285 #1976#.
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J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. Syst. Man Cybern. 6, 269--285 #Apr. 1976#.
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Weszka JS, Dyer CR, Rosenfeld A. A comparative study of texture measures for terrain classification. IEEE Tr. Sys. Man and Cybernetics 1976; 6: 269-285.
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Weska J., Dyer C., and Rosenfeld Azriel. "A comparative study' of texture measures for terrain classification," IEEE Trans. Syst. Man and Cyb SMC-6 (1976), 269-285.
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J. S. Weszka, C. R. Dyer, and A. Rosenfeld. "A comparative study of texture measures for terrain classification". IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(4):pp.269--285, 1976.
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