Results 121 to 130 of about 75,969 (161)

Affine-invariant texture classification

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2002
In content-based image retrieval, a texture pattern may appear in a wide range of 3D views. Affine transformation is an approximation frequently used in practice to represent the variation of a pattern. The existing approaches to texture classification cannot cope with this variation.
Dmitry Chetverikov, Zoltán Földvári
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Feature extraction for texture classification

Pattern Recognition, 1980
Abstract We address the problem of texture classification. Random walks are simulated for plane domains A bounded by absorbing boundaries Γ, and the absorption distributions are estimated. Measurements derived from the above distributions are the features used for texture classification.
Harry Wechsler, Todd K. Citron
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Independent filters for texture classification

Proceedings. International Conference on Image Processing, 2003
In this paper we propose a framework for texture classification through filtering. Given a set of textures, the filters are derived as the independent components of the input images and each texture is then characterized by the marginal distributions of its filter responses.
Xu Wen Liu, Lei Cheng
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Multiresolution eigenimages for texture classification

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004
Following an idea from B.M. ter Haar Romeny (see "Front-end vision and multiscale image analysis", Kluwer Academic Publishers, 2002), based on the Gaussian properties of eigenimages, the paper presents a new technique for texture classification using multiresolution eigenimages.
Mehrdad J. Gangeh, Michel Bister, Madasu Hanmandlu   +2 more
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Texture Feature Extraction and Classification

2001
This paper describes a novel technique for texture feature extraction and classification. The proposed feature extraction technique uses an Auto-Associative Neural Network (AANN) and the classification technique uses a Multi-Layer Perceptron (MLP) with a single hidden layer. The two approaches such as AANN-MLP and statistical-MLP were investigated. The
Brijesh K. Verma, Siddhivinayak Kulkarni
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A comprehensive approach for texture classification

10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), 2010
Classification of textures based on wavelet pattern analysis is one of the most effective methods in texture classification. However using all frequency sub-bands in decomposition for classification may increase time complexity of classification algorithms.
P. A. Reddy, P. S. R. C. Murty, Edara Srinivasa Reddy 0001   +2 more
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Efficient Classification of Seismic Textures

2018 International Joint Conference on Neural Networks (IJCNN), 2018
One of the most critical activities for the oil and gas industry is the discovery of new possibles reserves. Geoscientists must rely on indirect measures of the subsurface to scrutinize huge areas looking for leads of hydrocarbon reservoirs. Usually, to study the Earth’s crust, geoscientists examine seismic images.
Daniel Salles Chevitarese, Daniela Szwarcman, Emilio Vital Brazil, Bianca Zadrozny   +3 more
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Rotation-invariant texture classification

Pattern Recognition Letters, 2003
Summary: We propose a method for rotation-invariant 2D texture classification. Energy-normalized texture features are obtained by multiscale and multichannel decomposition using Gabor and Gaussian filters. Rotation invariance is achieved by the Fourier expansion of these features with respect to orientation. Unlike most previously reported methods, the
Franci Lahajnar, Stanislav Kovacic
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Classification of Textures Distorted by WaterWaves

18th International Conference on Pattern Recognition (ICPR'06), 2006
In this paper, we approach the novel problem of classifying images of underwater textures as observed from outside the water. Our main contribution is to combine a geometric distortion removal algorithm with a texture classification method to solve the problem of classifying images of submerged textures when the water is disturbed by waves.
Arturo Donate, Gary Dahme, Eraldo Ribeiro   +2 more
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Multifractal texture analysis and classification

Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 2003
Existing fractal methods of texture analysis rely on the fractal dimension of textures as a function of scale for their discrimination and classification. We propose a method which is based on the possible multiscaling/multifractality of textures. A stochastic model is suggested to represent this multiscaling behaviour.
Anh, V. V., Maeda, J., Tieng, Q. M., Tsui, H. T.   +3 more
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