Results 271 to 280 of about 93,638 (313)

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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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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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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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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Textural Features for Image Classification

IEEE Transactions on Systems, Man, and Cybernetics, 1973
Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category ...
Robert M. Haralick, Karthikeyan S. Shanmugam, Its'hak Dinstein   +2 more
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Vector quantization for texture classification

IEEE Transactions on Systems, Man, and Cybernetics, 1993
A method for classifying and coding textures that is based upon transform vector quantization is presented. Techniques for texture classification and vector quantization similarly process small, nonoverlapping blocks of image data. Local spatial frequency features have been identified as being appropriate for texture classification, indicating that a ...
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Cost-sensitive texture classification

2014 IEEE Congress on Evolutionary Computation (CEC), 2014
Texture recognition plays an important role in many computer vision tasks including segmentation, scene understanding and interpretation, medical imaging and object recognition. In some situations, the correct identification of particular textures is more important compared to others, for example recognition of enemy uniforms for automatic defense ...
Gerald Schaefer, Bartosz Krawczyk, Niraj P. Doshi, Tomoharu Nakashima   +3 more
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