Results 221 to 230 of about 125,098 (262)

Decorrelation Methods of Texture Feature Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980
This paper presents the development and evaluation of a visual texture feature extraction method based on a stochastic field model of texture. Results of recent visual texture discrimination experiments are reviewed in order to establish necessary and sufficient conditions for texture features that are in agreement with human discrimination.
Olivier D. Faugeras, William K. Pratt
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Texture Classification from Random Features

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Inspired by theories of sparse representation and compressed sensing, this paper presents a simple, novel, yet very powerful approach for texture classification based on random projection, suitable for large texture database applications. At the feature extraction stage, a small set of random features is extracted from local image patches.
Li Liu 0002, Paul W. Fieguth
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Fractal feature and texture analysis

Systems and Computers in Japan, 1988
AbstractAlthough fractal dimension is popular in computer graphics, it is not yet utilized adequately in image analysis. Thus, this paper presents an example of the application of fractal dimension to image analysis. First, the conventional fractal concept is described and then the possibilities of its application to image processing are discussed ...
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Noise robustness of texture features

Image and Vision Computing, 1997
This note examines the noise robustness of two sets of texture features, one set derived from the popular multichannel filtering approach, and the other from the benchmark grey level co-occurrence matrix approach. Comparative experimental results are presented. The results clearly demonstrate the superiority of the multichannel approach.
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On the Reliability of Computing Wigner Texture Features

Journal of Mathematical Imaging and Vision, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Svetlana Barsky, Maria Petrou
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Texture Features and Image Texture Models

2019
Image texture is an important phenomenon in many applications of pattern recognition and computer vision. Hence, several models for deriving texture properties have been proposed and developed. Although there is no formal definition of image texture in the literature, image texture is usually considered the spatial arrangement of grayscale pixels in a ...
Chih-Cheng Hung, Enmin Song, Yihua Lan
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Scale Sensitivity of Textural Features

2017
Prevailing surface material recognition methods are based on textural features but most of these features are very sensitive to scale variations and the recognition accuracy significantly declines with scale incompatibility between visual material measurements used for learning and unknown materials to be recognized.
Michal Haindl, Pavel Vácha
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Relational Features for Texture Classification

2011
Texture features play an important role in facilitating various applications, for instance, image retrieval and object recognition. In this work, we investigate the relational features as a texture descriptor in classifying materials and visual textures from their appearance.
Wan Nural Jawahir Hj Wan Yussof, Hans Burkhardt   +1 more
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MORPHOLOGICAL TEXTURAL FEATURES

Particulate Science and Technology, 1989
ABSTRACT This paper describes the statistical and mathematical models for the gray level surface. Morphological Textural Features (MTFs) derived from the models are invariants. They include: • Bessel-Fourier Coefficients • Measurments of Gray Level Distributions • Rotational Symmetry • Translational Symmetry • Coarseness • Contrast • Roughness ...
N. B. HSYUNG, D. W. LUERKENS, J. K. BEDDOW   +2 more
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Combining Features for Texture Analysis

2015
In the present paper we consider building feature vectors for texture analysis by combining information provided by two techniques.The first feature extraction method the Discrete Wavelet Transform is applied to the entire image. By computing the Gini index for several subimages of a given texture, we choose one that maximizes this measure.
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