Results 251 to 260 of about 1,823,626 (291)

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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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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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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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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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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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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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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Construction of texture features

2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, 2009
One well-known and effective method used for computationally efficient texture classification is the use of statistical information on 3×3 pixel blocks such as local binary patterns (LBP). However, there has been negligible research on sizes of pixel blocks beyond 3×3 while using the histogram approach. Specifically, larger or non-square features might
A. Oerlemans, null Qi Zhang, M.S. Lew
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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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Texture Defect Detection Using Invariant Textural Features

2001
In this paper we propose a novel method for the construction of invariant textural features for grey scale images. The textural features are based on an averaging over the 2D Euclidean transformation group with relational kernels. They are invariant against 2D Euclidean motion and strictly increasing grey scale transformations.
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