Results 251 to 260 of about 578,151 (295)

A Method of the Extraction of Texture Feature

2008
In order to understand the emotional information of the color image, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the `semantic gap' between the visual features and the richness of human perception. In this paper, we firstly get the ROI using the Eye tracker and divide every image into
Haifang Li 0001, Lihuan Men, Junjie Chen 0001   +2 more
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Texture discrimination based on an optimal utilization of texture features

Pattern Recognition, 1988
Abstract We present a new approach to texture discrimination based on an algorithm which automatically selects the texture features best suited to a particular classification problem. Promising results are obtained when applying the method to the discrimination of 10 Brodatz's features.
Dong-Chen He, Li Wang 0002, Jean Guibert
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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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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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Feature-Based Texture Synthesis

2005
We introduce a new method for texture synthesis on regular and irregular example textures. In this paper, an enhanced patch-based algorithm is proposed to select patches with the best structural similarity and to avoid discontinuity at the boundary of adjacent patches.
Tong-Yee Lee, Chung-Ren Yan
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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 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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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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Feature-Based Textures.

2023
This paper introduces feature-based textures, a new image representation that combines features and texture samples for high-quality texture mapping. Features identify boundaries within a texture where samples change discontinuously. They can be extracted from vector graphics representations, or explicity added to raster images to improve sharpness ...
Ganesh Ramanarayanan, Kavita Bala, Bruce Walter   +2 more
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