Results 151 to 160 of about 374,369 (195)

Texture classification using logical operators

IEEE Transactions on Image Processing, 2000
In this paper, a new algorithm for texture classification based on logical operators is presented. Operators constructed from logical building blocks are convolved with texture images. An optimal set of six operators are selected based on their texture discrimination ability. The responses are then converted to standard deviation matrices computed over
Manian, Vidya, Vásquez, Ramón, Katiyar, Praveen   +2 more
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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, Paul W, Fieguth
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Texture classification using spectral histograms

IEEE Transactions on Image Processing, 2003
Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram ...
Xiuwen, Liu, DeLiang, Wang
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Texture classification using fuzzy uncertainty texture spectrum

Neurocomputing, 1998
Abstract A new method using fuzzy uncertainty, which measures the uncertainty of the uniform surface in an image, is proposed for texture analysis. A grey-scale image can be transformed into a fuzzy image by the uncertainty definition. The distribution of the membership in a measured fuzzy image, denoted by the fuzzy uncertainty texture spectrum ...
Yih-Gong Lee, Jia-Hong Lee, Yuang-Cheh Hsueh   +2 more
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Texture classification using color local texture features

2013 International Conference on Signal Processing , Image Processing & Pattern Recognition, 2013
This Paper proposes a new approach to extract the features of a color texture image for the purpose of texture classification. Four feature sets are involved. Dominant Neighbourhood Structure (DNS) is the new feature set that has been used for color texture image classification.
S. Arivazhagan, R. Benitta
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Combined Geometric-Texture Image Classification

2005
In this paper, we propose a framework to carry out supervised classification of images containing both textured and non textured areas. Our approach is based on active contours. Using a decomposition algorithm inspired by the recent work of Y. Meyer, we can get two channels from the original image to classify: one containing the geometrical information,
Aujol, Jean-François, Chan, Tony
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Modified Textural Soil Classification

2020
In this world, different types of soils are present. These soils are consisting of different types of soil particles. The engineering properties of soil are defined on the basis of particles size and consistency limits. Soil was classified into different divisions and subdivisions by different organizations.
Jitendra Khatti, N. P. Kaushik, J. K. Sharma, K. S. Grover   +3 more
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Hyperspectral soil texture classification

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003, 2004
A soil texture classification system is developed and exploited in the hyperspectral domain. The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures.
null Xudong Zhang, V. Vijayaraj, N.H. Younan   +2 more
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Measuring texture classification algorithms

Pattern Recognition Letters, 1997
Summary: The texture analysis literature lacks a widely accepted method for comparing algorithms. This paper proposes a framework for comparing texture classification algorithms. The framework consists of several suites of texture classification problems, a standard functionality for algorithms, and a method for computing a score for each algorithm. We
Smith G., Burns I.
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Bayesian Texture Classification

2003
Pictures of natural scenes usually are composed of several types of textures. Texture classification is the art of identifying them and marking them by labels. Texture classification has many important applications, for example in quality control or remote sensing.
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