Results 241 to 250 of about 371,752 (272)

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
Lahajnar, Franci, Kovačič, Stanislav
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Texture classification using texture spectrum

Pattern Recognition, 1990
Abstract Pursuing our previous study where the Texture Spectrum method has been proposed for texture analysis, the purpose of this paper is to demonstrate the usefulness of the Texture Spectrum for texture classification. Promising results are obtained when applying the Texture Spectrum to classify four of Brodatz's natural images.
Li Wang, Dong-Chen He
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Compact color–texture description for texture classification

Pattern Recognition Letters, 2015
We show that combining multiple texture description methods significantly improves the performance compared to using the single best texture method alone.We further propose to use information theoretic compression approach to compress high-dimensional multi-texture features into a compact heterogeneous texture representation.We perform a comprehensive ...
Khan, Fahad Shahbaz, Anwer, Rao Muhammad, van de Weijer, Joost, Felsberg, Michael, Laaksonen, Jorma   +5 more
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On texture classification

International Journal of Systems Science, 1997
Texture analysis has found wide application in, say, remote sensing, medical diagnosis, and quality control.
Chen, Y.Q., Nixon, M.S., Thomas, D.W.
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Texture Classification Using Refined Histogram

IEEE Transactions on Image Processing, 2010
In this correspondence, we propose a novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and present a new image signature based on the RH model for supervised texture classification. Our RH makes use of a step function with exponentially increasing intervals to model the histogram of detail ...
L, Li, C S, Tong, S K, Choy
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Texture Classification Using Ridgelet Transform

Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2006
Texture classification has long been an important research topic in image processing. Now a day's classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities.
S. Arivazhagan, L. Ganesan, T.G. Subash Kumar   +2 more
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Texture classification using wavelet transform

Pattern Recognition Letters, 2003
Summary: Today, texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image data bases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively.
Arivazhagan, S., Ganesan, L.
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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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