• Corpus ID: 16840195

Wavelet-based Texture Analysis

@inproceedings{Scheunders1998WaveletbasedTA,
  title={Wavelet-based Texture Analysis},
  author={Paul Scheunders and Stefan Livens and Gert Van de Wouwer and Philippe Vautrot and Dirk Van Dyck},
  year={1998}
}
In this paper, texture analysis based on wavelet transformations is elaborated. The paper is meant as a practical guideline through some aspects of a wavelet-based texture analysis task. The following aspects of the problem are discussed: discrete and continuous wavelet decompositions, texture features for grey-level textures, extensions to colour texture and rotation-invariant features, and classi cation, including supervised image classi cation and unsupervised texture segmentation tasks. For… 
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References

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TLDR
Two feature extraction algorithms based on pyramidal and tree structured wavelet transforms are introduced and their performance is compared with the feature extraction which employs adaptive Gabor filtering.
A comparison of wavelet transform features for texture image annotation
TLDR
Issues discussed include image processing complexity, texture classification and discrimination, and suitability for developing indexing techniques.
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  • Mathematics, Computer Science
    IEEE Trans. Image Process.
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This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet decomposition, which yields
Color Texture Classification by Wavelet Energy Correlation Signatures
TLDR
This paper introduces wavelet energy-correlation signatures and the transformation of these signatures upon linear color space transformations is derived and it is demonstrated that the wavelet correlation features contain more information than the intensity or the energy features of each color plane separately.
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TLDR
A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed and is compared with that of several other methods.
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| This report gives an introduction to the application of wavelet based multiscale image analysis methods to texture analysis. It outlines the basic methods and comments on design issues. It also
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TLDR
A texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system is presented, which is based on reconstruction of the input image from the filtered images.
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TLDR
A novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2-D envelope detection and two algorithms for envelope detection based on the Hilbert transform and zero crossings are introduced.
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TLDR
A survey of current texture segmentation and feature extraction methods, with emphasis on techniques developed since 1980, particularly those with promise for unsupervised applications.
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TLDR
This paper describes the comparison between different spatial/spatial-frequency methods involving Gabor filters and wavelets, and two applications are considered: image segmentation and texture classification/recognition.
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