A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
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Texture Classification Using Windowed Fourier Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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HAPTICS '02 Proceedings of the 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Cumulative global distance for dimension reduction in handwritten digits database
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Generalized Daubechies Wavelet Families
IEEE Transactions on Signal Processing
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While visual texture classification is a widely-research topic in image analysis, little is known on its counterpart i.e. the haptic (touch) texture. This paper examines the visual texture classification in order to investigate how well it could be used for haptic texture search engine. In classifying the visual textures, feature extraction for a given image involving wavelet decomposition is used to obtain the transformation coefficients. Feature vectors are formed using energy signature from each wavelet sub-band coefficient. We conducted an experiment to investigate the extent in which wavelet decomposition could be used in haptic texture search engine. The experimental result, based on different testing data, shows that feature extraction using wavelet decomposition achieve accuracy rate more than 96%. This demonstrates that wavelet decomposition and energy signature is effective in extracting information from a visual texture. Based on this finding, we discuss on the suitability of wavelet decomposition for haptic texture searching, in terms of extracting information from image and haptic information.