Using Wavelet Extraction for Haptic Texture Classification
Identifieur interne : 004A22 ( Istex/Curation ); précédent : 004A21; suivant : 004A23Using Wavelet Extraction for Haptic Texture Classification
Auteurs : Waskito Adi [Malaisie] ; Suziah Sulaiman [Malaisie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Abstract
Abstract: 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.
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DOI: 10.1007/978-3-642-05036-7_30
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Waskito Adi<affiliation><mods:affiliation>E-mail: waskito@gmail.com</mods:affiliation><wicri:noCountry code="no comma">E-mail: waskito@gmail.com</wicri:noCountry></affiliation>Le document en format XML
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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.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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