Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11263-009-0220-6
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dc.titleViewpoint invariant texture description using fractal analysis
dc.contributor.authorXu, Y.
dc.contributor.authorJi, H.
dc.contributor.authorFermüller, C.
dc.date.accessioned2014-10-28T02:49:34Z
dc.date.available2014-10-28T02:49:34Z
dc.date.issued2009-06
dc.identifier.citationXu, Y., Ji, H., Fermüller, C. (2009-06). Viewpoint invariant texture description using fractal analysis. International Journal of Computer Vision 83 (1) : 85-100. ScholarBank@NUS Repository. https://doi.org/10.1007/s11263-009-0220-6
dc.identifier.issn09205691
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104453
dc.description.abstractImage texture provides a rich visual description of the surfaces in the scene. Many texture signatures based on various statistical descriptions and various local measurements have been developed. Existing signatures, in general, are not invariant to 3D geometric transformations, which is a serious limitation for many applications. In this paper we introduce a new texture signature, called the multifractal spectrum (MFS). The MFS is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes. It provides an efficient framework combining global spatial invariance and local robust measurements. Intuitively, the MFS could be viewed as a "better histogram" with greater robustness to various environmental changes and the advantage of capturing some geometrical distribution information encoded in the texture. Experiments demonstrate that the MFS codes the essential structure of textures with very low dimension, and thus represents an useful tool for texture classification. © 2009 Springer Science+Business Media, LLC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s11263-009-0220-6
dc.sourceScopus
dc.subjectFractal dimension
dc.subjectMulti-fractal spectrum
dc.subjectTexture classification
dc.subjectTexture description
dc.subjectViewpoint invariance
dc.typeArticle
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1007/s11263-009-0220-6
dc.description.sourcetitleInternational Journal of Computer Vision
dc.description.volume83
dc.description.issue1
dc.description.page85-100
dc.description.codenIJCVE
dc.identifier.isiut000263790600006
Appears in Collections:Staff Publications

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