Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.cviu.2009.08.005
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dc.titleInvariant texture classification for biomedical cell specimens via non-linear polar map filtering
dc.contributor.authorKumar, S.
dc.contributor.authorOng, S.H.
dc.contributor.authorRanganath, S.
dc.contributor.authorChew, F.T.
dc.date.accessioned2014-04-24T07:22:22Z
dc.date.available2014-04-24T07:22:22Z
dc.date.issued2010-01
dc.identifier.citationKumar, S., Ong, S.H., Ranganath, S., Chew, F.T. (2010-01). Invariant texture classification for biomedical cell specimens via non-linear polar map filtering. Computer Vision and Image Understanding 114 (1) : 44-53. ScholarBank@NUS Repository. https://doi.org/10.1016/j.cviu.2009.08.005
dc.identifier.issn10773142
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/50958
dc.description.abstractA novel texture-based classification scheme for cell specimens that is robust over a range of orientation, scale and contrast values is proposed. We achieve this robustness by first segmenting the cell specimens and for each specimen, we find the largest ellipse that can be contained within it, and from this, we then construct an orientation and scale-invariant polar map. Non-linear filtering by normalized cross-correlation is then performed on the polar map to obtain contrast-invariant similarity maps. Local and global energy measures are finally extracted from these maps and classified using a support vector machine. Experimental results show that the proposed method achieves an average accuracy of about 97% in classifying six species of pollen, fungal and fern spores. In addition, every invariant property was validated through a series of experiments. Unlike conventional wavelet decomposition, Laws filtering and co-occurrence methods, our method shows a consistently high classification accuracy for all classes of cell specimens in an airspora dataset. © 2009 Elsevier Inc. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.cviu.2009.08.005
dc.sourceScopus
dc.subjectBiomedical cells
dc.subjectContrast-invariant
dc.subjectPolar map
dc.subjectRotation invariant
dc.subjectScale-invariant
dc.subjectSupport vector machine
dc.subjectTexture classification
dc.typeArticle
dc.contributor.departmentBIOLOGICAL SCIENCES
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.cviu.2009.08.005
dc.description.sourcetitleComputer Vision and Image Understanding
dc.description.volume114
dc.description.issue1
dc.description.page44-53
dc.description.codenCVIUF
dc.identifier.isiut000272651600005
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