Please use this identifier to cite or link to this item: https://doi.org/10.1016/0031-3203(95)00007-M
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dc.titleA scale-space filtering approach for visual feature extraction
dc.contributor.authorXin, K.
dc.contributor.authorLim, K.B.
dc.contributor.authorHong, G.S.
dc.date.accessioned2014-06-17T05:07:49Z
dc.date.available2014-06-17T05:07:49Z
dc.date.issued1995
dc.identifier.citationXin, K., Lim, K.B., Hong, G.S. (1995). A scale-space filtering approach for visual feature extraction. Pattern Recognition 28 (8) : 1145-1158. ScholarBank@NUS Repository. https://doi.org/10.1016/0031-3203(95)00007-M
dc.identifier.issn00313203
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/57830
dc.description.abstractThis paper presents a new integrated approach for detecting visual features which include CORNERs, ENDs, ARCs and LINEs. The effect of scale-space filtering on visual features is studied in detail as it forms the theoretical basis of our work. In this approach, the outline of the object is first extracted and it is then smoothed by scale-space filtering at different scale levels. Subsequently, the Local Extreme Curvature Points extracted from the smoothed curve and END candidates are determined to guide the termination of the filtering process. Information about the curvature of each point at the largest scale level is used to detect the different kinds of visual features. Several algorithms are proposed to determine CORNERS, ENDs, ARCs and LINEs. Experimental results show that our approach is robust to translation, rotation and scaling of the object as well as noise corruption. In addition, efficient visual features can also be successfully extracted with this approach.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/0031-3203(95)00007-M
dc.sourceScopus
dc.subjectCurvature
dc.subjectGaussian smoothing
dc.subjectLocal Extreme Curvature Point
dc.subjectScale level
dc.subjectScale-space filtering
dc.subjectVisual feature extraction
dc.typeArticle
dc.contributor.departmentMECHANICAL & PRODUCTION ENGINEERING
dc.description.doi10.1016/0031-3203(95)00007-M
dc.description.sourcetitlePattern Recognition
dc.description.volume28
dc.description.issue8
dc.description.page1145-1158
dc.description.codenPTNRA
dc.identifier.isiutA1995RN78700004
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