Classification of partially occluded objects using 3-point matching and distance transformation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)27-40
Journal / PublicationPattern Recognition
Issue number1
Publication statusPublished - Jan 1994


Recognition of partially occluded objects has been recently made possible by describing an object boundary with a sequence of local feature segments localized with control or dominant points, and then matching the representations with those of a set of known reference shapes. When part of the object boundary is being occluded, contiguous intact feature segments will be used for identifying the object and distorted elements will be rejected in the matching process. The main problem associated with this method is that the nature of occlusion may separate the object contour into discrete regions, resulting in the degeneration of an individual representation into small isolated clusters reflecting only weak evidence on the object identity. In this paper, a new scheme capable of combining the isolated clusters for object classification is presented. In essence, Curvature Guided Polygonal Approximation is employed for detecting the dominant points of the boundaries. A 3-point matching technique is developed for extracting cluster(s) of dominant points on an unknown boundary which matched against reference objects, and to link up the scattered clusters to form a complete representation. The chamfer 3 4 Distance Transform, together with a quantitative measurement on the boundary covered by the matched dominant points is employed for calculating the similarity between the unknown and the reference contours. The scheme has been successfully applied for recognizing multiple overlapped handtools in different sizes and orientations. © 1994.

Research Area(s)

  • 3-point matching, Chamfer 3 4 Distance Transform, Curvature Guided Polygonal Approximation, Dominant points, Local feature, Occluded objects recognition