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- W2016124490 abstract "Generic shape feature extraction is a challenging task for image and video content analysis. We present a non-parametric statistics based method for extracting generic shape tokens based on a Perceptual Curve Partition and Grouping (PCPG) model. In this PCPG model, each curve is made up of Generic Edge Tokens (GET) connected at Curve Partitioning Points (CPP). The types of GET and CPP provide a set of basic shape descriptors for semantic vocabulary. The new implementation of the PCPG is based on: 1) An arctangent space is employed to signify the evidence of CPPs at pixel-level. 2) The pixels' sequential order is taken as heuristic to establish a bin order preserving arctangent histogram for locating CPPs by examining the continuity of generic feature criteria statistically. 3) A new CPP detection scheme is capable of detecting CPPs and classifying GETs on the fly. Experiments are presented for performance demonstration." @default.
- W2016124490 created "2016-06-24" @default.
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- W2016124490 date "2010-09-01" @default.
- W2016124490 modified "2023-10-18" @default.
- W2016124490 title "A non-parametric statistics based method for generic curve partition and classification" @default.
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- W2016124490 doi "https://doi.org/10.1109/icip.2010.5654096" @default.
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