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dc.contributor.authorYuan, Quanen_US
dc.contributor.authorSclaroff, Stanen_US
dc.date.accessioned2011-10-20T04:56:38Z
dc.date.available2011-10-20T04:56:38Z
dc.date.issued2009-07-12
dc.identifier.citationYuan, Quan; Sclaroff, Stan. "Is a Detector Only Good for Detection?", Technical Report BUCS-TR-2009-023, Computer Science Department, Boston University, July 12, 2009. [Available from: http://hdl.handle.net/2144/1747]
dc.identifier.urihttps://hdl.handle.net/2144/1747
dc.description.abstractA common design of an object recognition system has two steps, a detection step followed by a foreground within-class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via one-vs-all (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this work, we formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the FRGC V2 data set, hand shape detection and parameter estimation on a hand data set and vehicle detection and view angle estimation on a multi-view vehicle data set. On all data sets, our approach has comparable accuracy and is at least five times faster than the brute force approach.en_US
dc.description.sponsorshipNational Science Foundation (ISS-0705749)en_US
dc.language.isoen_US
dc.publisherBoston University Computer Science Departmenten_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-2009-023
dc.titleIs a Detector Only Good for Detection?en_US
dc.typeTechnical Reporten_US


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