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Conference Paper: A hand shape recognizer from simple sketches

TitleA hand shape recognizer from simple sketches
Authors
KeywordsHand shape
Sketch
Generative model
Issue Date2013
PublisherIEEE.
Citation
The 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013), Wellington, New Zealand, 27-29 November 2013. In Conference Proceedings, 2013, p. 130-135 How to Cite?
AbstractHand shape recognition is one of the most important techniques used in human-computer interaction. However, it often takes developers great efforts to customize their hand shape recognizers. In this paper, we present a novel method that enables a hand shape recognizer to be built automatically from simple sketches, such as a 'stick-figure' of a hand shape. We introduce the Hand Boltzmann Machine (HBM), a generative model built upon unsupervised learning, to represent the hand shape space of a binary image, and formulate the user provided sketches as an initial guidance for sampling to generate realistic hand shape samples. Such samples are then used to train a hand shape recognizer. We evaluate our method and compare it with other state-of-the-art models in three aspects, namely i) its capability of handling different sketch input, ii) its classification accuracy, and iii) its ability to handle occlusions. Experimental results demonstrate the great potential of our method in real world applications. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/199307
ISBN

 

DC FieldValueLanguage
dc.contributor.authorZhu, Xen_US
dc.contributor.authorSang, Ren_US
dc.contributor.authorJia, Xen_US
dc.contributor.authorWong, KKYen_US
dc.date.accessioned2014-07-22T01:13:04Z-
dc.date.available2014-07-22T01:13:04Z-
dc.date.issued2013en_US
dc.identifier.citationThe 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013), Wellington, New Zealand, 27-29 November 2013. In Conference Proceedings, 2013, p. 130-135en_US
dc.identifier.isbn978-1-4799-0883-7-
dc.identifier.urihttp://hdl.handle.net/10722/199307-
dc.description.abstractHand shape recognition is one of the most important techniques used in human-computer interaction. However, it often takes developers great efforts to customize their hand shape recognizers. In this paper, we present a novel method that enables a hand shape recognizer to be built automatically from simple sketches, such as a 'stick-figure' of a hand shape. We introduce the Hand Boltzmann Machine (HBM), a generative model built upon unsupervised learning, to represent the hand shape space of a binary image, and formulate the user provided sketches as an initial guidance for sampling to generate realistic hand shape samples. Such samples are then used to train a hand shape recognizer. We evaluate our method and compare it with other state-of-the-art models in three aspects, namely i) its capability of handling different sketch input, ii) its classification accuracy, and iii) its ability to handle occlusions. Experimental results demonstrate the great potential of our method in real world applications. © 2013 IEEE.en_US
dc.languageengen_US
dc.publisherIEEE.en_US
dc.relation.ispartofInternational Conference on Image and Vision Computing New Zealand (IVCNZ) Proceedingsen_US
dc.rightsInternational Conference on Image and Vision Computing New Zealand (IVCNZ) Proceedings. Copyright © IEEE.en_US
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_US
dc.subjectHand shapeen_US
dc.subjectSketchen_US
dc.subjectGenerative modelen_US
dc.titleA hand shape recognizer from simple sketchesen_US
dc.typeConference_Paperen_US
dc.identifier.emailSang, R: rxsang@hku.hken_US
dc.identifier.emailWong, KKY: kykwong@cs.hku.hken_US
dc.identifier.authorityWong, KKY=rp01393en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/IVCNZ.2013.6727004en_US
dc.identifier.scopuseid_2-s2.0-84894354281-
dc.identifier.hkuros230413en_US
dc.identifier.spage130en_US
dc.identifier.epage135en_US
dc.publisher.placeUnited Statesen_US
dc.customcontrol.immutablesml 140730-

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