Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-35725-1_29
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dc.titleA novel and robust system for time recognition of the digital video clock using the domain knowledge
dc.contributor.authorYu, X.
dc.contributor.authorRong, T.
dc.contributor.authorLi, N.
dc.contributor.authorLeong, H.W.
dc.date.accessioned2014-07-04T03:10:56Z
dc.date.available2014-07-04T03:10:56Z
dc.date.issued2013
dc.identifier.citationYu, X.,Rong, T.,Li, N.,Leong, H.W. (2013). A novel and robust system for time recognition of the digital video clock using the domain knowledge. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7732 LNCS (PART 1) : 318-326. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-35725-1_29" target="_blank">https://doi.org/10.1007/978-3-642-35725-1_29</a>
dc.identifier.isbn9783642357244
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/77969
dc.description.abstractThis paper presents a novel and robust system for recognizing the time of the digital video clock by using the domain knowledge. This system comprises a set of the functions for the time recognition so that the user can conveniently use these functions to recognize the time of digital video clocks or to execute some steps of recognizing time. These functions are novel and robust because they use the novel methods derived from the domain knowledge of the digital video clock. These methods are region second periodicity, global maximum model, digit location model, digit-sequence recognition, and on-thefly SVM. Experimental results show that both the functions and the system can achieve very high recognition accuracy. © Springer-Verlag 2013.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-35725-1_29
dc.sourceScopus
dc.subjectDigital Video Clock
dc.subjectMixture Gaussian
dc.subjectOn-the-fly SVM
dc.subjectSecond Periodicity
dc.subjectTime Recognition
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-35725-1_29
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7732 LNCS
dc.description.issuePART 1
dc.description.page318-326
dc.identifier.isiutNOT_IN_WOS
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