HEAL DSpace

Gesture-based video summarization

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dc.contributor.author Kosmopoulos, DI en
dc.contributor.author Doulamis, A en
dc.contributor.author Doulamis, N en
dc.date.accessioned 2014-03-01T02:43:21Z
dc.date.available 2014-03-01T02:43:21Z
dc.date.issued 2005 en
dc.identifier.issn 15224880 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/31348
dc.subject Local Minima en
dc.subject Sign Language en
dc.subject Skin Color en
dc.subject Video Summarization en
dc.subject Zernike Moment en
dc.subject.other Gesture energy variation en
dc.subject.other Sign language videos en
dc.subject.other Video summarization en
dc.subject.other Zernike moments en
dc.subject.other Color image processing en
dc.subject.other Feature extraction en
dc.subject.other Image segmentation en
dc.subject.other Video signal processing en
dc.subject.other Gesture recognition en
dc.title Gesture-based video summarization en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ICIP.2005.1530618 en
heal.identifier.secondary http://dx.doi.org/10.1109/ICIP.2005.1530618 en
heal.identifier.secondary 1530618 en
heal.publicationDate 2005 en
heal.abstract A novel method for summarizing videos of gestures is presented. The gestures performed by the hands and the head are extracted through skin color segmentation and represented through Zernike moments. The gesture energy is calculated using the norms of the Zernike moments and monitored through time for local minima and maxima that indicate distinctive visual events and thus key-frames. The proposed scheme is not threshold-dependent and therefore the number of extracted key-frames varies according to the complexity of gesture energy variation. The applicability of the method is verified experimentally in sign language videos. © 2005 IEEE. en
heal.journalName Proceedings - International Conference on Image Processing, ICIP en
dc.identifier.doi 10.1109/ICIP.2005.1530618 en
dc.identifier.volume 3 en
dc.identifier.spage 1220 en
dc.identifier.epage 1223 en


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