Probabilistic Shape Parsing and Action Recognition Through Binary Spatio-Temporal Feature Description

FieldValue
dc.contributor.authorWhiten, Christopher J.
dc.date.accessioned2013-04-09T14:55:35Z
dc.date.available2013-04-09T14:55:35Z
dc.date.created2013
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10393/24006
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-2912
dc.description.abstractIn this thesis, contributions are presented in the areas of shape parsing for view-based object recognition and spatio-temporal feature description for action recognition. A probabilistic model for parsing shapes into several distinguishable parts for accurate shape recognition is presented. This approach is based on robust geometric features that permit high recognition accuracy. As the second contribution in this thesis, a binary spatio-temporal feature descriptor is presented. Recent work shows that binary spatial feature descriptors are effective for increasing the efficiency of object recognition, while retaining comparable performance to state of the art descriptors. An extension of these approaches to action recognition is presented, facilitating huge gains in efficiency due to the computational advantage of computing a bag-of-words representation with the Hamming distance. A scene's motion and appearance is encoded with a short binary string. Exploiting the binary makeup of this descriptor greatly increases the efficiency while retaining competitive recognition performance.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectbinary
dc.subjectfeature description
dc.subjectspatiotemporal feature
dc.subjectshape parsing
dc.subjectaction recognition
dc.subjectobject recognition
dc.titleProbabilistic Shape Parsing and Action Recognition Through Binary Spatio-Temporal Feature Description
dc.typeThesis
dc.faculty.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
dc.contributor.supervisorLaganiere, Robert
dc.embargo.termsimmediate
dc.degree.nameMCS
dc.degree.levelmasters
dc.degree.disciplineGénie / Engineering
thesis.degree.nameMCS
thesis.degree.levelMasters
thesis.degree.disciplineGénie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

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