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dc.contributor.advisorJiang, Jianmin
dc.contributor.authorJia, Jia*
dc.date.accessioned2010-03-15T16:31:05Z
dc.date.available2010-03-15T16:31:05Z
dc.date.issued2010-03-15T16:31:05Z
dc.identifier.urihttp://hdl.handle.net/10454/4259
dc.description.abstractWith the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. As a sub-field, Interactive Image Processing emphasizes particularly on the communications between machine and human. The basic flowchart is definition of object, analysis and training phase, recognition and feedback. Generally speaking, the core issue is how we define the interesting object and track them more accurately in order to complete the interaction process successfully. This thesis proposes a novel dynamic simulation scheme for interactive image processing. The work consists of two main parts: Hand Motion Detection and Hand Gesture recognition. Within a hand motion detection processing, movement of hand will be identified and extracted. In a specific detection period, the current image is compared with the previous image in order to generate the difference between them. If the generated difference exceeds predefined threshold alarm, a typical hand motion movement is detected. Furthermore, in some particular situations, changes of hand gesture are also desired to be detected and classified. This task requires features extraction and feature comparison among each type of gestures. The essentials of hand gesture are including some low level features such as color, shape etc. Another important feature is orientation histogram. Each type of hand gestures has its particular representation in the domain of orientation histogram. Because Gaussian Mixture Model has great advantages to represent the object with essential feature elements and the Expectation-Maximization is the efficient procedure to compute the maximum likelihood between testing images and predefined standard sample of each different gesture, the comparability between testing image and samples of each type of gestures will be estimated by Expectation-Maximization algorithm in Gaussian Mixture Model. The performance of this approach in experiments shows the proposed method works well and accurately.en
dc.language.isoenen
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.en
dc.subjectInteractive imagingen
dc.subjecthand gesture recognitionen
dc.subjectFeature extractionen
dc.subjectSegmentationen
dc.subjectMotion detectionen
dc.subjectGaussian Mixture modelen
dc.subjectExpectation Maximization algorithmen
dc.titleInteractive Imaging via Hand Gesture Recognition.en
dc.type.qualificationlevelresearch mastersen
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentDepartment of Electronic Imaging and Media Communicationsen
dc.typeThesiseng
dc.type.qualificationnameMPhilen
dc.date.awarded2009
refterms.dateFOA2018-07-18T23:16:42Z


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