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dc.contributor.supervisor Peters, James (Electrical and Computer Engineering) en_US
dc.contributor.author Hettiarachchi, Randima
dc.date.accessioned 2017-01-04T15:31:56Z
dc.date.available 2017-01-04T15:31:56Z
dc.date.issued 2015-09 en_US
dc.date.issued 2016-11 en_US
dc.date.issued 2016-12 en_US
dc.identifier.citation R. Hettiarachchi and J. Peters, Multi-Manifold LLE Learning in Pattern Recognition., Pattern Recognition, Elsevier, vol. 48, no.9, pp. 2947– 2960, 2015 en_US
dc.identifier.citation R. Hettiarachchi and J. Peters, Multi-Manifold-Based Skin Classifier on Feature Space Voronoi Regions for Skin Segmentation., Journal of Visual Communication and Image Representation, Elsevier, vol. 41, pp. 123-139, 2016 en_US
dc.identifier.citation R. Hettiarachchi and J. Peters, Voronoi Region-Based Adaptive Unsupervised Color Image Segmentation., Pattern Recognition, Elsevier, In Press, Accepted on 12 December 2016 en_US
dc.identifier.uri http://hdl.handle.net/1993/31969
dc.description.abstract A computer vision system consists of many stages, depending on its application. Feature extraction and segmentation are two key stages of a typical computer vision system and hence developments in feature extraction and segmentation are significant in improving the overall performance of a computer vision system. There are many inherent problems associated with feature extraction and segmentation processes of a computer vision system. In this thesis, I propose novel solutions to some of these problems in feature extraction and segmentation. First, I explore manifold learning, which is a non-linear dimensionality reduction technique for feature extraction in high dimensional data. The classical manifold learning techniques perform dimensionality reduction assuming that original data lie on a single low dimensional manifold. However, in reality, data sets often consist of data belonging to multiple classes, which lie on their own manifolds. Thus, I propose a multi-manifold learning technique to simultaneously learn multiple manifolds present in a data set, which cannot be achieved through classical single manifold learning techniques. Secondly, in image segmentation, when the number of segments of the image is not known, automatically determining the number of segments becomes a challenging problem. In this thesis, I propose an adaptive unsupervised image segmentation technique based on spatial and feature space Dirichlet tessellation as a solution to this problem. Skin segmentation is an important as well as a challenging problem in computer vision applications. Thus, thirdly, I propose a novel skin segmentation technique by combining the multi-manifold learning-based feature extraction and Vorono\"{i} region-based image segmentation. Finally, I explore hand gesture recognition, which is a prevalent topic in intelligent human computer interaction and demonstrate that the proposed improvements in the feature extraction and segmentation stages improve the overall recognition rates of the proposed hand gesture recognition framework. I use the proposed skin segmentation technique to segment the hand, the object of interest in hand gesture recognition and manifold learning for feature extraction to automatically extract the salient features. Furthermore, in this thesis, I show that different instances of the same dynamic hand gesture have similar underlying manifolds, which allows manifold-matching based hand gesture recognition. en_US
dc.publisher Elsevier en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject Computer Vision en_US
dc.subject Multi-Manifold learning en_US
dc.subject Image segmentation en_US
dc.subject Voronoi region-based segmentation en_US
dc.subject Hand gesture recognition en_US
dc.title Multi-Manifold learning and Voronoi region-based segmentation with an application in hand gesture recognition en_US
dc.type info:eu-repo/semantics/doctoralThesis
dc.type doctoral thesis en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee McLeod, Robert (Electrical and Computer Engineering) Yahampath, Pradeepa (Electrical and Computer Engineering) Thomas, Robert (Mathematics) Reformat, Marek (Electrical and Computer Engineering, University of Alberta) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note February 2017 en_US


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