Browsing by Author "Aran, Oya."

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    Incremental neural network construction algorithms for training multilayer perceptrons
    (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2002., 2002.) Aran, Oya.; Alpaydın, Ethem.
    The problem of determining the architecture of a multilayer perceptron together with the disadvantages of the standard backpropagation algorithm, directed the research towards algorithms that determine not only the weights but also the structure of the net~vork necessary for learning the data. In this work we propose two algorithms: the Constructive Algorithm using Statistical Tests (CAST), and Constructive Algorithm with Multiple Operators using Statistical Tests (MOST). The first one constructs a single hidden layer network by adding hidden nodes one by one. The algorithm checks the difference between the errors of the current and candidate networks and decides whether to select the candidate network or not by using a statistical test for comparing the accuracies of the two networks. The networks that are constructed by MOST can have more than one hidden layer. The algorithm uses node removal, addition and layer addition and determines the number of nodes in layers by heuristics. To our krowledge, MOST is the only algorithm that constructs a multilayer perceptron with multiple hidden layers with multiple units per layer. The results of the algorithms are promising and near optimal.
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    Vision based sign language recognition: modeling and recognizing isolated signs with manual and non-manual components
    (Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2008., 2008.) Aran, Oya.; Akarun, Lale.
    This thesis addresses the problem of vision based sign language recognition and focuses on three main tasks to design improved techniques that increase the perfor- mance of sign language recognition systems. We first attack the markerless tracking problem during natural and unrestricted signing in less restricted environments. We propose a joint particle filter approach for tracking multiple identical objects, in our case the two hands and the face, which is robust to situations including fast move- ment, interactions and occlusions. Our experiments show that the proposed approach has a robust tracking performance during the challenging situations and is suitable for tracking long durations of signing with its ability of fast recovery. Second, we at- tack the problem of the recognition of signs that include both manual (hand gestures) and non-manual (head/body gestures) components. We investigated multi-modal fu- sion techniques to model the different temporal characteristics and propose a two-step sequential belief based fusion strategy. The evaluation of the proposed approach, in comparison to other state of the art fusion approaches, shows that our method models the two modalities better and achieves higher classification rates. Finally, we pro- pose a strategy to combine generative and discriminative models to increase the sign classification accuracy. We apply the Fisher kernel method and propose a multi-class classification strategy for gesture and sign sequences. The results of the experiments show that the classification power of discriminative models and the modeling power of generative models are effectively combined with a suitable multi-class strategy. We also present two applications, a sign language tutor and an automatic sign dictionary, developed based on the ideas and methods presented in this thesis.