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Identifier	000339025
Title	Probabilistic gesture recognition
Alternative Title	Πιθανοκρατική Αναγνώριση Χειρονομιών
Author	Σιγάλας, Μάρκος Μαρίνος
Abstract	Communication with the use of gestures is a very crucial and common form of interaction in human societies. Gestures not only allow us to interact with other people and objects, but, in some cases, substitute every other form of communication –deaf people for example. On the other hand, computers have become an inseparable part of our society, influencing many aspects of our daily lives in terms of communication and interaction. Evolution in the field of informatics has seen tremendously high speeds, mostly in the last few decades, enabling new forms of /Human-Computer Interaction/ (HCI) which fully exploit the dynamics of hand gestures. In the current thesis, a probabilistic approach towards Hand Gesture Recognition is proposed. Based on the assumption that various common gestures can be modeled without the need of high-level information, the proposed approach achieves to reduce the complexity of the problem by decreasing the space dimensionality of the parameters, which describe the configuration of the arm. The methodology for tracking the mentioned parameters, manages to extract a robust representation of the arm's pose and to end up with an efficient spatio-temporal gesture model. Initially, skin-colored blobs are being detected on the images. Since, usually, the highest detected skin-colored blob is the head, the height of the actor is easily calculated, which leads to an estimation of the size of the limbs, with the aid of simple anthropometric proportions. Once this is done, inverse kinematics equations serve for the extraction of an initial estimation of the arm's parameters, which are then tracked over time with the use of particle filters. The usage of particle filters implies that multiple hypotheses are being tracked simultaneously, enabling the recovery from cases where erroneous estimations occur. In order to assure time invariance and to prevent discontinuities, the extracted parameters are being filtered according to their relevancy to previous outputs, resulting with smooth parameter sequences, which are, therefore, used in order to model each hand gesture. The final, gesture recognition, step consists of a set of neural networks, each of them responsible for the recognition of a single gesture. The usage of multiple neural networks –instead of using a global one- ensures the elimination of possible ambiguities due to overlapping gesture paths. Since there is no prior knowledge regarding the possible gesture being performed, the parameter sequences are being fed to all neural networks simultaneously. Appropriate supervised training of the networks, ensures that only one network at each time will produce high output, resulting in the successful recognition of the performed gesture.
Physical description	xiii, 87 σ. : εικ. ; 30 cm.
Language	English
Issue date	2009-04-02
Collection	School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
	Type of Work--Post-graduate theses
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