Intuitive Interaktion durch videobasierte Gestenerkennung

Hinter der Forschung an videobasierter Handgestenerkennung steht die Vision, Interaktion zwischen Mensch und Computer losgelöst von klassischen Eingabegeräten wie Maus und Tastatur zu realisieren. Das Ziel dieser Arbeit ist die Entwicklung von echtzeitfähigen Verfahren, die eine robuste und fehlerarme Erkennung menschlicher Handgesten realisieren und so die Bedienung eines Computersystems auch für technisch unerfahrene Anwender nutzbar machen. In dieser Arbeit werden vier Verfahren entwickelt, die unterschiedliche Arten der Interaktion durch videobasierte Handgestenerkennung realisieren.The vision behind research on video based hand gesture recognition is to realise a new kind of interaction between humans and computer beyond the classical input devices such as mouse and keyboard. The aim of this thesis is to develop new video based realtime algorithms, which enable a robust and accurate recognition of human hand gestures and allow interaction with the computer even for technically unversed users. In this thesis four different algorithms are developed that can be used for intuitive interaction purposes depending on the demands and needs of different scenario applications

3D Hand Pose Reconstruction With ISOSOM

We present an appearance-based 3D hand posture estimation method that deter-mines a ranked set of possible hand posture candidates from an unmarked hand image, based on an analysis by synthesis method and an image retrieval algorithm. We formulate the posture estimation problem as a nonlinear, many-to-many map-ping problem in a high dimension space. A general algorithm called ISOSOM is proposed for nonlinear dimension reduction, applied to 3D hand pose reconstruc-tion to establish the mapping relationships between the hand poses and the image features. In order to interpolate the intermediate posture values given the sparse sampling of ground-truth training data, the geometric map structure of the samples’ manifold is generated. The experimental results show that the ISOSOM algorithm performs better than traditional image retrieval algorithms for hand pose estima-tion