Interaction with a computer has been the center of innovation ever since the advent of input devices. From simple punch cards to keyboards, there are number of novel ways of interaction with computers which influence the user experience. Communicating using gestures is perhaps one of the most natural ways of interaction. Gesture recognition as a tool for interpreting signs constitutes a pivotal area in gesture recognition research where accuracy of the algorithm and the ease of usability determine the effectiveness of the algorithm or system. Introducing gesture based interaction in Virtual reality applications has not only helped solve problems which were commonly reported in traditional Virtual Reality systems, but also gives user a more natural and enriching experience. This paper concentrates on comparison of different systems and identifying their similarities, differences, advantages and demerits which can play a key role in designing a system using such technologies.

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This paper proposes an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. Active Contour Model provides a statistical model of object shape and appearance to a new image which are built during a training phase. In the graph cut technique, each pixel is represented as a node and the distance between those nodes is represented as edges. In graph theory, a cut is a partition of the nodes that divides the graph into two disjoint subsets. For initialization, a pseudo strategy is employed and the organs are segmented slice by slice through the OACAM (Oriented Active Contour Appearance Model). Initialization provides rough object localization and shape constraints which produce refined delineation. This method is tested with different set of images including CT and MR images (3D image) and produced perfect segmentation results.

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