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This paper describes a stereo-based approach for gesture recognition that works well under extreme lighting conditions and tolerates a large range of hand poses. The approach proposes a hybrid gesture representation that models the user's arm as a 3D line and uses images to represent the hand gestures. The algorithm finds the arm orientation and the hand location from the disparity data and uses this information to initialize a color based segmentation algorithm that cleanly separates the hand from the background. Finally, our approach uses the arm orientation to compute a frontal view of the hand through perspective unwarping producing easily recognizable hand gesture templates. The classification algorithm uses statistical moments of the binarized gesture templates to find the match. We achieve 96% recognition rates under varying lighting and 3D poses for a set of 6 gestures.

Semi-peripheral collisions of ^{64}Ni + ^{124}Sn in direct and inverse kinematics, at 35 A MeV incident energy, are studied to characterize the dynamical origin and align-ments properties of fragments emission at midrapidity. This emission is partly due to the fragmentation of a transient neck joining the projectile and target in the early stages of the reaction. Recently we exploited observable from neck fragmentation to constraint the density dependence of the symmetry energy. In this contribution we focus on the density evolution of the mid-rapidity source. An experimental survey is suggested to check if data are consistent with the formation of neck fragments in a dilute density region in contact with the projectile-like and target-like spectators at normal saturation density.