We present a cortical-like strategy to obtain reliable estimates of the motions of objects in a scene toward/away from the observer (motion in depth), from local measurements of binocular parameters derived from direct comparison of the results of monocular spatiotemporal filtering operations performed on stereo image pairs. This approach is suitable for a hardware implementation, in which such parameters can be gained via a feedforward computation (i.e., collection, comparison, and punctual operations) on the outputs of the nodes of recurrent VLSI lattice networks, performing local computations. These networks act as efficient computational structures for embedded analog filtering operations in smart vision sensors. Extensive simulations on both synthetic and real-world image sequences prove the validity of the approach that allows to gain high-level information about the 3D structure of the scene, directly from sensorial data, without resorting to explicit scene reconstruction.

A perceptual human-machine interface based on visual appearance of hand movements is presented. Gestures are defined as the temporal evolution of 3D poses the of user's hand. Each gesture is described by five temporal sequences corresponding to the time evolution of the stereo depth information at the finger-tips. To this end feature processing directly exploits binocular disparity information considered in its full spatio-temporal dimension. Hidden Markov models are used to represent the statistical properties of the gesture. The high recognition rate obtained by experimental testing on a five gesture alphabet validates the approach.