Abstract: A better basis for a further processing such as virtual view rendering, in form of a disparity map is achieved. To this end, the disparity map generation is done in two separate steps, namely the generation of two depth/disparity map estimates based on two different pairs of views of the scene in a manner independent from each other, with then comparing both depth/disparity map estimates so as to obtain a reliability measure for one or both of the depth/disparity map estimates.

Abstract: A better basis for a further processing such as virtual view rendering, in form of a disparity map is achieved. To this end, the disparity map generation is done in two separate steps, namely the generation of two depth/disparity map estimates based on two different pairs of views of the scene in a manner independent from each other, with then comparing both depth/disparity map estimates so as to obtain a reliability measure for one or both of the depth/disparity map estimates.

Abstract: During post-processing of video data in a YUV color space it may be necessary, for instance for immersive video conferences, to separate a video object in the image foreground from the known image background. Hitherto, rapid, locally limited deviations in illumination in the actual image to be examined, in particular shadows and brightenings, could not be compensated. The inventive recognition and compensation method, however, can compensate in real time shadows and brightenings, even at great quantities of image data by directly utilizing different properties of the technically based YUV color space. Chrominance, color saturation and color intensity of an actual pixel (P1) are approximated directly from associated YUV values (&agr;, a, b) which leads to the avoidance of time-consuming calculations.