Dynamic image segmentation and optic flow extraction
Hilary Tunley · 2002
A preattentive recurrent neural network model which, given an image sequence as input, simultaneously achieves segmentation, occlusion-finding, and optic flow mapping is discussed. The importance of heterarchically integrated processing is stressed, resulting in simultaneous output. One of the novel aspects of this model is that it detects moving features solely as a consequence of determining optic flow. Another novelty is its detection of occlusion. It is argued that occlusion detection is important for any visual motion system, for two main reasons. First, it supplies structural information on the relative depths of moving objects. Second, it provides additional information on the presence of stationary objects and surfaces, without the need for separate static image processing. These novel features of the model result in significant computational savings.>