A recognition network model-based approach to dynamic image understanding

In this paper, we present definitions for a dynamic knowledge-based image understanding system. From a sequence of grey level images, the system produces a flow of image interpretations. We use a semantic network to represent the knowledge embodied in the system. Dynamic representation is achieved by ahypotheses network. This network is a graph in which nodes represent information and arcs relations. A control strategy performs a continuous update of this network. The originality of our work lies in the control strategy: it includes astructure tracking phase, using the representation structure obtained from previous images to reduce the computational complexity of understanding processes. We demonstrate that in our case the computational complexity, which is exponential if we only use a purely data-driven bottom-up scheme, is polynomial when using the hypotheses tracking mechanism. This is to say that gain improvement in computation time is a major reason for dynamic understanding. The proposed system is implemented; experimental results of road mark detection and tracking are given.