摘要
This paper presents a new approach to the outdoor road scene understand-ing by using omni-view images and backpropagation networks. Both the road directions used for vehicle heading and the road categories used for velilcle local-ization are determined by the integrated system. There are three main features about the work. First, an omni-view image sensor is used to extract image samples, and the original image is preprocessed so that the inputs of the net-work is rotation-invariant and simple. Second, the problem of the network size,especially the number of the hidden units, is decided by the analysis of system-atic experimental results. Finally, the internal representation, which reveals the properties of the neural network, is analyzed in the view point of visual signal processing. Experimental results with real scene images are encouraging.
This paper presents a new approach to the outdoor road scene understand-ing by using omni-view images and backpropagation networks. Both the road directions used for vehicle heading and the road categories used for velilcle local-ization are determined by the integrated system. There are three main features about the work. First, an omni-view image sensor is used to extract image samples, and the original image is preprocessed so that the inputs of the net-work is rotation-invariant and simple. Second, the problem of the network size,especially the number of the hidden units, is decided by the analysis of system-atic experimental results. Finally, the internal representation, which reveals the properties of the neural network, is analyzed in the view point of visual signal processing. Experimental results with real scene images are encouraging.
