Neural-network-based motion stereo methods

Abstract

This paper presents neural network based lateral and longitudinal motion stereo methods. Lateral motion stereo infers depth information from a lateral motion. Existing lateral motion stereo algorithms use either a Kalman filter or recursive least square algorithm to update the disparity values. Due to the unmeasurable estimation error, the estimated disparity values at each recursion are unreliable, yielding a noisy disparity field. Instead of updating the disparity values, we recursively update the bias inputs of the network. The disparity field is then computed by using a neural network. Since the recursive algorithm implements the matching algorithm only once, and the bias input updating scheme can be accomplished in real time, a vision system employing such an algorithm is feasible. For the purpose of handling batch data, we have also designed a batch algorithm. The batch algorithm integrates information from all images by embedding them into the bias inputs of the network. Then a static matching procedure is used to compute the disparity values. Longitudinal motion stereo infers depth information from a forward or backward motion. Existing longitudinal stereo algorithms have some problems associated with the location of the focus of expansion (FOE), and with the camera and surface orientations. Instead, our approach allows the camera to move along its optical axis forward or backward, requires no information on the FOE, and makes no assumption about the object surface. The algorithm uses a Gabor correlation operator to extract image features and employs the neural network to compute the disparity field based on the Gabor features. It produces multiple dense disparity fields and recovers the depth map very efficiently.