Growing Aggregation Algorithm for Dense Two-Frame Stereo Correspondence

This work aims at defining a new method for matching correspondences in stereoscopic image analysis. The salient aspects of the method are-an explicit representation of occlusions driving the overall matching process and the use of neural adaptive technique in disparity computation. In particular, based on the taxonomy proposed by Scharstein and Szelinsky, the dense stereo matching process has been divided into three tasks: matching cost computation, aggregation of local evidence and computation of disparity values. Within the second phase a new strategy has been introduced in an attempt to improve reliability in computing disparity. An experiment was conducted to evaluate the solutions proposed. The experiment is based on an analysis of test images including data with a ground truth disparity map.