A Cepstrum-based technique for disparity detection in a stereo vision system

Recovery of depth information for 3-D modeling of surfaces and objects is a central problem in low-level computer vision. A number of applications related to reconstruction, representation, and recognition of scenes and objects in the three-dimensional geometry of the real world rely on the correct estimation of range data. In stereo vision, depth information is obtained from triangulation of corresponding points in a pair of stereo images. The automatic determination of local similarity between two data sets or collections of pixels is fundamental in a depth-algorithm from stereo algorithm for numerical evaluation of disparities. In this paper, the use of the Cepstrum transformation as a disparity measurement technique between corresponding windows of different block sizes is described. This measurement process is embedded within a coarse to fine stereopsis algorithm, providing an initial depth map with the depth information encoded as gray levels. In real stereo imagery the features to be used as information in the matching procedure are usually sparsely and irregularly distributed over the images. For that reason, obtention of a dense array of depth data is a difficult task in passive methods. This paper makes emphasis in surface recovery of smooth curved surfaces with application to depth estimation of medical images. Examples and experimental results using random dot stereograms and real imagery are presented