Towards an Embedded Stereo Matching Algorithm Based on Multiple Correlation Windows

Stereo matching consists in extracting 3D information from digital images, such as those obtained by a CCD camera. It is an important issue under several real world applications, such as positioning systems for mobile robots, augmented reality systems, etc. In previous works one of the most popular trend to address the stereo matching challenge is that compares scene information from two viewpoints (left-right) with an eppipolar geometry via correlation metrics. In regard to the correlation metrics, most previous works compute the similarity between pixels in the left image and pixels in the right image using a correlation index computed on neighborhoods of these pixels called correlation windows. Unfortunately, in order to preserve edges, small correlation windows need to be used, while, for homogeneous areas, large correlation windows are required. To address this problem, we lay down on the hypothesis that small correlation windows combined with large correlation windows should deliver accurate results under homogeneous areas while at the same time edges are preserved. To validate our hypothesis, in this paper a similarity criterion based on the grayscale homogeneity of the correlation window being processed is presented. Preliminary results are encourageous, validates our hypothesis and demonstrated the viability performance and scope of the proposed approach.