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In the literature, stereo matching is used for building pixel correspondences for stereo image pairs. Such correspondences can serve as fundamentals for applications such as 3D scene reconstruction. In some applications, however, stereo vision is adopted for object localization so that only object correspondences are required. However, existing pixel based stereo matching approaches are computationally inefficient for these applications. In this paper, we address the problem of object correspondence construction in stereo camera systems by using a fast and accurate algorithm adopting reverse stereo triangulation. This algorithm is based on a belief that any incorrect object pair will demonstrate inconsistency in its spatial location calculated from reverse stereo triangulation, so that correct object pairs can be identified accurately from all possible object pairs. We present experimental results from a dual camera human face capturing system in which more than 99% genuine object correspondences can be accurately identified, while 100% of falsely detected objects are eliminated. Besides, our proposed method can handle no less than 100 object pairs within 1 ms in a P4 1.5 GHz desktop PC.
Date of Conference: 8-9 Jan. 2008