Mr. Zheng Xu Profile

Zheng Xu

at Univ of Electro-Communications

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Proceedings Article | 6 December 2005 Paper

Mask scale adjusting stereo matching method for an object with curved surface

Zheng Xu, Masanori Idesawa

Proceedings Volume 6051, 60510O (2005) https://doi.org/10.1117/12.648871

KEYWORDS: Cameras, Image processing, Computer simulations, Machine vision, Computer vision technology, 3D image processing, Image segmentation, Signal to noise ratio, Visual system, 3D vision

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Stereo matching process is needed in order to obtain 3D information from stereo pair images. Many sophisticated methods have been proposed and used for obtaining the correspondence in stereo pair. However, they are all directing to obtaining global matching in a whole scene. In the recent studies on the human visual system, volume perception with binocular viewing is reported: in which object can be perceived as if it extending to sufficiently farther depth than the depth expected from the visible parts of an object; then found that the binocularly unpaired areas on an object playing an indispensable role for the volume perception. In order to simulate the volume perception on computer vision, more precise matching process in the adjacent areas of unpaired regions on an object were required. However, because of some inherent problems, even in the ideal condition without any noise, conventional stereo matching methods can not be applied for obtaining detailed information especially in the areas adjacent to the contour of an object with curved surface. The authors investigated in details on the stereo matching in the adjacent areas of the object contour with curved surface; then found that different shrinking ratios between two stereo images were the essential factor causing the difficulty in stereo matching. In order to solve this problem, the authors devise a mask scale adjusting stereo matching method and improve the matching accuracy especially in the adjacent areas of binocularly unpaired parts. By applying the proposed method more precise 3D information of an object can be expected; then the volume perception could be simulated in computer vision.

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