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A new region matching method for stereoscopic images

机译:一种新的立体图像区域匹配方法

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For region matching in stereoscopic images, most existing algorithms used the adjacency constraint (AC) between regions. However, in the case where there are occluded objects in the stereo images, this constrain may be violated. Therefore, there remain false matches and miss-correspondences in the final results. To overcome this problem, we propose here a new algorithm based on the relative position constraint (RPC) between regions. The relative positions between regions in one image are constrained to be the same as the relative positions between their corresponding ones in the other image. The matching process is achieved in two processing steps. The first one searches the candidate matches by using the epipolar constraint and the relative position constraint. The second step selects the correct matches among the possible one, according to a proposed cost function. The matching process is propagated by using the adjacency relationship between regions. Starting with matched couples, the adjacent regions are examined first and matched if possible. The process is gradually extended to all the regions on the image. The proposed new matching algorithm is illustrated by a set of synthesized and real stereo images, with a comparison to the well-known algorithm based on the adjacency constraint.
机译:对于立体图像中的区域匹配,大多数现有算法使用区域之间的邻接约束(AC)。然而,在立体图像中存在被遮挡的物体的情况下,该约束可能被违反。因此,最终结果中仍然存在错误的匹配和未对应的信息。为了克服这个问题,我们在此提出一种基于区域之间相对位置约束(RPC)的新算法。一幅图像中区域之间的相对位置被约束为与另一幅图像中它们对应区域之间的相对位置相同。匹配过程通过两个处理步骤实现。第一个通过使用对极约束和相对位置约束来搜索候选匹配项。第二步,根据建议的成本函数,在可能的匹配项中选择正确的匹配项。通过使用区域之间的邻接关系来传播匹配过程。从匹配对开始,首先检查相邻区域,并在可能的情况下进行匹配。该过程逐渐扩展到图像上的所有区域。通过一组合成的和真实的立体图像来说明所提出的新匹配算法,并与基于邻接约束的著名算法进行比较。

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