论文标题
基于立体视觉的稀疏差异估计的差异计算的混合算法
A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo vision
论文作者
论文摘要
在本文中，我们通过结合现有基于块和基于区域的立体声匹配的现有方法来提出一种新的立体声差异估计方法。我们的方法可以从仅18％像素的左图或立体声图像对的右图像中产生密集的差异图。它通过使用K-均值聚类的快速实现来分割图像像素的轻度值来工作。然后，它通过形态滤波和连接的组件分析来完善这些段边界，从而消除了许多冗余边界像素。接下来是通过悲伤的成本函数确定边界的差异。最后，我们通过考虑相邻区域的差异，从边界的差异从边界的差异中重建整个场景差异图。 Middlebury立体声视觉数据集的实验结果表明，与最新方法相比，与基于绝对差异（AD）成本差异的最新方法相比，所提出的方法优于SAD和NCC（NCC）等传统差异确定方法的提高2.6％[1]。
In this paper, we have proposed a novel method for stereo disparity estimation by combining the existing methods of block based and region based stereo matching. Our method can generate dense disparity maps from disparity measurements of only 18% pixels of either the left or the right image of a stereo image pair. It works by segmenting the lightness values of image pixels using a fast implementation of K-Means clustering. It then refines those segment boundaries by morphological filtering and connected components analysis, thus removing a lot of redundant boundary pixels. This is followed by determining the boundaries' disparities by the SAD cost function. Lastly, we reconstruct the entire disparity map of the scene from the boundaries' disparities through disparity propagation along the scan lines and disparity prediction of regions of uncertainty by considering disparities of the neighboring regions. Experimental results on the Middlebury stereo vision dataset demonstrate that the proposed method outperforms traditional disparity determination methods like SAD and NCC by up to 30% and achieves an improvement of 2.6% when compared to a recent approach based on absolute difference (AD) cost function for disparity calculations [1].
