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    Hopfield网络在视差空间上的立体匹配求解

    Solving the Stereo Matching in Disparity Image Space Using the Hopfield Neural Network

    • 摘要: 由于边界区域的匹配精度是立体匹配问题的瓶颈,这里采用一种基于特征的匹配算法来重点研究场景中边界区域的匹配.首先针对立体匹配问题,提出一种基于RBF的边界提取算法,使得边界区域成为待匹配的像素点.研究像素点匹配需要满足的约束,构建相应的能量方程,接着采用Hopfield网络对能量函数进行优化来获得问题的求解.由于针对的是整个边界区域,直接将特征点输入网络会导致神经元数目过多、复杂度过高.为了降低算法复杂度,提出从视差空间上来构造网络模型.最后通过大量实验来验证算法的性能,包括标准图片、噪声图片与真实的场景图片.实验证明新算法能大大提高边界区域精度,克服了立体匹配的瓶颈,明显提高了整体区域精度,算法有很强的鲁棒性和实用性,即使在复杂情况下也能取得较好的效果.

       

      Abstract: As the accuracy of boundary region is the bottleneck of stereo matching, here a feature-based matching algorithm is used to focus on the matching problem in the boundary. Firstly, for the stereo matching, we propose a new boundary feature extraction algorithm based on the RBF, and make the boundary as the region to be matched. After considering various constraints the matching needs to meet, we build a new energy function, and then use the Hopfield neural network (HNN) to do the optimization. Because the object area is all of the boundary, so it is not wise to input too many neurons to the network. To further reduce the complexity of the algorithm, it is proposed to construct a network model from the disparity image space, which is an innovative approach. Finally, a large number of experiments are conducted to validate the algorithm, including the standard images, the noise images and the real scene images. Experimental results show that the new algorithm can greatly improve the accuracy of the boundary so as to overcome the bottleneck. At the same time, the accuracy of the whole region will be also significantly improved. When tested in complex cases, the algorithm can still get a good result, so it can be said that the robustness and performance of the algorithm are quite good.

       

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