Hierarchical disparity estimation with energy-based regularization
Hierarchical disparity estimation with energy-based regularization
复制标题
基于能量正则化的分层视差估计
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
K. Sohn
中科院分区：
文献类型：
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作者：
Hansung Kim;K. Sohn
本文提出了一种基于能量正则化的分层视差估计算法。采用基于特征的区域分割视差估计技术从下采样立体图像中获得初始视差矢量。在全分辨率图像中，利用这些具有形状自适应窗口的初始向量估计密集差。最后，利用最小化能量泛函对矢量场进行正则化，同时考虑了矢量场的保真度和平滑度。前两步提供了高度可靠的视差向量，从而避免了正则化步骤中的局部极小问题。该算法生成精确的视差图，在保持物体边界不连续性的同时，在物体内部保持平滑。实验结果说明了所提出的视差估计技术的能力。
In this paper, we propose a hierarchical disparity estimation algorithm with energy-based regularization. Initial disparity vectors are obtained from downsampled stereo images using a feature-based region-dividing disparity estimation technique. Dense disparities are estimated from these initial vectors with shape-adaptive windows in full resolution images. Finally, the vector fields are regularized with the minimization of the energy functional which considers both fidelity and smoothness of the fields. The first two steps provide highly reliable disparity vectors, so that local minimum problem can be avoided in regularization step. The proposed algorithm generates accurate disparity map which is smooth inside objects while preserving its discontinuities in boundaries. Experimental results are presented to illustrate the capabilities of the proposed disparity estimation technique.