Stereo under Sequential Optimal Sampling: A Statistical Analysis Framework for Search Space Reduction
Stereo under Sequential Optimal Sampling: A Statistical Analysis Framework for Search Space Reduction
复制标题
顺序最优采样下的立体：用于减少搜索空间的统计分析框架
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Jan
中科院分区：
文献类型：
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作者：
Yilin Wang;Ke Wang;Enrique Dunn;Jan
我们开发了一个序贯最佳采样框架立体视差估计通过调整序贯概率比测试（SPRT）模型。我们通过迭代估计单像素视差值来操作局部图像邻域，直到收集到足够的证据来验证或反驳当前关于局部场景结构的假设。我们的采样的输出是一组采样的像素位置沿着与一个强大的和紧凑的估计包含在一个给定的区域内的一组差异。我们进一步提出了一个有效的平面传播机制，利用预先计算的采样位置和局部结构模型所描述的减少局部视差集。我们的采样框架是一个通用的预处理机制，旨在降低计算复杂度的视差搜索算法，通过确定一个减少的视差假设为每个像素。实验表明，所提出的方法相比，最先进的方法的有效性。
We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model. We operate over local image neighborhoods by iteratively estimating single pixel disparity values until sufficient evidence has been gathered to either validate or contradict the current hypothesis regarding local scene structure. The output of our sampling is a set of sampled pixel positions along with a robust and compact estimate of the set of disparities contained within a given region. We further propose an efficient plane propagation mechanism that leverages the pre-computed sampling positions and the local structure model described by the reduced local disparity set. Our sampling framework is a general pre-processing mechanism aimed at reducing computational complexity of disparity search algorithms by ascertaining a reduced set of disparity hypotheses for each pixel. Experiments demonstrate the effectiveness of the proposed approach when compared to state of the art methods.