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首页> 外文会议>Mexican International Conference on Artificial Intelligence(MICAI 2007); 20071104-10; Aguascalientes(MX) >A Coarse-and-Fine Bayesian Belief Propagation for Correspondence Problems in Computer Vision
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A Coarse-and-Fine Bayesian Belief Propagation for Correspondence Problems in Computer Vision

机译：计算机视觉中对应问题的粗略精细贝叶斯信念传播

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摘要

We present the use of a multi-resolution, coarse-and-fine, pyramid image architecture to solve correspondence problems in various computer vision modules including shape recognition through contour matching, stereovision, and motion estimation. The algorithm works with a grid matching and an inter-grid correspondence model by message passing in a Bayesian belief propagation (BBP) network. The local smoothness and other constraints are expressed within each resolution scale grid and also between grids in a single paradigm. Top-down and bottom-up matching are concurrently performed for each pair of adjacent levels of the image pyramid level in order to find the best matched features at each level simultaneously. The coarse-and-fine algorithm uses matching results in each layer to constrain the process in its 2 adjacent upper and lower layers by measuring the consistency between corresponding points among adjacent layers so that good matches at different resolution scales constrain one another. The coarse-and-fine method helps avoid the local minimum problem by bringing features closer at the coarse level and yet providing a complete solution at the finer level. The method is used to constrain the solution with examples in shape retrieval, stereovision, and motion estimation to demonstrate its desirable properties such as rapid convergence, the ability to obtain near optimal solution while avoiding local minima, and immunity to error propagation found in the coarse-to-fine approach. ...
机译：我们提出使用多分辨率，粗略和精细的金字塔图像体系结构来解决各种计算机视觉模块中的对应问题，包括通过轮廓匹配，立体视觉和运动估计进行形状识别。该算法通过在贝叶斯信念传播（BBP）网络中传递消息来与网格匹配和网格间对应模型一起工作。局部平滑度和其他约束条件在每个分辨率比例网格内以及单个范例中的网格之间表达。对图像金字塔级别的每对相邻级别同时执行自顶向下和自底向上匹配，以便同时找到每个级别的最佳匹配特征。粗略算法通过测量相邻层之间对应点之间的一致性，在每一层中使用匹配结果，以限制其2个相邻上层和下层中的过程，从而使不同分辨率范围内的良好匹配相互约束。粗化和细化方法通过在较粗的层次上使要素更紧密，而在较细的层次上提供完整的解决方案，有助于避免局部最小问题。该方法用于通过形状检索，立体视觉和运动估计中的示例来约束解决方案，以证明其理想的属性，例如快速收敛，能够在避免局部最小值的情况下获得接近最佳解的能力以及对粗略中发现的错误传播的抵抗力细化方法。 ...

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