多结构稳健拟合的确定性假设生成
Deterministic Hypothesis Generation for Robust Fitting of Multiple Structures
摘要：
我们提出了一种新的算法，用于生成多结构模型拟合的鲁棒性和一致性假设。现有的方法大多采用随机抽样，特别是当离群值比率较高时，会产生不同的结果。对于拟合模型的结构，当存在多个结构时，其他结构的内点被视为离群点。最近已经研究了全局优化以提供稳定和唯一的解决方案，但是对于具有合理大小的大多数图像数据，算法的计算成本过高。本文提出的算法使用最大可行子系统（MaxFS）算法，仅从空间重叠的局部图像区域中的部分数据集生成一致的初始假设。我们的假设是，每一个真正的结构将作为主导结构存在于至少一个局部区域中。为了改进从部分数据集估计的初始假设并去除MaxFS算法的残余公差依赖性，对所有图像数据执行迭代重新加权L1（IRL1）最小化。IRL 1框架的初始权重由局部区域产生的初始假设确定。我们的方法比那些只对所有图像数据进行全局优化的方法效率要高得多。实验结果表明，与随机抽样方法相比，该方法能更好地估计单个和多个结构，并能更好地处理大量离群数据。在实验中，我们清楚地揭示了算法参数设置对结果的影响。
英文摘要：
We present a novel algorithm for generating robust and consistent hypotheses for multiple-structure model fitting. Most of the existing methods utilize random sampling which produce varying results especially when outlier ratio is high. For a structure where a model is fitted, the inliers of other structures are regarded as outliers when multiple structures are present. Global optimization has recently been investigated to provide stable and unique solutions, but the computational cost of the algorithms is prohibitively high for most image data with reasonable sizes. The algorithm presented in this paper uses a maximum feasible subsystem (MaxFS) algorithm to generate consistent initial hypotheses only from partial datasets in spatially overlapping local image regions. Our assumption is that each genuine structure will exist as a dominant structure in at least one of the local regions. To refine initial hypotheses estimated from partial datasets and to remove residual tolerance dependency of the MaxFS algorithm, iterative re-weighted L1 (IRL1) minimization is performed for all the image data. Initial weights of IRL1 framework are determined from the initial hypotheses generated in local regions. Our approach is significantly more efficient than those that use only global optimization for all the image data. Experimental results demonstrate that the presented method can generate more reliable and consistent hypotheses than random-sampling methods for estimating single and multiple structures from data with a large amount of outliers. We clearly expose the influence of algorithm parameter settings on the results in our experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
