Electrical Engineering Department, University of Texas at Arlington, Arlington, TX 76010, USA.
IEEE Trans Image Process. 2010 Jun;19(6):1393-402. doi: 10.1109/TIP.2010.2042647.
An efficient and robust framework is proposed for two-view multiple structure-and-motion segmentation of unknown number of rigid objects. The segmentation problem has three unknowns, namely the object memberships, the corresponding fundamental matrices, and the number of objects. To handle this otherwise recursive problem, hypotheses for fundamental matrices are generated through local sampling. Once the hypotheses are available, a combinatorial selection problem is formulated to optimize a model selection cost which takes into account the hypotheses likelihoods and the model complexity. An explicit model for outliers is also added for robust segmentation. The model selection cost is minimized through the branch-and-bound technique of combinatorial optimization. The proposed branch-and-bound approach efficiently searches the solution space and guarantees optimality over the current set of hypotheses. The efficiency and the guarantee of optimality of the method is due to its ability to reject solutions without explicitly evaluating them. The proposed approach was validated with synthetic data, and segmentation results are presented for real images.
提出了一种用于两视图多结构和运动分割的有效和鲁棒框架，用于分割未知数量的刚体。分割问题有三个未知数，即物体成员、对应的基础矩阵和物体数量。为了解决这个递归问题，通过局部采样生成基础矩阵的假设。一旦有了假设，就可以制定一个组合选择问题，以优化一个模型选择成本，该成本考虑了假设的可能性和模型的复杂性。还添加了一个显式的异常值模型，以进行鲁棒分割。通过组合优化的分支定界技术最小化模型选择成本。所提出的分支定界方法有效地搜索解空间，并保证在当前假设集上的最优性。该方法的效率和最优性保证是由于其能够拒绝解决方案而无需显式评估它们。该方法已通过合成数据进行了验证，并为真实图像提供了分割结果。
