Finding an optimal correspondence between point sets is a common task in computer vision. Existing techniques assume relatively simple relationships among points and do not guarantee an optimal match. We introduce an algorithm capable of exactly solving point set matching by modeling the task as hypergraph matching. The algorithm extends the classical branch and bound paradigm to select and aggregate vertices under a proposed decomposition of the multilinear objective function. The methodology is motivated by Caenorhabditis elegans, a model organism used frequently in developmental biology and neurobiology. The embryonic C. elegans contains seam cells that can act as fiducial markers allowing the identification of other nuclei during embryo development. The proposed algorithm identifies seam cells more accurately than established point-set matching methods, while providing a framework to approach other similarly complex point set matching tasks.
翻译:在计算机视野中,找到点形之间的最佳对应是共同的任务。 现有技术假定各点之间的关系相对简单,不能保证最佳匹配。 我们引入了一种算法,能够通过将任务建模为高光比对来精确地解决点集。 算法扩展了古典分支和约束范式,以便在多线性目标功能的拟议分解下选择和综合脊椎。 方法的动机是骨质细胞,这是发展生物学和神经生物学中常用的模型生物体。 胚胎C. 肝素含有可以作为纤维标记的接缝细胞,可以在胚胎发育期间识别其他核。 提议的算法比既定的点定比匹配方法更精确地识别接缝细胞,同时提供一个框架,用以处理其他类似复杂点的匹配任务。