  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.
