An Exact Hypergraph Matching Algorithm for Nuclear Identification in Embryonic Caenorhabditis elegans

by   Andrew Lauziere, et al.

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.


page 10

page 11


An Efficient Multilinear Optimization Framework for Hypergraph Matching

Hypergraph matching has recently become a popular approach for solving c...

Learning Hypergraph Labeling for Feature Matching

This study poses the feature correspondence problem as a hypergraph node...

Activity recognition from videos with parallel hypergraph matching on GPUs

In this paper, we propose a method for activity recognition from videos ...

A Concave Optimization Algorithm for Matching Partially Overlapping Point Sets

Point matching refers to the process of finding spatial transformation a...

Hypergraph Representation via Axis-Aligned Point-Subspace Cover

We propose a new representation of k-partite, k-uniform hypergraphs (i.e...

Code Repositories

Please sign up or login with your details

Forgot password? Click here to reset