A Hypergraph-based Approach to Affine Parameters Estimation

Abstract

A problem commonly encountered in Computer Vision is the recovery of the transformation parameters between two affinely distorted images. In this paper, we propose a novel feature-based approach that casts the matching problem to the search of a maximum clique over an auxiliary hypergraph. We also introduce a continuous-based characterization of cliques in hypergraphs that allows us to handle the hard combinatorial problem using tools from the continuous domain. Finally, we present experimental result and comparisons with a state-of-the-art algorithm.

Ihara Coefficients: A Flexible Tool for Higher Order Learning

“…Specific applications of hypergraph related methods in visual processing include the algorithms described in [3] [6]. One common feature of these methods is that they exploit domain specific and goal directed representations, and do not lend themselves to generalization.…”