Spatial Topology Graphs for Feature-Minimal Correspondence

Multiview image matching methods typically require feature point correspondences. We propose a novel spatial topology method that represents the space with a set of connected projective invariant features. Typically, isolated features, such as corners, cannot be matched reliably. Hence, limitations are imposed on viewpoint changes, or projective invariant descriptions are needed. The fundamental matrix is discovered using stochastic optimization requiring a large number of features. In contrast, our enhanced feature set models connectivity in space, forming a unique configuration that can be matched with few features and over large viewpoint changes. Our features are derived from edges, their curvatures, and neighborhood relationships. A probabilistic spatial topology graph models the space using these features and a second graph represents the neighborhood relationships. Probabilistic graph matching is used to find feature correspondences. Our results show robust feature detection and an average 80% discovery rate of feature matches.