Compact Shape Trees: A Contribution to the Forest of Shape Correspondences and Matching Methods

TL;DR

This paper introduces compact shape trees, a new method for efficiently computing shape correspondences and matching in 2-D, with invariance to scale and rotation, supported by theoretical analysis and shape recognition experiments.

Contribution

The paper presents a novel compact shape tree representation that reduces computational complexity and achieves scale and rotation invariance for shape matching.

Findings

Efficient O(n^2) shape correspondence computation.

Compact shape trees have equal discriminatory power as traditional methods.

Successful shape recognition experiments demonstrate effectiveness.

Abstract

We propose a novel technique, termed compact shape trees, for computing correspondences of single-boundary 2-D shapes in O(n2) time. Together with zero or more features defined at each of n sample points on the shape's boundary, the compact shape tree of a shape comprises the O(n) collection of vectors emanating from any of the sample points on the shape's boundary to the rest of the sample points on the boundary. As it turns out, compact shape trees have a number of elegant properties both in the spatial and frequency domains. In particular, via a simple vector-algebraic argument, we show that the O(n) collection of vectors in a compact shape tree possesses at least the same discriminatory power as the O(n2) collection of lines emanating from each sample point to every other sample point on a shape's boundary. In addition, we describe neat approaches for achieving scale and rotation invariance with compact shape trees in the spatial domain; by viewing compact shape trees as aperiodic discrete signals, we also prove scale and rotation invariance properties for them in the Fourier domain. Towards these, along the way, using concepts from differential geometry and the Calculus, we propose a novel theory for sampling 2-D shape boundaries in a scale and rotation invariant manner. Finally, we propose a number of shape recognition experiments to test the efficacy of our concept.