FFT-based Alignment of 2d Closed Curves with Application to Elastic Shape Analysis

TL;DR

This paper introduces a fast O(N log N) FFT-based algorithm for aligning closed curves in the plane, significantly improving speed and accuracy in shape analysis tasks in computer vision and scientific imaging.

Contribution

The paper presents a novel FFT-based method for rigid alignment of closed curves, reducing computational complexity from O(N^2) to O(N log N).

Findings

Achieved an order of magnitude speed-up in curve alignment.

Provided accurate shape distance computations at lower computational cost.

Demonstrated effectiveness in elastic shape analysis applications.

Abstract

For many shape analysis problems in computer vision and scientific imaging (e.g., computational anatomy, morphological cytometry), the ability to align two closed curves in the plane is crucial. In this paper, we concentrate on rigidly aligning pairs of closed curves in the plane. If the curves have the same length and are centered at the origin, the critical steps to an optimal rigid alignment are finding the best rotation for one curve to match the other and redefining the starting point of the rotated curve so that the starting points of the two curves match. Unlike open curves, closed curves do not have fixed starting points, and this introduces an additional degree of freedom in the alignment. Hence the common naive method to find the best rotation and starting point for optimal rigid alignment has O(N^2) time complexity, N the number of nodes per curve. This can be slow for curves with large numbers of nodes. In this paper, we propose a new O(N log N) algorithm for this problem based on the Fast Fourier Transform. Together with uniform resampling of the curves with respect to arc length, the new algorithm results in an order of magnitude speed-up in our experiments. Additionally, we describe how we can use our new algorithm as part of elastic shape distance computations between closed curves to obtain accurate shape distance values at a fraction of the cost of previous approaches.