Classification of curves in 2D and 3D via affine integral signatures

TL;DR

This paper introduces a new affine invariant signature for classifying 2D and 3D curves, especially effective on noisy data, by using integral invariants that outperform classical differential invariants.

Contribution

The paper presents the first affine integral invariants for spatial curves and develops global and local signatures that are robust to noise and occlusions.

Findings

Affine integral invariants outperform differential invariants on noisy data

Global signature is sensitive to occlusions and initial point choice

Local signature is robust to occlusions and useful for local curve comparison

Abstract

We propose a robust classification algorithm for curves in 2D and 3D, under the special and full groups of affine transformations. To each plane or spatial curve we assign a plane signature curve. Curves, equivalent under an affine transformation, have the same signature. The signatures introduced in this paper are based on integral invariants, which behave much better on noisy images than classically known differential invariants. The comparison with other types of invariants is given in the introduction. Though the integral invariants for planar curves were known before, the affine integral invariants for spatial curves are proposed here for the first time. Using the inductive variation of the moving frame method we compute affine invariants in terms of Euclidean invariants. We present two types of signatures, the global signature and the local signature. Both signatures are independent of parameterization (curve sampling). The global signature depends on the choice of the initial point and does not allow us to compare fragments of curves, and is therefore sensitive to occlusions. The local signature, although is slightly more sensitive to noise, is independent of the choice of the initial point and is not sensitive to occlusions in an image. It helps establish local equivalence of curves. The robustness of these invariants and signatures in their application to the problem of classification of noisy spatial curves extracted from a 3D object is analyzed.