# Signatures in Shape Analysis: an Efficient Approach to Motion Identification

## TL;DR

This paper introduces a shape classification method using signatures, offering an efficient alternative to existing techniques like SRV transform and dynamic programming, with potential improvements in speed and accuracy.

## Contribution

It presents a novel shape classification approach leveraging signatures, providing a reparametrization invariant method that enhances efficiency over current methods.

## Key findings

- Signature-based classification is competitive with SRV transform.
- The proposed method offers improved computational efficiency.
- It effectively distinguishes shapes in various scenarios.

## Abstract

Signatures provide a succinct description of certain features of paths in a reparametrization invariant way. We propose a method for classifying shapes based on signatures, and compare it to current approaches based on the SRV transform and dynamic programming.