# A Functional Approach to Curve Alignment and Shape Analysis

arXiv: https://arxiv.org/abs/2503.05632 · HTML: https://mlsanity.com/paper/2503.05632/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains](https://mlsanity.com/paper/1509.02029/) (2015)
- [Functional Data Analysis of Amplitude and Phase Variation](https://mlsanity.com/paper/1512.03216/) (2015)
- [Statistical Inference on the Hilbert Sphere with Application to Random Densities](https://mlsanity.com/paper/2101.00527/) (2021)
- [Functional additive models on manifolds of planar shapes and forms](https://mlsanity.com/paper/2109.02624/) (2021)
- [A General Framework for Multivariate Functional Principal Component Analysis of Amplitude and Phase Variation](https://mlsanity.com/paper/1810.13321/) (2018)

## Built on by (most cited first, 2 of 2)

- [Multivariate Planar Curves: A Statistical Framework for Shape Analysis in Images](https://mlsanity.com/paper/2508.11780/) (2025)
- [Shape-Based Inductive Bias for Glioma Grading from Tumor Contours](https://mlsanity.com/paper/2607.26090/) (2026)

## Abstract (arXiv)

In many image analysis problems, the contours of objects carry important statistical information about shape. Such contours are typically affected by deformation variables including scaling, translation, rotation, and reparametrization. Previous studies in statistical shape analysis have mainly focused on analyzing contours and shapes through discrete observations. While this approach might offer computational advantages, it overlooks the continuous nature of these objects and their underlying geometric structure. It also ignores potential dependencies between the deformation variables and their effect on the shape, which may result in a loss of statistical information and reduced interpretability. In this paper, we introduce a novel framework for analyzing shapes within the context of Functional Data Analysis (FDA). Basis expansion techniques are employed to derive analytic solutions for the estimation of deformation variables, namely scaling, translation, rotation, and reparametrization, thereby achieving curve alignment. A generative model for random contours is then developed using principal component analysis techniques. Numerical experiments on simulated data and the textitMPEG-7 database demonstrate that our method successfully identifies deformation parameters and captures the underlying distribution of random contours in settings where traditional FDA methods fail.