Abstract: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 \textit{MPEG-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.
| Subjects: | Methodology (stat.ME); Machine Learning (stat.ML) |
| Cite as: | arXiv:2503.05632 [stat.ME] |
| (or arXiv:2503.05632v2 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2503.05632 arXiv-issued DOI via DataCite |
From: Issam-Ali Moindjié [view email]
[v1]
Fri, 7 Mar 2025 17:55:14 UTC (3,305 KB)
[v2]
Sun, 24 May 2026 13:04:23 UTC (1,725 KB)
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