Rigid and Non-rigid Shape Evolutions for Shape Alignment and Recovery in Images

TL;DR

This paper introduces a novel shape alignment and recovery method using deterministic energy minimization and prior variation shape evolution (PVSE), effectively handling rigid and non-rigid deformations in challenging images.

Contribution

It presents a general framework for shape alignment and recovery based on PVSE, incorporating shape-preserving deformation models derived from active contour energies.

Findings

Experimental results validate the shape-preserving properties of PVSE.

The method effectively handles complex shape deformations.

The approach outperforms existing shape extraction techniques.

Abstract

The same type of objects in different images may vary in their shapes because of rigid and non-rigid shape deformations, occluding foreground as well as cluttered background. The problem concerned in this work is the shape extraction in such challenging situations. We approach the shape extraction through shape alignment and recovery. This paper presents a novel and general method for shape alignment and recovery by using one example shapes based on deterministic energy minimization. Our idea is to use general model of shape deformation in minimizing active contour energies. Given \emph{a priori} form of the shape deformation, we show how the curve evolution equation corresponding to the shape deformation can be derived. The curve evolution is called the prior variation shape evolution (PVSE). We also derive the energy-minimizing PVSE for minimizing active contour energies. For shape recovery, we propose to use the PVSE that deforms the shape while preserving its shape characteristics. For choosing such shape-preserving PVSE, a theory of shape preservability of the PVSE is established. Experimental results validate the theory and the formulations, and they demonstrate the effectiveness of our method.