Shape Prior Segmentation Guided by Harmonic Beltrami Signature

TL;DR

This paper introduces a shape prior segmentation method using the Harmonic Beltrami Signature (HBS), which robustly captures shape features and improves segmentation accuracy, especially in challenging conditions.

Contribution

It presents a novel shape segmentation framework that integrates HBS within a quasi-conformal topology preserving approach, enhancing robustness and shape fidelity.

Findings

Improves segmentation accuracy over baseline methods.

Resists noise and occlusions effectively.

Eliminates preprocessing requirements.

Abstract

This paper presents a novel shape prior segmentation method guided by the Harmonic Beltrami Signature (HBS). The HBS is a shape representation fully capturing 2D simply connected shapes, exhibiting resilience against perturbations and invariance to translation, rotation, and scaling. The proposed method integrates the HBS within a quasi-conformal topology preserving segmentation framework, leveraging shape prior knowledge to significantly enhance segmentation performance, especially for low-quality or occluded images. The key innovation lies in the bifurcation of the optimization process into two iterative stages: 1) The computation of a quasi-conformal deformation map, which transforms the unit disk into the targeted segmentation area, driven by image data and other regularization terms; 2) The subsequent refinement of this map is contingent upon minimizing the $L_2$ distance between its Beltrami coefficient and the reference HBS. This shape-constrained refinement ensures that the segmentation adheres to the reference shape(s) by exploiting the inherent invariance, robustness, and discerning shape discriminative capabilities afforded by the HBS. Extensive experiments on synthetic and real-world images validate the method's ability to improve segmentation accuracy over baselines, eliminate preprocessing requirements, resist noise corruption, and flexibly acquire and apply shape priors. Overall, the HBS segmentation framework offers an efficient strategy to robustly incorporate the shape prior knowledge, thereby advancing critical low-level vision tasks.