Convex Shape Priors for Level Set Representation

TL;DR

This paper introduces a level set method with convex shape priors for shape representation and segmentation, providing a necessary and sufficient condition for convexity and demonstrating improved performance with landmark integration.

Contribution

It presents a novel convexity condition for level set functions and develops algorithms for shape segmentation that incorporate landmarks, enhancing segmentation accuracy.

Findings

Convexity condition for level set functions established.

Algorithms validated on various images showing improved segmentation.

Landmark integration enhances segmentation performance.

Abstract

For many applications, we need to use techniques to represent convex shapes and objects. In this work, we use level set method to represent shapes and find a necessary and sufficient condition on the level set function to guarantee the convexity of the represented shapes. We take image segmentation as an example to apply our technique. Numerical algorithm is developed to solve the variational model. In order to improve the performance of segmentation for complex images, we also incorporate landmarks into the model. One option is to specify points that the object boundary must contain. Another option is to specify points that the foreground (the object) and the background must contain. Numerical experiments on different images validate the efficiency of the proposed models and algorithms. We want to emphasize that the proposed technique could be used for general shape optimization with convex shape prior. For other applications, the numerical algorithms need to be extended and modified.