Compensated Convex Based Transforms for Image Processing and Shape Interrogation

TL;DR

This paper reviews recent applications of compensated convex transforms in image processing and shape interrogation, emphasizing stability and multiscale features, supported by numerical experiments showing competitive performance.

Contribution

It introduces the use of compensated convex transforms and proximity hulls for image and shape analysis, highlighting their stability and multiscale capabilities.

Findings

Demonstrates Hausdorff stability of the methods

Shows effectiveness through numerical experiments

Achieves competitive results compared to state-of-the-art techniques

Abstract

This paper reviews some recent applications of the theory of the compensated convex transforms or of the proximity hull as developed by the authors to image processing and shape interrogation with special attention given to the Hausdorff stability and multiscale properties. The paper contains also numerical experiments that demonstrate the performance of our methods compared to the state-of-art ones.