lambda-Connectedness Determination for Image Segmentation

TL;DR

This paper introduces two novel methods for automatically determining the connectivity parameter in lambda-connected image segmentation, leveraging maximum entropy and minimum variance principles, and compares them with existing techniques.

Contribution

It proposes new automated parameter selection methods for lambda-connected segmentation based on entropy and variance principles, enhancing segmentation accuracy.

Findings

Maximum entropy method effectively determines optimal lambda values.

Minimum variance method provides robust segmentation results.

Extended methods show promise compared to Mumford-Shah approach.

Abstract

Image segmentation is to separate an image into distinct homogeneous regions belonging to different objects. It is an essential step in image analysis and computer vision. This paper compares some segmentation technologies and attempts to find an automated way to better determine the parameters for image segmentation, especially the connectivity value of $\lambda$ in $\lambda$-connected segmentation. Based on the theories on the maximum entropy method and Otsu's minimum variance method, we propose:(1)maximum entropy connectedness determination: a method that uses maximum entropy to determine the best $\lambda$ value in $\lambda$-connected segmentation, and (2) minimum variance connectedness determination: a method that uses the principle of minimum variance to determine $\lambda$ value. Applying these optimization techniques in real images, the experimental results have shown great promise in the development of the new methods. In the end, we extend the above method to more general case in order to compare it with the famous Mumford-Shah method that uses variational principle and geometric measure.