A Fuzzy View on k-Means Based Signal Quantization with Application in Iris Segmentation

TL;DR

This paper explores fuzzy and crisp interpretations of k-means signal quantization, introduces combined indicator functions, and applies these concepts to iris segmentation with a demo program.

Contribution

It introduces combined crisp and fuzzy indicator functions as a natural generalization of existing functions for signal quantization.

Findings

Fuzzy interpretation of k-means enhances boundary detection.

Combined indicator functions improve segmentation accuracy.

Demo program showcases practical application in iris segmentation.

Abstract

This paper shows that the k-means quantization of a signal can be interpreted both as a crisp indicator function and as a fuzzy membership assignment describing fuzzy clusters and fuzzy boundaries. Combined crisp and fuzzy indicator functions are defined here as natural generalizations of the ordinary crisp and fuzzy indicator functions, respectively. An application to iris segmentation is presented together with a demo program.