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

— 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. Keywords — circular fuzzy iris ring, circular fuzzy limbic boundary, combined crisp indicator function, combined fuzzy indicator function, fast k-means quantization, fuzzy clusters, fuzzy boundaries, iris recognition, iris segmentation, k-means.

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Nicolaie Popescu-Bodorin
• Keywords: CORR 2011, Crisp Indicator Function, Education, Fuzzy Indicator Function, Indicator Function