Clustering algorithms to obtain regions of interest: a comparative study

Claudio M. Privitera; Nikhil Krishnan; Lawrence W. Stark

doi:10.1117/12.387197

2 June 2000 Clustering algorithms to obtain regions of interest: a comparative study

Claudio M. Privitera, Nikhil Krishnan, Lawrence W. Stark

Proceedings Volume 3959, Human Vision and Electronic Imaging V; (2000) https://doi.org/10.1117/12.387197
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

In parallel with our studies on human eye movements, we have investigated image processing algorithms that predict where human eyes fixate. These loci of fixations, traditionally named Regions-of-Interest, ROIs, are strategically important both for computer applications and for cognitive studies of human visual processing. A very important aspect of our methodology, beyond the specific image processing algorithms used, is how to select from a large initial set of candidates, usually local maxima in the processed image, a final set of few ROIs. In this paper we analyze this latter aspect, proposing and comparing different clustering procedures and study how different procedures may affect the fidelity of comparisons with human selected ROIs.

Citation Download Citation

Claudio M. Privitera, Nikhil Krishnan, and Lawrence W. Stark "Clustering algorithms to obtain regions of interest: a comparative study", Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); https://doi.org/10.1117/12.387197

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