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Automatic segmentation of pupil using local histogram and standard deviation

Authors: Muhammad Talal Ibrahim; Tariq Mahmood Khan; Muhammad Aurangzeb Khan; Ling Guan;

Automatic segmentation of pupil using local histogram and standard deviation

Abstract

This paper presents a novel approach for automatic pupil segmentation. The proposed algorithm uses local histogram and standard deviation based adaptive thresholding method that looks for the region that has the highest probability of having the pupil. We have tested our proposed algorithm on two public databases namely: CASIA v1.0 and MMU v1.0. Experimental results show that the proposed method has satisfying performance and good robustness against the reflection in the pupil.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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