Automatic segmentation of pupil using local histogram and standard deviation

Muhammad Talal Ibrahim; Tariq M. Khan; M. Aurangzeb Khan; Ling Guan

doi:10.1117/12.863264

Automatic segmentation of pupil using local histogram and standard deviation

Muhammad Talal Ibrahim, Tariq M. Khan, M. Aurangzeb Khan, Ling Guan

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	Visual Communications and Image Processing 2010
Place of Publication	Washington, DC
Publisher	SPIE
Pages	77442S-1 - 77442S-8
Number of pages	8
Volume	7744
ISBN (Print)	9780819482341
DOIs	https://doi.org/10.1117/12.863264
Publication status	Published - 2010
Externally published	Yes
Event	Visual Communications and Image Processing 2010 - Huangshan, China Duration: 11 Jul 2010 → 14 Jul 2010

Other

Other	Visual Communications and Image Processing 2010
Country/Territory	China
City	Huangshan
Period	11/07/10 → 14/07/10

Keywords

Adaptive thresholding
Pupil segmentation
Standard deviation

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10.1117/12.863264

Cite this

@inproceedings{3918363a4c7242848ec62d85a1b70b9f,

title = "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.",

keywords = "Adaptive thresholding, Pupil segmentation, Standard deviation",

author = "Ibrahim, \{Muhammad Talal\} and Khan, \{Tariq M.\} and Khan, \{M. Aurangzeb\} and Ling Guan",

year = "2010",

doi = "10.1117/12.863264",

language = "English",

isbn = "9780819482341",

volume = "7744",

pages = "77442S--1 -- 77442S--8",

booktitle = "Visual Communications and Image Processing 2010",

publisher = "SPIE",

address = "United States",

note = "Visual Communications and Image Processing 2010 ; Conference date: 11-07-2010 Through 14-07-2010",

}

Automatic segmentation of pupil using local histogram and standard deviation. / Ibrahim, Muhammad Talal; Khan, Tariq M.; Khan, M. Aurangzeb et al.
Visual Communications and Image Processing 2010. Vol. 7744 Washington, DC: SPIE, 2010. p. 77442S-1 - 77442S-8 77442S.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Automatic segmentation of pupil using local histogram and standard deviation

AU - Ibrahim, Muhammad Talal

AU - Khan, Tariq M.

AU - Khan, M. Aurangzeb

AU - Guan, Ling

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Adaptive thresholding

KW - Pupil segmentation

KW - Standard deviation

UR - https://www.scopus.com/pages/publications/78649785637

U2 - 10.1117/12.863264

DO - 10.1117/12.863264

M3 - Conference proceeding contribution

AN - SCOPUS:78649785637

SN - 9780819482341

VL - 7744

SP - 77442S-1 - 77442S-8

BT - Visual Communications and Image Processing 2010

PB - SPIE

CY - Washington, DC

T2 - Visual Communications and Image Processing 2010

Y2 - 11 July 2010 through 14 July 2010

ER -

Automatic segmentation of pupil using local histogram and standard deviation

Abstract

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Keywords

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