Race Classification Based Iris Image Segmentation

2020 ◽  
pp. 383-393
Author(s):  
Xianting Ke ◽  
Lingling An ◽  
Qingqi Pei ◽  
Xuyu Wang
Keyword(s):  
Image Segmentation ◽  
Iris Image

A simple shape prior model for iris image segmentation

2011 ◽  
Author(s):  
Daniel A. Bishop ◽  
Anthony Yezzi, Jr.
Keyword(s):  
Image Segmentation ◽  
Shape Prior ◽  
Simple Shape ◽  
Prior Model ◽  
Iris Image

Video-Based Noncooperative Iris Image Segmentation

2011 ◽  
Vol 41 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Yingzi Du ◽  
E Arslanturk ◽  
Zhi Zhou ◽  
C Belcher
Keyword(s):  
Image Segmentation ◽  
Iris Image

scholarly journals An Improved Bovine Iris Segmentation Method

2019 ◽  
Vol 267 ◽  
pp. 03002
Author(s):  
Zhongliang Luo ◽  
Jingguo Dai ◽  
Yingbiao Jia ◽  
Jiazhong He
Keyword(s):  
Image Segmentation ◽  
Iris Recognition ◽  
Noise Suppression ◽  
Safety Management ◽  
Iris Segmentation ◽  
Iris Image ◽  
Enhancement Method ◽  
Iris Feature Extraction ◽  
Speed And Accuracy ◽  
Image Segmentation Algorithm

In order to improve the performance of bovine iris image segmentation, an improved iris image segmentation algorithm is proposed according to the characteristics of bovine iris image. Firstly, based on mathematical morphology and noise suppression template, the inner and outer edges of bovine iris are detected by dynamic contour tracking and least squares fitting ellipse respectively. Then, the annular iris region is normalized. Finally, the normalized iris image is enhanced with adaptive image enhancement method. The experimental results show that the algorithm can effectively segment iris region, it has good performance of speed and accuracy for iris segmentation, and can eliminate the effects of uneven illumination, iris shrinkage and rotation, it promotes iris feature extraction and matching, which has certain reference significance for iris recognition research and meat food safety management of large livestock.

scholarly journals Iris Image Segmentation and Localization using Dynamic Reconfigurable Processor (DRP)

2020 ◽  
Vol 2 (3) ◽  
pp. 147-155
Author(s):  
Smaran S. Rao ◽  
Shreyas R. ◽  
Gajanan Maske ◽  
Antara Roy Choudhury
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Iris Recognition ◽  
Image Capture ◽  
Iris Localization ◽  
Reconfigurable Processor ◽  
Iris Image ◽  
Survey Paper ◽  
Configurable Processor ◽  
Iris Feature Extraction

Recognition of the Iris is among the finest techniques in the field of bio-metrics identification, because the iris has characteristics that are unique and stay the same all through the individual’s life. Iris recognition phases are namely image acquisition, segmentation of iris, localization of iris, feature extraction of iris and matching. This paper, which is an extension of the survey paper Smaran et.al[1], concentrates purely on the procedures of image capture, segmentation as well as localization of the iris. The aim of the paper is to optimize the above mentioned processes in terms of distance of capturing the image, time taken for memory and computation requirements, using the DRP (Dynamic Re-Configurable Processor) technology, uniquely developed by Renesas Electronics (www.renesas.com).

scholarly journals Contourlet Transform for Iris Image Segmentation

2012 ◽  
Vol 60 (10) ◽  
pp. 41-44 ◽  
Author(s):  
Behrooz Zali-Vargahan ◽  
Mehdi Chehel Amirani ◽  
Hadi Seyedarabi
Keyword(s):  
Image Segmentation ◽  
Contourlet Transform ◽  
Iris Image
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