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iris-Segmentation-and-normalization
  • Classification:Numerical Algorithm-Artificial Intelligence - matlab
  • Development Tool:matlab
  • Sise:411 KB
  • Upload time:2013/10/14 23:17:16
  • Uploader:mniran_91
  • Download Statistics:
Description
Segmentation of iris and its normalization in matlab


2.bmp

File list:
iris Segmentation and normalization
..................................\1
..................................\.\1
..................................\.\.\1-noise.bmp
1-noise.bmp
..................................\.\.\1-polar.bmp
1-polar.bmp
..................................\.\.\1-polarnoise.bmp
1-polarnoise.bmp
..................................\.\.\1-segmented.bmp
1-segmented.bmp
..................................\.\.\1.bmp
1.bmp
..................................\.\.\2-noise.bmp
2-noise.bmp
..................................\.\.\2-polar.bmp
2-polar.bmp
..................................\.\.\2-polarnoise.bmp
2-polarnoise.bmp
..................................\.\.\2-segmented.bmp
2-segmented.bmp
..................................\.\.\2.bmp
2.bmp
..................................\.\2
..................................\addcircle.m
..................................\adjgamma.m
..................................\canny.m
..................................\circlecoords.m
..................................\findcircle.m
..................................\findline.m
..................................\houghcircle.m
..................................\hysthresh.m
..................................\linecoords.m
..................................\main_segmention.m
..................................\nonmaxsup.m
..................................\normaliseiris.m
..................................\segmentiris.m
Related source code
[Final-Code-iris-recognition] - the code contain iris recognition code
[07194848iris-recogniton-with-haar-wa...] - iris recognition using haar wavelet
[iris-classification-matlab-master] - In this report, we devise a methodology for identifying the species of an iris amongst 3 based on 4 distinctive features. We first cover the constitution of the data set and input patterns. We then determine the layout and structure of the neural network we will use for the classification. We continue by testing the network in real life conditions. We conclude with a review of the methods used, and how they could be improved.
[Iris-Code] - iris code for detection of eye retena region for developing code
[IRIS-SEGMENT] - WORKING CODE FOR IRIS SEGMENTATION
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