1.1 --- a/reports/final/content/method.tex Thu Mar 18 18:34:20 2010 +0000
1.2 +++ b/reports/final/content/method.tex Fri Mar 19 09:09:52 2010 +0000
1.3 @@ -1,5 +1,5 @@
1.4 \chapter{Methodology}
1.5 -To achieve automated iris recognition, there are three main tasks: First we must locate the iris in a given image. Secondly, it is necessary to encode the iris information into a format which is amenable to calculation and computation, for example a binary string. Finally, the data must be storable, to load and compare these encodings.
1.6 +To achieve automated iris recognition, there are three main tasks: first we must locate the iris in a given image. Secondly, it is necessary to encode the iris information into a format which is amenable to calculation and computation, for example a binary string. Finally, the data must be storable, to load and compare these encodings.
1.7
1.8 \section{Iris Location}
1.9 When locating the iris there are two potential options. The software could
1.10 @@ -127,7 +127,7 @@
1.11 streamlined to accelerate the computation.
1.12
1.13 Thresholding the input image has the inherent benefit of changing the virtual
1.14 -bit-depth of the image to 1 (black or white), the implemented algorithm saves
1.15 +bit-depth of the image to 1 (black or white). Furthermore, the implemented algorithm saves
1.16 time by only operating on black pixels -- a greyscale implementation would
1.17 significantly add to the processing time. Additionally a search on the
1.18 threshold result (figure \ref{pupil_step_3_thresh}) gives confident parameter
