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Departamento
de Informática da Universidade da Beira Interior |
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SOCIA Lab. – Soft Computing and
Image Analysis Group Department of Computer Science, University of Beira
Interior, 6201-001 Covilhã, Portugal |
NICE:II Protocol 1. Overview. In order to participate in the
NICE:II contest, a registration form and an application executable should be
submitted. 1.1. The executable should receive
(through command-line parameters) the path of 4 images (2 iris images + 2
corresponding segmentation maps) and output a text file (which name and path
is received in the 5th parameter) that contains a numeric value
corresponding to the dissimilarity between these iris images. 1.2. The outputted dissimilarity value should be
a metric. Let I be a set of iris images. A metric on I is a function d:I x I
--> R that satisfies the following conditions: 1.2.1. d(I1, I1)=0 1.2.2. d(I1, I2)=0
-->I1=I2 1.2.3. d(I1, I2) +
d(I1, I3) >= d(I2, I3) 1.3. Example of the application
functioning, through command-line: >
app I1.jpg I1.bmp I2.jpg I2.bmp
I1_I2.txt An
overview of the task demanded to the NICE:II participants is given in figure
1:
Figure 1: NICE.II fundamental task. 1.4. The application executable can be
written in any programming language (Matlab inclusively) and should run in
one of the operating systems:”Windows XP, Service Pack 2” or ”Fedora Core 6”. 1.5. There will be no internet access
during the NICE:II evaluation. Thus, the application executable will need to
be installed and executed without access to the internet. 2. Registration. Each NICE:II participant will
receive a username. This username should be used as name of the submitted
executable. 2.1. Each participant is allowed to
submit one single algorithm and executable. 2.2. NICE:II participation agreement.
The application form must be filled and sent to the contest email address. 3. Evaluation. The NICE:II contest will be
evaluated by a Java framework built within the SOCIA Lab and within the
UBIRIS.v2 data set. 3.1. The evaluation framework will be
available to NICE:II participants, in order to facilitate the training and
tuning of their iris classification methods. 3.2. Together with the evaluation
framework, a data set of noisy iris images (portion of the UBIRIS.v2
database) will be given, with have close characteristics to the images used
in the evaluation stage. Additionally, the set of the correspondent and
manually classified “.bmp” images will also be given. 3.2.1. The image format of the provided
data set of input images is “.tiff”. 3.2.2. The binary maps that give the
segmentation of the iris images have the same name and “.bmp” format.
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DI-UBI Bloco VI Rua Marquês de Ávila e Bolama P- 6201-001
Covilhã PORTUGAL