diff --git a/README.rst b/README.rst
index d0ffe7a11cf6ba84655d6238a6ac0cb8e515d99b..97f135e6f8840646f49985c9ae12862ef4df0806 100644
--- a/README.rst
+++ b/README.rst
@@ -2,38 +2,38 @@
.. Fri 08 Jul 2016 15:38:56 CEST
.. image:: http://img.shields.io/badge/docs-stable-yellow.png
- :target: http://pythonhosted.org/bob.fingervein/index.html
+ :target: http://pythonhosted.org/bob.bio.vein/index.html
.. image:: http://img.shields.io/badge/docs-latest-orange.png
- :target: https://www.idiap.ch/software/bob/docs/latest/bioidiap/bob.fingervein/master/index.html
-.. image:: https://travis-ci.org/bioidiap/bob.fingervein.svg?branch=master
- :target: https://travis-ci.org/bioidiap/bob.fingervein
-.. image:: https://coveralls.io/repos/bioidiap/bob.fingervein/badge.png
- :target: https://coveralls.io/r/bioidiap/bob.fingervein
+ :target: https://www.idiap.ch/software/bob/docs/latest/bioidiap/bob.bio.vein/master/index.html
+.. image:: https://travis-ci.org/bioidiap/bob.bio.vein.svg?branch=master
+ :target: https://travis-ci.org/bioidiap/bob.bio.vein
+.. image:: https://coveralls.io/repos/bioidiap/bob.bio.vein/badge.png
+ :target: https://coveralls.io/r/bioidiap/bob.bio.vein
.. image:: https://img.shields.io/badge/github-master-0000c0.png
- :target: https://github.com/bioidiap/bob.fingervein/tree/master
-.. image:: http://img.shields.io/pypi/v/bob.fingervein.png
- :target: https://pypi.python.org/pypi/bob.fingervein
-.. image:: http://img.shields.io/pypi/dm/bob.fingervein.png
- :target: https://pypi.python.org/pypi/bob.fingervein
+ :target: https://github.com/bioidiap/bob.bio.vein/tree/master
+.. image:: http://img.shields.io/pypi/v/bob.bio.vein.png
+ :target: https://pypi.python.org/pypi/bob.bio.vein
+.. image:: http://img.shields.io/pypi/dm/bob.bio.vein.png
+ :target: https://pypi.python.org/pypi/bob.bio.vein
=========================================
The Biometrics Vein Recognition Library
=========================================
-Welcome to the Finger vein Recognition Library based on Bob. This library is
-designed to perform a fair comparison of finger vein recognition algorithms.
-It contains scripts to execute various kinds of finger vein recognition
-experiments on a variety of finger vein image databases, and running the help
-is as easy as going to the command line and typing::
+Welcome to this Vein Recognition Library based on Bob. This library is designed
+to perform a fair comparison of vein recognition algorithms. It contains
+scripts to execute various of vein recognition experiments on a variety
+of vein image databases, and running the help is as easy as going to the
+command line and typing::
- $ bin/veinverify.py --help
+ $ bin/verify.py --help
About
-----
-This library is developed at the `Biometrics group
+This library is currently developed at the `Biometrics group
<http://www.idiap.ch/scientific-research/research-groups/biometric-person-recognition>`_
at the `Idiap Research Institute <http://www.idiap.ch>`_. The vein recognition
library is designed to run vein recognition experiments in a comparable and
@@ -46,7 +46,7 @@ Databases
To achieve this goal, interfaces to some publicly available vein image
databases are contained, and default evaluation protocols are defined, e.g.:
-- UTFVP - University of Twente Finger Vein Database [http://website]
+- UTFVP - University of Twente Finger Vein Database [http://www.sas.ewi.utwente.nl/]
- VERA Finger vein Database [http://www.idiap.ch/scientific-research/resources]
@@ -56,9 +56,9 @@ Algorithms
Together with that, implementations of a variety of traditional and
state-of-the-art vein recognition algorithms are provided:
-* Maximum Curvature [MNM+05]_
-* Repeated Line Tracking [MNM+04]_
-* Wide Line Detector [HDLTL+10]_
+* Maximum Curvature [MNM05]_
+* Repeated Line Tracking [MNM04]_
+* Wide Line Detector [HDLTL10]_
Tools to evaluate the results can easily be used to create scientific plots. We
also provide handles to run experiments using parallel processes or an SGE
@@ -70,37 +70,46 @@ Extensions
On top of these already pre-coded algorithms, the vein recognition library
provides an easy Python interface for implementing new image preprocessors,
-feature types, finger vein recognition algorithms or database interfaces, which
-directly integrate into the fingervein recognition experiment. Hence, after a
-short period of coding, researchers can compare their new invention directly
-with already existing algorithms in a fair manner.
+feature types, vein recognition algorithms or database interfaces, which
+directly integrate into the vein recognition experiment. Hence, after a short
+period of coding, researchers can compare new ideas directly with already
+existing algorithms in a fair manner.
References
----------
-.. [MNM+05] *N. Miura, A. Nagasaka, and T. Miyatake*. **Extraction of Finger-Vein Pattern Using Maximum Curvature Points in Image Profiles**. Proceedings on IAPR conference on machine vision applications, 9, pp. 347--350, 2005.
+.. [MNM05] *N. Miura, A. Nagasaka, and T. Miyatake*. **Extraction of Finger-Vein Pattern Using Maximum Curvature Points in Image Profiles**. Proceedings on IAPR conference on machine vision applications, 9, pp. 347--350, 2005.
-.. [MNM+04] *N. Miura, A. Nagasaka, and T. Miyatake*. **Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification**. Machine Vision and Applications, Vol. 15, Num. 4, pp. 194--203, 2004.
+.. [MNM04] *N. Miura, A. Nagasaka, and T. Miyatake*. **Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification**. Machine Vision and Applications, Vol. 15, Num. 4, pp. 194--203, 2004.
-.. [HDLTL+10] *B. Huang, Y. Dai, R. Li, D. Tang and W. Li*. **Finger-vein authentication based on wide line detector and pattern normalization**. Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010.
+.. [HDLTL10] *B. Huang, Y. Dai, R. Li, D. Tang and W. Li*. **Finger-vein authentication based on wide line detector and pattern normalization**. Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010.
Installation
------------
-To download the finger vein library, go to
-http://pypi.python.org/pypi/bob.bio.vein click on the **download** button and
-extract the .zip file to a folder of your choice.
+The latest version of the vein recognition library can be installed with our
+`Conda-based builds`_. Once you have installed Bob_, just go on and install
+this package using the same installation mechanism.
-The FingerVeinRecLib is a satellite package of the free signal processing and
-machine learning library Bob_. We advise you install Bob first and then use
-that installation to bootstrap the installation of this package::
- $ python bootstrap.py
- $ bin/buildout
+Development
+-----------
-This will download any further dependencies and install them locally.
+In order to develop the latest version of this package::
+
+ $ git clone https://gitlab.idiap.ch/biometrc/bob.bio.vein.git
+ $ cd bob.bio.vein
+ $ python bootstrap-buildout.py
+ $ ./bin/buildout
+
+After those steps, you should have a functional **development** environment to
+test the package. The python interpreter and base environment on line 3 above
+should be pre-installed with all dependencies required for Bob_ to operate
+correctly. For example, you may start from our `conda-based builds`_ and then
+use the Python interpreter in there to bootstrap your local development
+environment.
Running tests
@@ -109,9 +118,8 @@ Running tests
To verify that your installation worked as expected, you might want to run our
unit tests with::
- $ bin/nosetests
+ $ ./bin/nosetests -sv
-Usually, all tests should pass, if you use the latest packages of Bob_.
Cite our paper
@@ -131,8 +139,9 @@ paper::
year = {2014},
ocation = {Darmstadt, Germay},
pdf = {http://publications.idiap.ch/downloads/papers/2014/Tome_IEEEBIOSIG2014.pdf}
-}
+ }
.. _bob: http://www.idiap.ch/software/bob
.. _idiap: http://www.idiap.ch
+.. _conda-based builds: https://github.com/idiap/bob/wiki/Binary-Installation