diff --git a/doc/baselines.rst b/doc/baselines.rst
index d74e3d4d0aedf04914be8ac2dfb66d23c4409e66..ca135d709766ab65632d9a1f173911b4aff04064 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -53,7 +53,7 @@ is available on the section :ref:`bob.bio.vein.resources`.
instructions described in this guide. You **need first** to procure yourself
the raw data files that correspond to *each* database used here in order to
correctly run experiments with those data. Biometric data is considered
- private date and, under EU regulations, cannot be distributed without a
+ private data and, under EU regulations, cannot be distributed without a
consent or license. You may consult our
:ref:`bob.bio.vein.resources.databases` resources section for checking
currently supported databases and accessing download links for the raw data
@@ -227,34 +227,28 @@ This package may generate results for other combinations of protocols and
databases. Here is a summary table for some variants (results expressed
correspond to the the equal-error rate on the development set, in percentage):
-======================== ================= ====== ====== ====== ====== ======
- Approach Vera Finger UTFVP
------------------------------------------- -------------------- -------------
- Feature Extractor Post Processing Full B Nom 1vsall nom
-======================== ================= ====== ====== ====== ====== ======
-Repeated Line Tracking None 23.9 24.1 24.9 1.7 1.4
-Repeated Line Tracking Histogram Eq. 26.2 23.6 24.9 2.1 0.9
-Maximum Curvature None 3.2 3.2 3.1 0.4 0.
-Maximum Curvature Histogram Eq. 3.0 2.7 2.7 0.4 0.
-Wide Line Detector None 10.2 10.2 10.5 2.3 1.7
-Wide Line Detector Histogram Eq. 8.0 9.7 7.3 1.7 0.9
-======================== ================= ====== ====== ====== ====== ======
+======================== ====== ====== ====== ====== ======
+ Toolchain Vera Finger UTFVP
+------------------------ -------------------- -------------
+ Feature Extractor Full B Nom 1vsall nom
+======================== ====== ====== ====== ====== ======
+Repeated Line Tracking 23.9 24.1 24.9 1.7 1.4
+Wide Line Detector 10.2 10.2 10.5 2.3 1.7
+Maximum Curvature 3.2 3.2 3.1 0.4 0.
+======================== ====== ====== ====== ====== ======
In a machine with 48 cores, running these baselines took the following time
(hh:mm):
-======================== ================= ====== ====== ====== ====== ======
- Approach Vera Finger UTFVP
------------------------------------------- -------------------- -------------
- Feature Extractor Post Processing Full B Nom 1vsall nom
-======================== ================= ====== ====== ====== ====== ======
-Repeated Line Tracking None 01:16 00:23 00:23 12:44 00:35
-Repeated Line Tracking Histogram Eq. 00:50 00:23 00:23 13:00 00:35
-Maximum Curvature None 03:28 00:54 00:59 58:34 01:48
-Maximum Curvature Histogram Eq. 02:45 00:54 00:59 49:03 01:49
-Wide Line Detector None 00:07 00:01 00:01 02:25 00:05
-Wide Line Detector Histogram Eq. 00:04 00:01 00:01 02:04 00:06
-======================== ================= ====== ====== ====== ====== ======
+======================== ====== ====== ====== ====== ======
+ Toolchain Vera Finger UTFVP
+------------------------ -------------------- -------------
+ Feature Extractor Full B Nom 1vsall nom
+======================== ====== ====== ====== ====== ======
+Repeated Line Tracking 01:16 00:23 00:23 12:44 00:35
+Wide Line Detector 00:07 00:01 00:01 02:25 00:05
+Maximum Curvature 03:28 00:54 00:59 58:34 01:48
+======================== ====== ====== ====== ====== ======
Modifying Baseline Experiments
@@ -326,6 +320,49 @@ This package contains other resources that can be used to evaluate different
bits of the vein processing toolchain.
+Training the Watershed Finger region detector
+=============================================
+
+The correct detection of the finger boundaries is an important step of many
+algorithms for the recognition of finger veins. It allows to compensate for
+eventual rotation and scaling issues one might find when comparing models and
+probes. In this package, we propose a novel finger boundary detector based on
+the `Watershedding Morphological Algorithm
+<https://en.wikipedia.org/wiki/Watershed_(image_processing)>`. Watershedding
+works in three steps:
+
+1. Determine markers on the original image indicating the types of areas one
+ would like to detect (e.g. "finger" or "background")
+2. Determine a 2D (gray-scale) surface representing the original image in which
+ darker spots (representing valleys) are more likely to be filled by
+ surrounding markers. This is normally achieved by filtering the image with a
+ high-pass filter like Sobel or using an edge detector such as Canny.
+3. Run the watershed algorithm
+
+In order to determine markers for step 1, we train a neural network which
+outputs the likelihood of a point being part of a finger, given its coordinates
+and values of surrounding pixels.
+
+When used to run an experiment,
+:py:class:`bob.bio.vein.preprocessor.WatershedMask` requires you provide a
+*pre-trained* neural network model that presets the markers before
+watershedding takes place. In order to create one, you can run the program
+`markdet.py`:
+
+.. code-block:: sh
+
+ $ markdet.py --hidden=20 --samples=500 fv3d central dev
+
+You input, as arguments to this application, the database, protocol and subset
+name you wish to use for training the network. The data is loaded observing a
+total maximum number of samples from the dataset (passed with ``--samples=N``),
+the network is trained and recorded into an HDF5 file (by default, the file is
+called ``model.hdf5``, but the name can be changed with the option
+``--model=``). Once you have a model, you can use the preprocessor mask by
+constructing an object and attaching it to the
+:py:class:`bob.bio.vein.preprocessor.Preprocessor` entry on your configuration.
+
+
Region of Interest Goodness of Fit
==================================
@@ -358,13 +395,17 @@ mask
can use the option ``-n 5`` to print the 5 worst cases according to each of the
metrics.
+
+Pipeline Display
+================
+
You can use the program ``view_sample.py`` to display the images after
full processing using:
.. code-block:: sh
- $ ./bin/view_sample.py /path/to/verafinger/database /path/to/bob-bio-vein/output/for/toolchain 030-M/030_L_1 -s output-directory
- $ # open output-directory
+ $ ./bin/view_sample.py --save=output-dir verafinger /path/to/processed/directory 030-M/030_L_1
+ $ # open output-dir
And you should be able to view images like these (example taken from the Vera
fingervein database, using the automatic annotator and Maximum Curvature
diff --git a/doc/links.rst b/doc/links.rst
index 30721102cc8aaf4896ae8725614b2f244a45be26..096cf373bf6f373c9092e81f46590b91d06c277f 100644
--- a/doc/links.rst
+++ b/doc/links.rst
@@ -20,3 +20,4 @@
.. _mailing list: https://www.idiap.ch/software/bob/discuss
.. _bob.bio.base: https://pypi.python.org/pypi/bob.bio.base
.. _jaccard index: https://en.wikipedia.org/wiki/Jaccard_index
+.. _watershed: