Tan and Triggs preprocessing

Forked from tpereira/tan_triggs/4

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.
This algorithm is splittable

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: main

Endpoint Name Data Format Nature
image_gray system/array_2d_floats/1 Input
tantriggs_image system/array_2d_floats/1 Output

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
gamma float64 0.2
threshold float64 10.0
alpha float64 0.1
sigma1 float64 2.0
sigma0 float64 1.0
kernel_size int64 5

The code for this algorithm in Python
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This algorithm is a multi-stage image preprocessor relying on the `Bob <http://www.idiap.ch/software/bob/>`_ library. It implements the multi-stage preprocessing method described in [Tan07], the steps being a gamma correction, a difference of Gaussian filtering and a contrast equalization

The difference between this algorithm with the one published here (https://www.beat-eu.org/platform/algorithms/tpereira/tan_triggs/4/) is that for this one, the input is system/array_2d_floats/1

Both inputs and outputs are expected to be grayscale images as two-dimensional arrays of floats (64 bits).

[Tan07]
  1. Tan, B. Triggs: Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. Analysis and Modeling of Faces and Gestures (2007) 168-182

Docutils System Messages

System Message: ERROR/3 (<string>, line 1); backlink

Unknown target name: "bob &lt;http://www.idiap.ch/software/bob/&gt;".

Experiments

Updated Name Databases/Protocols Analyzers
smarcel/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_mobio-female_det_bobv2-0 mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_cpqd-smartphone-male_det cpqd/1@smartphone_male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_cpqd-smartphone-male_det cpqd/1@smartphone_male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_cpqd-smartphone-male_det cpqd/1@smartphone_male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_cpqd-smartphone-female_det cpqd/1@smartphone_female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_cpqd-smartphone-female_det cpqd/1@smartphone_female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_cpqd-smartphone-female_det cpqd/1@smartphone_female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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