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DOC: Update to https links
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LICENSE

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Apache License
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Version 2.0, January 2004
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http://www.apache.org/licenses/
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https://www.apache.org/licenses/
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TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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https://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,

README.md

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Structure Preserving Color Normalization is a technique described in [Vahadane et al., 2016](https://doi.org/10.1109/TMI.2016.2529665) and modified in [Ramakrishnan et al., 2019](https://arxiv.org/abs/1901.03088). The idea is to model the color of an image pixel as something close to pure white, which is reduced in intensity in a color-specific way via an optical absorption model that depends upon the amounts of hematoxylin and eosin that are present. [Non-negative matrix factorization](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization) is used on each analyzed image to simultaneously derive the amount of hematoxylin and eosin stain at each pixel and the image-wide effective colors of each stain.
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The implementation in ITK accelerates the non-negative matrix factorization by choosing the initial estimate for the color absorption characteristics using a technique mimicking that presented in [Arora et al., 2013](http://proceedings.mlr.press/v28/arora13.html) and modified in [Newberg et al., 2018](https://doi.org/10.1371/journal.pone.0193067). This approach finds a good solution for a non-negative matrix factorization by first transforming it to the problem of finding a convex hull for a set of points in a cloud.
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The implementation in ITK accelerates the non-negative matrix factorization by choosing the initial estimate for the color absorption characteristics using a technique mimicking that presented in [Arora et al., 2013](https://proceedings.mlr.press/v28/arora13.html) and modified in [Newberg et al., 2018](https://doi.org/10.1371/journal.pone.0193067). This approach finds a good solution for a non-negative matrix factorization by first transforming it to the problem of finding a convex hull for a set of points in a cloud.
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The lead developer of InsightSoftwareConsortium/ITKColorNormalization is [Lee Newberg](https://github.com/Leengit/).
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include/itkStructurePreservingColorNormalizationFilter.h

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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0.txt
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* https://www.apache.org/licenses/LICENSE-2.0.txt
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,

include/itkStructurePreservingColorNormalizationFilter.hxx

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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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* http://www.apache.org/licenses/LICENSE-2.0.txt
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* https://www.apache.org/licenses/LICENSE-2.0.txt
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,

test/itkStructurePreservingColorNormalizationFilterTest.cxx

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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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* http://www.apache.org/licenses/LICENSE-2.0.txt
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* https://www.apache.org/licenses/LICENSE-2.0.txt
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,

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