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          <h1 id="project_title">Open Image Analysis Group</h1>
          <h2 id="project_tagline">Image analysis, Machine learning, Statistics, Visualization, Plant phenotyping</h2>

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        <h3>
<a id="welcome-to-the-open-image-analysis-group" class="anchor" href="#welcome-to-the-open-image-analysis-group" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Welcome to the Open Image Analysis Group</h3>
Collaboration of experts for image-based plant phenotyping, machine learning and statistical data post-processing :-)

<h2>
<a id="members" class="anchor" href="#members" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Members</h2>

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<li>Dr. Christian Klukas (LemnaTec)</li>
<li>Jean-Michel Pape (IPK Gatersleben)</li>
<li>Dijun Chen (University Potsdam)</li>
</ul>

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<a id="projects" class="anchor" href="#projects" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Projects</h2>

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<a id="iap---integrated-analysis-platform" class="anchor" href="#iap---integrated-analysis-platform" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>IAP - Integrated Analysis Platform</h3>

<p>The Integrated Analysis Platform (IAP) has been designed and developed to support the analysis of large-scale image data sets of different camera systems. It aims in bridging different data domains and in integrating different approaches to data analysis and post-processing.</p>

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<li><a href="http://openimageanalysisgroup.github.io/IAP">IAP</a></li>
</ul>

<h3>
<a id="mcccs---multi-channel-classification-and-clustering-system" class="anchor" href="#mcccs---multi-channel-classification-and-clustering-system" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>MCCCS - Multi Channel Classification and Clustering System</h3>

<p>It is a generalized, script-based classification system for processing various kinds of image data. Due to the modular design, individual processing-components can be easily adapted, extended or exchanged by other external commands. </p>

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<li><a href="http://openimageanalysisgroup.github.io/MCCCS">MCCCS</a></li>
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<h3>
<a id="iccs---image-color-correction-system" class="anchor" href="#iccs---image-color-correction-system" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>ICCS - Image Color Correction System</h3>

<p>ICCS provides tools and methods for image color correction based on a color chart. This includes the color chart detection and several methods for image color correation using machine learning approaches.</p>

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<li><a href="http://openimageanalysisgroup.github.io/ICCS">ICCS</a></li>
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<a id="publications" class="anchor" href="#publications aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Publications
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  	<a href="http://www.plantphysiol.org/content/165/2/506.abstract" target="_blank" style="text-decoration: none"><img src="../images/plant_phys.gif" width=75 border="none" /></a>
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  	C. Klukas, J.-M. Pape, D. Chen: <a href="http://www.plantphysiol.org/content/165/2/506.abstract" target="_blank" style="color:#186c21; text-decoration: none;"><b>Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping</b></a>. Plant Physiology, 165 (2014)
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  	<a href="http://link.springer.com/chapter/10.1007/978-3-319-16220-1_5 " target="_blank"  style="text-decoration: none"><img src="../images/eccv.png" width=75 border="none" /></a>		
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  	J.-M. Pape, C. Klukas: <a href="http://link.springer.com/chapter/10.1007/978-3-319-16220-1_5 " target="_blank" style="color:#186c21; text-decoration: none"><b>
  	3-D histogram-based segmentation and leaf detection for rosette plants.     </b></a>In: Fleet D, Pajdla T, Schiele B, Tuytelaars T 
  (Eds.): Computer Vision - ECCV 2014 Workshops: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, proceedings, part IV (Series: Lecture Notes in Computer Science) Cham:
      Springer (2015)
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  	<a href="https://dx.doi.org/10.5244/C.29.CVPPP.3" target="_blank"  style="text-decoration: none"><img src="../images/logo_bmvc.png" width=75 border="none" /></a>		
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  	<td style="padding: 15px; width:700px;">
  	J.-M. Pape, C. Klukas: <a href="https://dx.doi.org/10.5244/C.29.CVPPP.3" target="_blank" style="color:#186c21; text-decoration: none"><b>
  	Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images.</b></a> Workshop: Computer Vision Problems in Plant Phenotyping (CVPPP) at BMVC (Brith Machine Vision Conference), BMVC Press (2015)
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