Tutorials

Welcome to CBICA’s C++ learning resource.

Here, we will be showcasing our seminar series "CPP for Image Processing and Machine Learning" including presentations and code examples.

There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (<a href="http://www.itk.org/">ITK</a> for medical imaging, <a href="http://opencv.org">OpenCV</a> for computer vision and machine learning, <a href="http://arma.sourceforge.net/">Armadillo</a> for linear algebra, <a href="http://image.diku.dk/shark/">Shark</a>, <a href="http://dlib.net">dlib</a> and <a href="http://www.shogun-toolbox.org/">Shogun</a> for machine learning and so on).

The documentation provided with these packages, though extensive, assume a certain level of experience with C++ (familiarity with syntax, mainly). Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB).

Here we will be focusing on how someone with a good theoretical background in image processing and machine learning can quickly prototype algorithms using CPP and extend them to create meaningful software packages.

There will be more tutorials as and when we finalize the topics. Please contact us at <a href="mailto:tutorials@cbica.upenn.edu">tutorials[at]cbica[dot]upenn[dot]edu</a> for topic suggestions and questions.

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The University of Pennsylvania and the Center for Biomedical Image Computing and Analytics assume no responsibility for the code provided in these tutorials. The user is free to use and distribute the code as they see fit as long as they cite the relevant source(s).