Learning Connectedness and Convexity of Binary Images from Their Projections

@inproceedings{Gara2010LearningCA,
  title={Learning Connectedness and Convexity of Binary Images from Their Projections},
  author={Mih{\'a}ly Gara and Tam{\'a}s S{\'a}muel Tasi and P{\'e}ter Bal{\'a}zs},
  year={2010}
}
In this paper we investigate the retrieval of geometrical information (especially, convexity and connectedness) of binary images from their projections which can be useful in binary tomography to facilitate the task of reconstruction. Supposing that the projections are the features of the images, we study how decision trees, neural networks, and nearest neighbor learning algorithms perform in classifying binary images with different connectedness and convexity properties. 

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