Introduction This project demonstrates the use of Graph Neural Networks (GNNs) to classify synthetic 2D shapes (triangles, rectangles, circles, hexagons, ellipses). It covers the full machine learning ...

A framework is presented which uses a polar representation of a segmented object for shape classification. This method produces a position, rotation and scale invariant representation of the shape. An ...