Soft shape context for iterative closest point registration
This paper introduces a shape descriptor, the soft shape context, motivated by the shape context method. Unlike the original shape context method, where each image point was hard assigned into a single histogram bin, we instead allow each image point to contribute to multiple bins, hence more robust to distortions. The soft shape context can easily be integrated into the iterative closest point (ICP) method as an auxiliary feature vector, enriching the representation of an image point from spatial information only, to spatial and shape information. This yields a registration method more robust than the original ICP method. The method is general for 2D shapes. It does not calculate derivatives, hence being able to handle shapes with junctions and discontinuities. We present experimental results to demonstrate the robustness compared with the standard ICP method.