An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds

Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N2) algorithm with a total error of 0.17.