Joint object boundary and skeleton detection using convolutional neural networks
CovenanteeUniversity of Toronto
Document typeBachelor thesis
Rights accessOpen Access
While the duality between boundary and medial representations has been exploited in the context of pre-segmented shapes, it has not been studied in the context of natural scenes. The goal of this project is to use a shared feature representation to address edge and skeleton detection simultaneously, improving performance for both tasks. To compare the relative benefits of a joint approach, we plan to use a single convolutional neural network (CNN) for both tasks, combined with a novel loss function that enforces consistency between detected boundary and medial points.