  Edge detection is a critical component of many vision systems, including
object detectors and image segmentation algorithms. Patches of edges exhibit
well-known forms of local structure, such as straight lines or T-junctions. In
this paper we take advantage of the structure present in local image patches to
learn both an accurate and computationally efficient edge detector. We
formulate the problem of predicting local edge masks in a structured learning
framework applied to random decision forests. Our novel approach to learning
decision trees robustly maps the structured labels to a discrete space on which
standard information gain measures may be evaluated. The result is an approach
that obtains realtime performance that is orders of magnitude faster than many
competing state-of-the-art approaches, while also achieving state-of-the-art
edge detection results on the BSDS500 Segmentation dataset and NYU Depth
dataset. Finally, we show the potential of our approach as a general purpose
edge detector by showing our learned edge models generalize well across
datasets.
