work by Piotr Dollar and C. Lawrence Zitnick, published in PAMI 2015
presented by Vojtěch Cvrček
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.
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