  In this paper, we present a novel approach for contour detection with
Convolutional Neural Networks. A multi-scale CNN learning framework is designed
to automatically learn the most relevant features for contour patch detection.
Our method uses patch-level measurements to create contour maps with
overlapping patches. We show the proposed CNN is able to to detect large-scale
contours in an image efficienly. We further propose a guided filtering method
to refine the contour maps produced from large-scale contours. Experimental
results on the major contour benchmark databases demonstrate the effectiveness
of the proposed technique. We show our method can achieve good detection of
both fine-scale and large-scale contours.
