  We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM classifier to accomplish contour detection. In the experiment of
contour detection, we look into the effectiveness of combining per-pixel
features from different CNN layers and verify their performance on BSDS500.
