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2015

Pixel-wise Deep Learning for Contour Detection

Hwang, Jyh-Jing, Liu, Tyng-Luh

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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.

Pixel-wise Deep Learning for Contour Detection · Around

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A computational approach to edge detection
Canny, John · 1986
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Original
Martin, David, Fowlkes, Charless, Tal, Doron, and Malik, Jitendra · 2001
Earlier work this paper cites.

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Arbelaez, Pablo, Maire, Michael, Fowlkes, Charless, and Malik, Jitendra · 2011
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Then

Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Later among the works it cites.
Densenet: Implementing efficient convnet descriptor pyramids
Original
Iandola, Forrest, Moskewicz, Matt, Karayev, Sergey, Girshick, Ross, Darrell, Trevor, and Keutzer, Kurt · 2014
Later among the works it cites.

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