Pixel-wise Deep Learning for Contour Detection

By Jyh-Jing Hwang, Tyng-Luh Liu. 2015

110 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

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Citations in the corpus

InstanceCut: From Edges to Instances with MultiCut (2016, methodology): “We employ two techniques to overcome this problem of training with unbalanced data: a balanced loss function [51, 25] and pruning of the ground truth data.”
EDTER: Edge Detection with Transformer (2022, methodology): “DeepEdge [3] CVPR’15 0.753 0.772 0.807 CSCNN [24] ArXiv’15 0.756 0.775 0.798 MSC [51] PAMI’15 0.756 0.776 0.787 DeepContour [48] CVPR’15 0.757 0.776 0.800 HFL [4] ICCV’15 0.767 0.788 0.795 HED [65] ICCV’15 0.788 0.808 0.840 Deep Boundary [29] ICLR’15 0.813 0.831 0.866 CEDN [67] CVPR’16 0.788 0.804 -”
Holistically-Nested Edge Detection (2015, methodology): “Recent CNN-based methods [9, 32, 2, 17] have demonstrated promising F-score performance improvements over SE.”
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform (2015, background): “This allows us to use the DT as a layer in a CNN, thereby allowing us to jointly learn DCNNs that compute the coarse segmentation score maps in x and the reference edge map in g.”
Pushing the Boundaries of Boundary Detection using Deep Learning (2015, background): “Method ODS OIS AP gPb-owt-ucm (Arbelaez et al., 2011) 0.726 0.757 0.696 SE-Var (Doll´ar & Zitnick, 2015) 0.746 0.767 0.803 DeepNets (Kivinen et al., 2014) 0.738 0.759 0.758 N4-Fields (Ganin & Lempitsky, 2014) 0.753 0.769 0.784 DeepEdge (Bertasius et al., 2015) 0.753 0.772 0.807 CSCNN (Hwang & Liu, 2”
PixelNet: Towards a General Pixel-level Architecture (2016, unknown): “….640 .580 Felz-Hutt [23] .610 .640 .560 gPb-owt-ucm [2] .726 .757 .696 Sketch Tokens [44] .727 .746 .780 SCG [69] .739 .758 .773 PMI [37] .740 .770 .780 SE-Var [17] .746 .767 .803 OEF [30] .749 .772 .817 DeepNets [39] .738 .759 .758 CSCNN [34] .756 .775 .798 HED [71] .782 .804 .833 HED [71] not.”
Richer Convolutional Features for Edge Detection (2016, methodology): “Some well-known CNN methods have pushed forward this ﬁeld signiﬁcantly, such as DeepEdge [4], N 4 - Fields [17], CSCNN [23], DeepContour [40], and HED [50].”