Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection

By Xavier Soria Poma, Edgar Riba, Angel Domingo Sappa. 2019

397 citations recorded by the source corpus.

This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Radiology, Nuclear Medicine and Imaging, Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Tao A. Xiang
2. Luc Van Gool
3. Xiaogang Wang
4. Dacheng Tao
5. Yi Yang
6. Ming-Hsuan Yang
7. Qi Tian
8. Jitendra Malik

In Media Technology

1. Li Liu
2. Timothy M. Hospedales
3. Jitendra Malik
4. Qi Tian
5. Tao A. Xiang
6. Matti Pietikäinen
7. Du Tran
8. Yi-Zhe Song

In Radiology, Nuclear Medicine and Imaging

1. Jitendra Malik
2. Qi Tian
3. Tao A. Xiang
4. Timothy M. Hospedales
5. Yi-Zhe Song
6. Haibin Ling
7. Zitong Yu
8. Dewen Hu

In Model Architectures

1. Du Tran
2. Matt Feiszli
3. Heng Wang
4. Weiyao Wang
5. Jitendra Malik
6. Qi Tian
7. Tao A. Xiang
8. Matti Pietikäinen

Citations in the corpus

• Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity (2022, methodology): “(d) Edge prediction of the image in (a) using off-the-shelf edge detector [49].”
• Pixel Difference Networks for Efficient Edge Detection (2021, background): “Some of them may not need a pretrained network based on large scale dataset [45].”
• RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth (2021, background): “As shown in Table 1, ten compared models are symbolized as HED, RCF, CED, DexiNed, BDCN, CASENet, DFF, *DeepLabV3+, *DOOBNet and *OFNet, respectively.”
• Deep Learning for Free-Hand Sketch: A Survey (2020, background): “(ii) Photo-to-sketch generation does not refer to extracting edge-map from natural photos [203], [283] (edge-maps of literal perspective projections), but instead needs models that learn to mimic human sketching and abstract drawing style [55].”