Abstract
Edge detection plays an important role in image processing. With the development of deep learning, the accuracy of edge detection has been greatly improved, and people have more and more requirements for edge detection tasks. Most edge detection algorithms are binary edge detection methods, but there are usually multiple categories of edges in an image. In this paper, we present an accurate multi-category edge detection network Richer-CASENet (R-CASENet). In order to make full use of CNN’s powerful feature expression capabilities, we attempt to use more information from feature map for edge feature extraction and classification. Using the ResNet101 network as the backbone, firstly we merge the building blocks in different composite blocks and down-sample to obtain the feature map. Then we fuse the feature maps in different composite blocks to obtain the final fused classifier. Experiments show that we achieved better results on a public dataset.
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References
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)
Bertasius, G., Shi, J., Torresani, L.: DeepEdge: a multi-scale bifurcated deep network for top-down contour detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4380–4389. IEEE (2015)
Bertasius, G., Shi, J., Torresani, L.: High-for-low and low-for-high: efficient boundary detection from deep object features and its applications to high-level vision. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 504–512 (2015)
Canny, J.: A computational approach to edge detection. In: Readings in Computer Vision, pp. 184–203. Elsevier (1987)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Dollar, P., Tu, Z., Belongie, S.: Supervised learning of edges and object boundaries. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1964–1971. IEEE (2006)
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The Pascal visual object classes challenge 2012 (voc2012) results (2012). http://www.pascal-network.org/challenges/VOC/voc2011/workshop/index.html (2011)
Ganin, Y., Lempitsky, V.: \(N^4\)-fields: neural network nearest neighbor fields for image transforms. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 536–551. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16808-1_36
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 991–998. IEEE (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kittler, J.: On the accuracy of the sobel edge detector. Image Vis. Comput. 1(1), 37–42 (1983)
Konishi, S., Yuille, A.L., Coughlan, J.M., Zhu, S.C.: Statistical edge detection: learning and evaluating edge cues. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 57–74 (2003)
Lim, J.J., Zitnick, C.L., Dollár, P.: Sketch Tokens: a learned mid-level representation for contour and object detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3158–3165. IEEE (2013)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, Y., Cheng, M.M., Hu, X., Wang, K., Bai, X.: Richer convolutional features for edge detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5872–5881. IEEE (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)
Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: DeepContour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3982–3991 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)
Yu, Z., Feng, C., Liu, M.Y., Ramalingam, S.: CASENet: deep category-aware semantic edge detection. ArXiv e-prints (2017)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)
Acknowledgements
We gratefully acknowledge the NVIDIA Corporation with the donation of the Tesla K40 GPU for this research. The work is partially supported by the Natural Science Foundation for China (NSFC) (No. 61772296) and Shenzhen fundamental research fund (Grant Nos. JCYJ20160531194840025 and JCYJ20170412170438636).
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Shen, Y., Liu, H., Guo, Z. (2018). R-CASENet: A Multi-category Edge Detection Network. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_34
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DOI: https://doi.org/10.1007/978-3-030-02698-1_34
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