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Object Contour and Edge Detection with RefineContourNet

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Computer Analysis of Images and Patterns (CAIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11678))

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Abstract

A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse high, mid and low-level features in that specific order, which differs from many other approaches. The tensor with the highest-levelled features is set as the starting point to combine it layer-by-layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL-val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500 dataset, we reach state-of-the-art results for edge-detection with an ODS of 0.824.

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References

  1. 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). https://doi.org/10.1109/TPAMI.2010.161

    Article  Google Scholar 

  2. Asghari, M.H., Jalali, B.: Physics-inspired image edge detection. In: 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 293–296, December 2014. https://doi.org/10.1109/GlobalSIP.2014.7032125

  3. 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, June 2015. https://doi.org/10.1109/CVPR.2015.7299067

  4. 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: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 504–512, December 2015. https://doi.org/10.1109/ICCV.2015.65

  5. Bertasius, G., Shi, J., Torresani, L.: Semantic segmentation with boundary neural fields. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3602–3610, June 2016. https://doi.org/10.1109/CVPR.2016.392

  6. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  7. Chen, L., Barron, J.T., Papandreou, G., Murphy, K., Yuille, A.L.: Semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4545–4554, June 2016. https://doi.org/10.1109/CVPR.2016.492

  8. Deng, R., Shen, C., Liu, S., Wang, H., Liu, X.: Learning to predict crisp boundaries. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 570–586. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_35

    Chapter  Google Scholar 

  9. Dollár, P.: Piotr’s Computer Vision Matlab Toolbox (PMT). https://github.com/pdollar/toolbox

  10. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html

  11. 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

    Chapter  Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016. https://doi.org/10.1109/CVPR.2016.90

  13. Khoreva, A., Benenson, R., Omran, M., Hein, M., Schiele, B.: Weakly supervised object boundaries. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 183–192, June 2016. https://doi.org/10.1109/CVPR.2016.27

  14. Kivinen, J., Williams, C., Heess, N.: Visual boundary prediction: a deep neural prediction network and quality dissection. In: Kaski, S., Corander, J. (eds.) Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, PMLR, Reykjavik, Iceland, 22–25 April 2014, vol. 33, pp. 512–521 (2014). http://proceedings.mlr.press/v33/kivinen14.html

  15. Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 24, pp. 109–117. Curran Associates, Inc. (2011). http://papers.nips.cc/paper/4296-efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials.pdf

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

  17. Lei, P., Li, F., Todorovic, S.: Boundary flow: a Siamese network that predicts boundary motion without training on motion. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3282–3290, June 2018. https://doi.org/10.1109/CVPR.2018.00346

  18. Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5168–5177, July 2017. https://doi.org/10.1109/CVPR.2017.549

  19. 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

    Chapter  Google Scholar 

  20. Liu, Y., et al.: Richer convolutional features for edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2018). https://doi.org/10.1109/TPAMI.2018.2878849

    Article  Google Scholar 

  21. Maninis, K., Pont-Tuset, J., Arbeláez, P., Gool, L.V.: Convolutional oriented boundaries: from image segmentation to high-level tasks. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 40(4), 819–833 (2017)

    Article  Google Scholar 

  22. Nazeri, K., Ng, E., Joseph, T., Qureshi, F.Z., Ebrahimi, M.: EdgeConnect: generative image inpainting with adversarial edge learning. CoRR abs/1901.00212 (2019). http://arxiv.org/abs/1901.00212

  23. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1520–1528, December 2015. https://doi.org/10.1109/ICCV.2015.178

  24. Pont-Tuset, J., Arbeláez, P., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping for image segmentation and object proposal generation. arXiv:1503.00848, March 2015

  25. Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: Edge-preserving interpolation of correspondences for optical flow. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1164–1172, June 2015. https://doi.org/10.1109/CVPR.2015.7298720

  26. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017). https://doi.org/10.1109/TPAMI.2016.2572683

    Article  Google Scholar 

  27. Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: DeepContour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3982–3991, June 2015. https://doi.org/10.1109/CVPR.2015.7299024

  28. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)

    Google Scholar 

  29. Sobel, I.: Camera models and machine perception. Camera models and machine perception. Technical report, Stanford University, California, Department of Computer Science (1970)

    Google Scholar 

  30. Vedaldi, A., Lenc, K.: MatconvNet - convolutional neural networks for MATLAB. In: Proceedings of the 23rd ACM International Conference on Multimedia (2015)

    Google Scholar 

  31. Wang, Y., Zhao, X., Li, Y., Huang, K.: Deep crisp boundaries: from boundaries to higher-level tasks. IEEE Trans. Image Process. 28(3), 1285–1298 (2019). https://doi.org/10.1109/TIP.2018.2874279

    Article  MathSciNet  MATH  Google Scholar 

  32. Xie, S., Tu, Z.: Holistically-nested edge detection. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1395–1403, December 2015. https://doi.org/10.1109/ICCV.2015.164

  33. Xu, S., Liu, D., Xiong, Z.: Edge-guided generative adversarial network for image inpainting. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4, December 2017. https://doi.org/10.1109/VCIP.2017.8305138

  34. Yang, J., Price, B., Cohen, S., Lee, H., Yang, M.: Object contour detection with a fully convolutional encoder-decoder network. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 193–202, June 2016. https://doi.org/10.1109/CVPR.2016.28

  35. Zhang, H., Jiang, K., Zhang, Y., Li, Q., Xia, C., Chen, X.: Discriminative feature learning for video semantic segmentation. In: 2014 International Conference on Virtual Reality and Visualization, pp. 321–326, August 2014. https://doi.org/10.1109/ICVRV.2014.65

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Authors and Affiliations

  1. Department of Signal Processing and Communications, Helmut Schmidt University, Holstenhofweg 85, 22043, Hamburg, Germany

    André Peter Kelm & Udo Zölzer

  2. Department of Mechatronics, Hamburg University of Technology, Am Schwarzenberg-Campus 1, 21073, Hamburg, Germany

    Vijesh Soorya Rao

Authors
  1. André Peter Kelm
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  2. Vijesh Soorya Rao
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  3. Udo Zölzer
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Corresponding author

Correspondence to André Peter Kelm .

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Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Gennaro Percannella

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Kelm, A.P., Rao, V.S., Zölzer, U. (2019). Object Contour and Edge Detection with RefineContourNet. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-29888-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29887-6

  • Online ISBN: 978-3-030-29888-3

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