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Learning Deep Structured Multi-scale Features for Crisp and Object Occlusion Edge Detection

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Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11729))

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Abstract

The key challenge for edge detection is that predicted edges are thick and need Non-Maximum Suppression to post-process to obtain crisp edges. In addition, object occlusion edge detection is an important research problem in computer vision. To increase the crispness and accuracy of occlusion relationships effectively, we propose a novel method of edge detection called MSDF (Multi Scale Decode and Fusion) based on deep structured multi-scale features to generate crisp salient edges in this paper. The decoder layer of MSDF can fuse the adjacent-scale features and increase the affinity between the features. We also propose a novel loss function to solve the class imbalance in object occlusion edge detection and a two streams learning framework to predict edge and occlusion orientation. Extensive experiments on BSDS500 dataset and the larger NYUD dataset show that the effectiveness of the proposed model and of the overall hierarchical framework. We also surpass the state of the art on the BSDS ownership dataset in occlusion edge detection.

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Authors and Affiliations

  1. College of Computer Science, Nankai University, Tian Jin, China

    Zihao Dong & Xiuli Shao

  2. MIT Laboratory for Financial Engineering, Cambridge, MA, USA

    Ruixun Zhang

Authors
  1. Zihao Dong
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  2. Ruixun Zhang
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  3. Xiuli Shao
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Corresponding author

Correspondence to Xiuli Shao .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Praha 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Dong, Z., Zhang, R., Shao, X. (2019). Learning Deep Structured Multi-scale Features for Crisp and Object Occlusion Edge Detection. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_21

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  • DOI: https://doi.org/10.1007/978-3-030-30508-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30507-9

  • Online ISBN: 978-3-030-30508-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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