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2019 | OriginalPaper | Buchkapitel

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

verfasst von : Zihao Dong, Ruixun Zhang, Xiuli Shao

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

Verlag: Springer International Publishing

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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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Titel: Learning Deep Structured Multi-scale Features for Crisp and Object Occlusion Edge Detection
verfasst von: Zihao Dong
Ruixun Zhang
Xiuli Shao
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing
Print ISBN: 978-3-030-30507-9

Electronic ISBN: 978-3-030-30508-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-30508-6_21

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