A Natural Scene Edge Detection Algorithm Based on Image Fusion
Pages 70 - 74
Abstract
Convolutional neural network (CNN) has been widely used in the edge detection areas and shown competitive results. However, with the increase of receptive fields, the convolution features in CNN gradually become rough and difficult to figure out. To tackle with the problem, a novel network is proposed in this paper, making full use of the multi-scale and multi-level information of the object to perform image-to-image prediction, and combining all distinctive convolution features in a holistic manner. Further, the effect of simply connecting the feature map is enhanced by an image fusion algorithm to improve the utilization of features. The feature maps obtained by convolutions of each layer are fused through the fusion network to obtain a more detailed feature. The improved algorithm is validated in the BSDS500 dataset and the ODS F-measure has reached 0.818, which significantly exceeds the current state-of-the-art results.
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Index Terms
- A Natural Scene Edge Detection Algorithm Based on Image Fusion
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Published In
December 2018
252 pages
ISBN:9781450366137
DOI:10.1145/3301506
Copyright © 2018 ACM.
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In-Cooperation
- Kyoto University: Kyoto University
- TU: Tianjin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 December 2018
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- Refereed limited
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ICVIP 2018
ICVIP 2018: 2018 the 2nd International Conference on Video and Image Processing
December 29 - 31, 2018
Hong Kong, Hong Kong
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