Adaptive weighted semantic edge detection of cultural relics

Xiang Li; Xiaohan Li; Shuang Li; Xiang Zou

doi:10.1117/12.2573899

12 June 2020 Adaptive weighted semantic edge detection of cultural relics

Xiang Li, Xiaohan Li, Shuang Li, Xiang Zou

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190G (2020) https://doi.org/10.1117/12.2573899
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Boundary and edge cues are very useful in improving various visual tasks, such as semantic segmentation, object recognition, stereo vision, and object generation. In recent years, the issue of edge detection has been revisited, and deep learning has made significant progress. The traditional edge detection is a challenging two-category problem, and the Multi-category semantic edge detection is a more challenging problem. And we model the edge detection of cultural relics and classify the pixels of cultural relics. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet. Then, we proposed an adaptive class weighter for this problem to supervise the training. The results show that the proposed architecture is superior to the existing semantic edge detection methods in our own design of cultural relic edge detection performance.

Citation Download Citation

Xiang Li, Xiaohan Li, Shuang Li, and Xiang Zou "Adaptive weighted semantic edge detection of cultural relics", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190G (12 June 2020); https://doi.org/10.1117/12.2573899

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KEYWORDS

Edge detection

Classification systems

Image segmentation

Feature extraction

Convolution

Network architectures

Computer programming

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