Abstract
The key challenge for edge detection is that salient edge is difficult to detect due to the complex background. To improve the resolution and accuracy of salient edge effectively, we propose a novel method of edge detection called MSDF (Multi Scale Decode and Fusion) based on deep structured multi-scale features in this paper. The decoding layer of MSDF can fuse the adjacent features of the DNN multi-scale and increase the correlation between the features. In the fusion of different scale’s information, the traditional method of up-sample based on deconvolution is not used and Subpixel [16] algorithm is adopted to improve the resolution of the convolution layer’s output image. We also build a new Conditional Random Fields (CRF) model with CRF-RNN layer to reduce the number of irrelevant features and eliminate the weak correlation information while retaining the important structural attributes. Extensive experiments on BSDS500 [1] dataset and the larger NYUD [17] dataset show that the effectiveness of the proposed model and of the overall hierarchical framework.
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Dong, Z., Zhang, R., Shao, X., Li, H., Yang, Z. (2018). Multi-scale Feature Decode and Fuse Model with CRF Layer for Boundary Detection. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_3
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