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Proceedings Paper

Image edge detection based on Sparse Autoencoder network
Author(s): Yingwei Liu; Xiaorong Gao; Jinlong Li
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Paper Abstract

Edge detection plays an important role in image pattern recognition. Because of the shortcomings of poor anti-noise and spurious edges by using traditional edge detection methods. A method of image edge detection based on Sparse Autoencoder neural work is proposed in this paper. This method uses Berkeley Segmentation data set to extract the highdimensional edge features of sample data by training the sparse autoencoder. Through the ZCA (Zero-phase Component Analysis) whitening treatment, the correlation between images is effectively reduced. The standard edge images are input into a Softmax classifier to train a classifier that can classify the edge features of each pixel. Last, the extracted features of each pixel sample are input into the trained Softmax classifier to classify the edge pixels to achieve edge detection. Experiments show that the algorithm has good noise immunity and certain application value.

Paper Details

Date Published: 15 November 2018
PDF: 8 pages
Proc. SPIE 10964, Tenth International Conference on Information Optics and Photonics, 1096432 (15 November 2018); doi: 10.1117/12.2505873
Show Author Affiliations
Yingwei Liu, Southwest Jiaotong Univ. (China)
Xiaorong Gao, Southwest Jiaotong Univ. (China)
Jinlong Li, Southwest Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 10964:
Tenth International Conference on Information Optics and Photonics
Yidong Huang, Editor(s)

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