Image edge detection based on Sparse Autoencoder network

Yingwei Liu; Xiaorong Gao; Jinlong Li

doi:10.1117/12.2505873

15 November 2018 Image edge detection based on Sparse Autoencoder network

Yingwei Liu, Xiaorong Gao, Jinlong Li

Proceedings Volume 10964, Tenth International Conference on Information Optics and Photonics; 1096432 (2018) https://doi.org/10.1117/12.2505873
Event: Tenth International Conference on Information Optics and Photonics (CIOP 2018), 2018, Beijing, China

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.

Citation Download Citation

Yingwei Liu, Xiaorong Gao, and Jinlong Li "Image edge detection based on Sparse Autoencoder network", Proc. SPIE 10964, Tenth International Conference on Information Optics and Photonics, 1096432 (15 November 2018); https://doi.org/10.1117/12.2505873

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