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.
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