Application of Improved BP Neural Network in Threshold Selection for Image Processing

Abstract:

There are a lot of methods to select threshold in image processing. Because BP neural network can adapt to fixed environment, it is applied in this area in this paper. Firstly, according to the feature of image, BP neural network is constructed. The input items of network are the features of image. The mean value and variance of gray in the image is the important features of image, so two input items can be chosen. The output items are the values of threshold. If one threshold is chosen, one output item can be chosen. In some condition, two thresholds should be set, then two output items would be chosen. In order to overcome the shortcomings of BP neural network, it should be improved by certain momentum which is used to avoid local minimums. In order to speed the training of the network, adaptive learning rate should be used, too. The BP neural network establishes the relationship between the features of image and the threshold. After training, the network can select suitable threshold for images in fixed environment. Some practical images are used to prove its good effect.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 2872-2875
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.2872
• Online since: December 2013
• Authors: Yang Ming He*, Jian Qiang Du
• Keywords: Back Propagation, Color Image, Neural Network (NN), Threshold Selection

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