Abstract
In order to achieve vision detection for tiny bran specks in flour, this paper proposes a new detection method based on pulse coupled neural network (PCNN). First, the flour image is mapped into gray entropy image using local gray entropy transformation, so the location of bran specks in flour can be enhanced in image. Then, the PCNN is utilized for the gray entropy image, and final target segmentation can be completed after iterative processing, while the optimal iteration number is determined according to the minimum cross entropy. The compared experimental results not only demonstrate the effectiveness but also show that the proposed method has a higher detection sensitivity.
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Acknowledgments
This work was partly supported by Science and Technology Research Project of The Education Department Henan Province (No. 14B413001) and High level talents fund of Henan University of Technology (No. 2014BS008).
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Chen, T., Wu, X., Li, X. (2015). A Bran Specks Detection Method Based on PCNN. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_48
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DOI: https://doi.org/10.1007/978-3-662-46469-4_48
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