An Adaptive Binarization Method Based on Deblocking PCNN

Liejun  Wang; Song  Zhang; Yanqing  Qi

doi:10.6180/jase.2017.20.4.12

An Adaptive Binarization Method Based on Deblocking PCNN

Computer Science and Information Engineering

Liejun Wang¹, Song Zhang This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Yanqing Qi¹

¹College of Information Science and Engineering, Xinjiang University, Urumqi, P.R. China

Received: June 20, 2016
Accepted: September 5, 2017
Publication Date: December 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.4.12

ABSTRACT

In recent years, the pulse coupled neural network (PCNN) is widely used in image segmentation, but the existing algorithm has an unsatisfactory performance with the definition of single threshold, especially in uneven illumination or low contrast image. In this paper, in order to eliminate the impact of the illumination and improve the adaptability, deblocking PCNN algorithm is utilized. It segments the image into several rectangles with the same size. Then PCNN algorithm is applied to segment each block and stopped by improved OSTU. The emulation experiment shows that this method is better than traditional image segmentation method in uneven illumination or low contrast image.

Keywords: PCNN, OSTU, Image Segmentation

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