Title :
Image segmentation using dynamic mechanism based PCNN model
Author :
Qiao, Yuanhua ; Miao, Jun ; Duan, Lijuan ; Lu, Yunfeng
Author_Institution :
Coll. of Appl. Sci., Beijing Univ. of Technol., Beijing
Abstract :
Pulse-coupled neuron networks (PCNN) can be efficiently applied to image segmentation. However, the performance of segmentation depends on the suitable PCNN parameters, which are obtained by manual experiment, and the effect of the segmentation needs to be improved for images with noise. In this paper, dynamic mechanism based PCNN(DMPCNN) is brought forward to simulate the integrate-and-fire mechanism, and it is applied to segment images with noise effectively. Parameter selection is based on dynamic mechanism. Experimental results for image segmentation show its validity and robustness.
Keywords :
image segmentation; neural nets; image segmentation; integrate-and-fire mechanism; pulse-coupled neuron networks; Biomembranes; Capacitors; Cells (biology); Educational institutions; Image processing; Image segmentation; Mathematical model; Neural networks; Neurons; Noise robustness;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634094
