Open-close by reconstruction on CNNUM

Author

Qingli, Zhang ; Zhaoyang, Zhang

Author_Institution

Lab. of Image Process. & Pattern Recognition, Shanghai Univ., China

fYear

2005

fDate

28-30 May 2005

Firstpage

69

Lastpage

72

Abstract

Open-close by reconstruction is one of the most important algorithms in mathematical morphology. It is used widely in image and video processing, but it requires the huge computational power, what is the bottleneck for application. So in this paper, cellular neural network (CNN) is used to solve the problem, new +1 template and "and" template are designed here. Along with the already developed templates and image preprocessing technique, the gray-scale open-close by reconstruction is realized. Experimental results based on CNN simulator are shown, proved that the speed on CNN is about 300 times faster than in traditional PC, the real time processing can be realized.

Keywords

cellular neural nets; image processing; mathematical morphology; +1 template; and template; cellular neural network; mathematical morphology; open-close by reconstruction; Cellular neural networks; Digital signal processing; Filters; Gray-scale; Image processing; Image reconstruction; Image segmentation; Laboratories; Morphology; Signal processing algorithms; Cellular Neural Network (CNN); Mathematical morphology; morphological filters by reconstruction; open-close by reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and Their Applications, 2005 9th International Workshop on

Print_ISBN

0-7803-9185-3

Type

conf

DOI

10.1109/CNNA.2005.1543163

Filename

1543163