Improved Morphological TOP-HAT Filter Optimized with Genetic Algorithm

Author

Wang, Jiangang ; Gao, Deyuan

Author_Institution

Coll. of Comput., Northwestern Poly Tech. Univ., Xi´´an, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

5

Abstract

The Top-Hat morphological filters are a class of nonlinear signal processing algorithms, which have been applied extensively to computer vision, image processing, and more recently target detection. In this paper a novel method for optimal learning of morphological filtering parameters for spot target detection is presented. We show how the genetic algorithms can be used for an automatic optimization of structuring elements. Experimental results show that the identified probability to the image of SNR 2 can reach more than 98% by this method.

Keywords

genetic algorithms; image segmentation; infrared imaging; mathematical morphology; nonlinear filters; object detection; TOP-HAT morphological filter; automatic infrared image target detection; genetic algorithm; nonlinear signal processing algorithm; spot target detection; Bismuth; Computer vision; Filtering; Filters; Genetic algorithms; Image processing; Morphology; Object detection; Shape measurement; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5303727

Filename

5303727