Publication detail

Infrared image segmentation using growing immune field and clone threshold

YU, X. ZHOU, Z. GAO, Q. LI, D. ŘÍHA, K.

Original Title

Infrared image segmentation using growing immune field and clone threshold

English Title

Infrared image segmentation using growing immune field and clone threshold

Type

journal article

Language

en

Original Abstract

Fast and accurate segmentation of infrared target is the basis of automatic target recognition, but there is a problem that it is easy to appear the significant differences of target areas in segmentation. In order to solve this problem, in this paper a new method based on growing immune field and clone threshold for segmentation of infrared targets is introduced. First, according to the global gray information, obtain the best threshold of the image using the clonal selection algorithm for global threshold segmentation. And the seed region is selected based on global threshold segmentation. Second, the source seeds are obtained by comparing the similarity threshold with seed region. Third, the growing immune field is adjusted automatically for region growing through the source seeds. Finally, the segmented image is obtained by immune region growing. The simulation results show that the target information gained by the proposed method is complete and exact. This resultgreatly facilitates the target recognition.

English abstract

Fast and accurate segmentation of infrared target is the basis of automatic target recognition, but there is a problem that it is easy to appear the significant differences of target areas in segmentation. In order to solve this problem, in this paper a new method based on growing immune field and clone threshold for segmentation of infrared targets is introduced. First, according to the global gray information, obtain the best threshold of the image using the clonal selection algorithm for global threshold segmentation. And the seed region is selected based on global threshold segmentation. Second, the source seeds are obtained by comparing the similarity threshold with seed region. Third, the growing immune field is adjusted automatically for region growing through the source seeds. Finally, the segmented image is obtained by immune region growing. The simulation results show that the target information gained by the proposed method is complete and exact. This resultgreatly facilitates the target recognition.

Keywords

Infrared image Clonal selection algorithm Region growing Growing immune field

Released

09.01.2018

Publisher

Elsevier

Pages from

184

Pages to

193

Pages count

10

BibTex


@article{BUT148564,
  author="Xiao {Yu} and Zijie {Zhou} and Qiang {Gao} and Dahua {Li} and Kamil {Říha}",
  title="Infrared image segmentation using growing immune field and clone threshold",
  annote="Fast and accurate segmentation of infrared target is the basis of automatic target recognition, but there is
a problem that it is easy to appear the significant differences of target areas in segmentation. In order to
solve this problem, in this paper a new method based on growing immune field and clone threshold for
segmentation of infrared targets is introduced. First, according to the global gray information, obtain the
best threshold of the image using the clonal selection algorithm for global threshold segmentation. And
the seed region is selected based on global threshold segmentation. Second, the source seeds are obtained
by comparing the similarity threshold with seed region. Third, the growing immune field is adjusted
automatically for region growing through the source seeds. Finally, the segmented image is obtained
by immune region growing. The simulation results show that the target information gained by the proposed
method is complete and exact. This resultgreatly facilitates the target recognition.",
  address="Elsevier",
  chapter="148564",
  doi="10.1016/j.infrared.2017.11.029",
  howpublished="online",
  institution="Elsevier",
  number="2018",
  volume="88",
  year="2018",
  month="january",
  pages="184--193",
  publisher="Elsevier",
  type="journal article"
}