Publication detail

Blurred Infrared Image Segmentation Using New Immune Algorithm with Minimum Mean Distance Immune Field

YU, X. ZHOU, Z. ŘÍHA, K.

Original Title

Blurred Infrared Image Segmentation Using New Immune Algorithm with Minimum Mean Distance Immune Field

English Title

Blurred Infrared Image Segmentation Using New Immune Algorithm with Minimum Mean Distance Immune Field

Type

journal article

Language

en

Original Abstract

Criminals use various methods to avoid traditional forensic image technologies, so infrared image is becoming an effective means for obtaining crime scene traces. However, segmentation targets from infrared image shoot in crime scene is a challenging task as these images are target weakened infrared images. Previous studies about immune algorithms do not describe immune variation and immune recognition distance in the net-work and algorithm. In opposition to segment these target weakened traces infrared images, we propose a new immune framework with immune variation and minimum mean immune recognition distance, and construct a new immune segmentation algorithm with minimum mean distance immune field. According to the distinguishing feature of infrared images, this method use multi-step classification algorithm, immune variation and adaptive immune minimum mean distance recognition to achieve optimal classification based on the overall statistical properties of target areas and background areas. Experimental results show that the proposed immune algorithm with minimum mean distance can segment target weakened infrared images efficiently. Compared with classical edge template and conventional region template methods, the proposed algorithm has better segmentation results, especially the boundaries of five fingers.

English abstract

Criminals use various methods to avoid traditional forensic image technologies, so infrared image is becoming an effective means for obtaining crime scene traces. However, segmentation targets from infrared image shoot in crime scene is a challenging task as these images are target weakened infrared images. Previous studies about immune algorithms do not describe immune variation and immune recognition distance in the net-work and algorithm. In opposition to segment these target weakened traces infrared images, we propose a new immune framework with immune variation and minimum mean immune recognition distance, and construct a new immune segmentation algorithm with minimum mean distance immune field. According to the distinguishing feature of infrared images, this method use multi-step classification algorithm, immune variation and adaptive immune minimum mean distance recognition to achieve optimal classification based on the overall statistical properties of target areas and background areas. Experimental results show that the proposed immune algorithm with minimum mean distance can segment target weakened infrared images efficiently. Compared with classical edge template and conventional region template methods, the proposed algorithm has better segmentation results, especially the boundaries of five fingers.

Keywords

Blurred infrared image; Image segmentation; Immune field

Released

01.11.2018

Pages from

1

Pages to

5

Pages count

5

BibTex


@article{BUT150889,
  author="Xiao {Yu} and Zijie {Zhou} and Kamil {Říha}",
  title="Blurred Infrared Image Segmentation Using New Immune Algorithm with Minimum Mean Distance Immune Field",
  annote="Criminals use various methods to avoid traditional forensic image technologies, so infrared image is becoming an effective means for obtaining crime scene traces. However, segmentation targets from infrared image shoot in crime scene is a challenging task as these images are target weakened infrared images. Previous studies about immune algorithms do not describe immune variation and immune recognition distance in the net-work and algorithm. In opposition to segment these target weakened traces infrared images, we propose a new immune framework with immune variation and minimum mean immune recognition distance, and construct a new immune segmentation algorithm with minimum mean distance immune field. According to the distinguishing feature of infrared images, this method use multi-step classification algorithm, immune variation and adaptive immune minimum mean distance recognition to achieve optimal classification based on the overall statistical properties of target areas and background areas. Experimental results show that the proposed immune algorithm with minimum mean distance can segment target weakened infrared images efficiently. Compared with classical edge template and conventional region template methods, the proposed algorithm has better segmentation results, especially the boundaries of five fingers.",
  chapter="150889",
  doi="10.3964/j.issn.1000-0593(2018)11-00-08",
  howpublished="online",
  number="11",
  volume="38",
  year="2018",
  month="november",
  pages="1--5",
  type="journal article"
}