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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU129526" target="_blank" >RIV/00216305:26220/18:PU129526 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.3964/j.issn.1000-0593(2018)11-3645-08" target="_blank" >http://dx.doi.org/10.3964/j.issn.1000-0593(2018)11-3645-08</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3964/j.issn.1000-0593(2018)11-3645-08" target="_blank" >10.3964/j.issn.1000-0593(2018)11-3645-08</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SPECTROSCOPY AND SPECTRAL ANALYSIS

ISSN

1000-0593

e-ISSN

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Volume of the periodical

38

Issue of the periodical within the volume

11

Country of publishing house

CN - CHINA

Number of pages

5

Pages from-to

1-5

UT code for WoS article

000452247200052

EID of the result in the Scopus database

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