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Target extraction of banded blurred infrared images by immune dynamical algorithm with two-dimensional minimum distance immune field

机译：二维最小距离免疫场的免疫动力学算法对带状模糊红外图像进行目标提取

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摘要

Banded blurred Infrared image segmentation is a challenging topic since banded blurred infrared images are characterized by high noise, low contrast, and weak edges. Based on the interconnected and networked collaborative mechanism between innate immune factors and adaptive immune factors, this paper presents an immune dynamical algorithm with two-dimensional minimum distance immune field to solve this puzzle. Firstly, using the original characteristics as antigen surface molecular patterns, innate immune factors in the first layer of immune dynamical network extract banded blurred regions from the whole banded blurred infrared image region. Secondly, innate immune factors in the second layer of immune dynamical network extract new characteristics to design the complex of major histocompatibility complex (MHC) and antigen peptide. Lastly, adaptive immune factors in the last layer will extract object and background antigens from all the banded blurred image antigens, and design the optimal immune field. of every adaptive immune factors. Experimental results on hand trace infrared images verified that the proposed algorithm could efficiently extract targets from images, and produce better extraction accuracy. (C) 2016 Elsevier B.V. All rights reserved.
机译：带状模糊的红外图像分割是一个具有挑战性的主题，因为带状模糊的红外图像具有高噪声，低对比度和弱边缘的特点。基于先天免疫因子与适应性免疫因子之间的互联互通协作机制，提出了一种具有二维最小距离免疫场的免疫动力学算法来解决这一难题。首先，利用原始特征作为抗原表面分子模式，免疫动力学网络第一层的先天免疫因子从整个带状模糊红外图像区域中提取带状模糊区域。其次，免疫动力学网络第二层的先天免疫因子提取了新的特征，以设计主要组织相容性复合体（MHC）和抗原肽的复合体。最后，最后一层的适应性免疫因子将从所有带状模糊图像抗原中提取目标抗原和背景抗原，并设计最佳免疫场。每个适应性免疫因子。在手迹红外图像上的实验结果证明，该算法可以有效地从图像中提取目标，并具有较好的提取精度。 （C）2016 Elsevier B.V.保留所有权利。

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