Infrared Image Extraction Algorithm Based on Adaptive Growth Immune Field

Abstract

In criminal investigation, there are hidden traces that many people can’t find, so infrared image is becoming an effective means to obtain these scene traces. The extraction algorithm with growth immune field can extract the target of infrared image relatively effectively, but it is lack of efficiency and reliability in complex environment. Here we propose a new target extraction algorithm with adaptive growth immune field, combining the image information of region and edge gradient. The region of the target in complex environment is obtained by K-means clustering algorithm and the source seed points are selected from the region. The regional characteristics around the seed points as the criteria for growth and the image gradient information is applied as the condition of the adaptive growth immune field. This algorithm improves the accuracy of target extraction in complex environment while preventing overgrowth. We compare the algorithm with the original algorithm and other algorithms and we find that the new algorithm combining edge gradient information can reduce the probability of over growth and ensure the integrity of target extraction under complex background.