TY - GEN
T1 - An efficient method for target extraction of infrared images
AU - Li, Ying
AU - Mao, Xingjin
PY - 2010
Y1 - 2010
N2 - This paper proposes an efficient method to extract targets from an infrared image. First, the regions of interests (ROIs) which contain the entire targets and a little background region are detected based on the variance weighted information entropy feature. Second, the infrared image is modeled by Gaussian Markov random field, and the ROIs are used as the target regions while the remaining region as the background to perform the initial segmentation. Finally, by searching solution space within the ROIs, the targets are accurately extracted by energy minimization using the iterated condition mode. Because the iterated segmentation results are updated within the ROIs only, this coarse-to-fine extraction method can greatly accelerate the convergence speed and efficiently reduce the interference of background noise. Experimental results of the real infrared images demonstrate that the proposed method can extract single and multiple infrared objects accurately and rapidly.
AB - This paper proposes an efficient method to extract targets from an infrared image. First, the regions of interests (ROIs) which contain the entire targets and a little background region are detected based on the variance weighted information entropy feature. Second, the infrared image is modeled by Gaussian Markov random field, and the ROIs are used as the target regions while the remaining region as the background to perform the initial segmentation. Finally, by searching solution space within the ROIs, the targets are accurately extracted by energy minimization using the iterated condition mode. Because the iterated segmentation results are updated within the ROIs only, this coarse-to-fine extraction method can greatly accelerate the convergence speed and efficiently reduce the interference of background noise. Experimental results of the real infrared images demonstrate that the proposed method can extract single and multiple infrared objects accurately and rapidly.
KW - infrared image segmentation
KW - iterated condition mode
KW - Markov random field
KW - regions of interests
KW - weighted information entropy
UR - http://www.scopus.com/inward/record.url?scp=78649939964&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-16530-6_23
DO - 10.1007/978-3-642-16530-6_23
M3 - 会议稿件
AN - SCOPUS:78649939964
SN - 364216529X
SN - 9783642165290
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 185
EP - 192
BT - Artificial Intelligence and Computational Intelligence - International Conference, AICI 2010, Proceedings
T2 - 2010 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2010
Y2 - 23 October 2010 through 24 October 2010
ER -