19 October 2016 Algorithm research on infrared imaging target extraction based on GAC model
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Proceedings Volume 10153, Advanced Laser Manufacturing Technology; 1015304 (2016) https://doi.org/10.1117/12.2243768
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
Good target detection and tracking technique is significantly meaningful to increase infrared target detection distance and enhance resolution capacity. For the target detection problem about infrared imagining, firstly, the basic principles of level set method and GAC model are is analyzed in great detail. Secondly, “convergent force” is added according to the defect that GAC model is stagnant outside the deep concave region and cannot reach deep concave edge to build the promoted GAC model. Lastly, the self-adaptive detection method in combination of Sobel operation and GAC model is put forward by combining the advantages that subject position of the target could be detected with Sobel operator and the continuous edge of the target could be obtained through GAC model. In order to verify the effectiveness of the model, the two groups of experiments are carried out by selecting the images under different noise effects. Besides, the comparative analysis is conducted with LBF and LIF models. The experimental result shows that target could be better locked through LIF and LBF algorithms for the slight noise effect. The accuracy of segmentation is above 0.8. However, as for the strong noise effect, the target and noise couldn’t be distinguished under the strong interference of GAC, LIF and LBF algorithms, thus lots of non-target parts are extracted during iterative process. The accuracy of segmentation is below 0.8. The accurate target position is extracted through the algorithm proposed in this paper. Besides, the accuracy of segmentation is above 0.8.
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Yingchun Li, Youchen Fan, Yanqing Wang, "Algorithm research on infrared imaging target extraction based on GAC model", Proc. SPIE 10153, Advanced Laser Manufacturing Technology, 1015304 (19 October 2016); doi: 10.1117/12.2243768; https://doi.org/10.1117/12.2243768
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