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Human segmentation of infrared image for mobile robot search

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Abstract

In the search robotics field, human target segmentation method plays a basic preprocessing step in the visual guidance. However, with the wide application of the infrared sensor on robot vision, traditional segmentation methods are facing more challenges of low-contrast, overlapping and blurring targets, and complex background. This paper introduces an infrared human segmentation approach that integrates the improved pulse coupled neural network (PCNN), the curvature gravity gradient tensor (CGGT) and the mathematical morphology to address these above problems. This approach starts with an improved PCNN segmentation model. Local dynamic synapse weights are designed to enhance the synchronous pulsing ability of the improved PCNN model with similar inputs, and a reformed threshold is conducted to guide the process of segmentation. Moreover, eigenvalues of CGGT are guaranteed in this model as linking coefficients, in order to capture the edges and details of human target more exactly in segmentation. Lastly, the segmentation result is repaired by morphology operators, to ensure the integrity of the target region and the independent noise removal. Experiments on 200 real infrared images captured by the mobile robot CQSearcher I, demonstrate that our method is superior over the other classic segmentation methods in both the subjective visual performance and the objective indicators of misclassification error and f-measure.

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Acknowledgements

This work was supported by the Ph.D. Programs Foundation of Ministry of Education of China (Grant No.20130191110021), the Fundamental Research Funds for the Central Universities of China(Grant No.XDJK2013C157), and the Program of Study Abroad for Young Scholar Sponsored by China Scholarship Council (Grant No.201506995083).

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Authors and Affiliations

  1. Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing, 40044, China

    Fuliang He, Yongcai Guo & Chao Gao

  2. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Fuliang He

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  1. Fuliang He
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  2. Yongcai Guo
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  3. Chao Gao
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Correspondence to Fuliang He.

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He, F., Guo, Y. & Gao, C. Human segmentation of infrared image for mobile robot search. Multimed Tools Appl 77, 10701–10714 (2018). https://doi.org/10.1007/s11042-017-4872-x

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  • DOI: https://doi.org/10.1007/s11042-017-4872-x

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