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Flue gas layer feature segmentation based on multi-channel pixel adaptive

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Abstract

It is very difficult to accurately separate the smoke contour in fire video. Because the scene of a fire is complex and changeable, and there are often many interference factors, such as continuously changing light and fast switching scenes, it is difficult to accurately guarantee the separation of smoke contours. In this paper, an aggregate channel feature algorithm that combines color, saturation and texture is designed, and a fast pixel block feature matching method is used to build the background model. In order to overcome the error caused by scene switching, a dynamic threshold control method based on the background switching speed is proposed, which eliminates the interference caused by the dynamic background update, and effectively extracts the foreground smoke pixels and smoke contour map. The experimental results show that the algorithm can accurately extract the smoke layer contour map, and compared with the traditional foreground extraction algorithms, the algorithm is faster and more accurate.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Yunfei Yin, Hui Cheng & Huan Liu

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  1. Yunfei Yin
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  2. Hui Cheng
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  3. Huan Liu
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Correspondence to Yunfei Yin.

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Yin, Y., Cheng, H. & Liu, H. Flue gas layer feature segmentation based on multi-channel pixel adaptive. Multimed Tools Appl 79, 29069–29085 (2020). https://doi.org/10.1007/s11042-020-09466-5

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