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A smoke segmentation algorithm based on improved intelligent seeded region growing
Image segmentation method based on region growing has the advantages of simple segmentation method and complete segmentation target. Because the color discrimination and gray gradient of smoke are not obvious, the traditional region growing segmentation method is difficult to separate it from the image, resulting in an unsatisfactory segmentation effect. To solve this problem, this paper partially improved the region growing method and proposed a new smoke segmentation algorithm based on the improved intelligent seeded region growing (IISRG) method. First, smoke images obtained from experimental videos were converted from the RGB color space to the HSV color space, and image binarization was achieved using background subtraction with an adaptive threshold in the V channel. Then, a pixel in the binary image was selected intelligently as the seed point, which was used for the regional growth. The final smoke segmentation images were obtained by the morphological processing of region growing images. Experimental smoke segmentation results show that the proposed algorithm has a higher overlap rate and a lower overflow rate, and performs a better smoke segmentation effect compared with the other two approaches. In addition, this algorithm can also effectively solve the problems of under‐segmentation and over‐segmentation of smoke images.
A smoke segmentation algorithm based on improved intelligent seeded region growing
Image segmentation method based on region growing has the advantages of simple segmentation method and complete segmentation target. Because the color discrimination and gray gradient of smoke are not obvious, the traditional region growing segmentation method is difficult to separate it from the image, resulting in an unsatisfactory segmentation effect. To solve this problem, this paper partially improved the region growing method and proposed a new smoke segmentation algorithm based on the improved intelligent seeded region growing (IISRG) method. First, smoke images obtained from experimental videos were converted from the RGB color space to the HSV color space, and image binarization was achieved using background subtraction with an adaptive threshold in the V channel. Then, a pixel in the binary image was selected intelligently as the seed point, which was used for the regional growth. The final smoke segmentation images were obtained by the morphological processing of region growing images. Experimental smoke segmentation results show that the proposed algorithm has a higher overlap rate and a lower overflow rate, and performs a better smoke segmentation effect compared with the other two approaches. In addition, this algorithm can also effectively solve the problems of under‐segmentation and over‐segmentation of smoke images.
A smoke segmentation algorithm based on improved intelligent seeded region growing
Zhao, Wangda (author) / Chen, Weixiang (author) / Liu, Yujie (author) / Wang, Xiangwei (author) / Zhou, Yang (author)
Fire and Materials ; 43 ; 725-733
2019-10-01
9 pages
Article (Journal)
Electronic Resource
English
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