Smoke Image Segmentation Based on Color Model
DOI:
https://doi.org/10.24212/2179-3565.2015v6i2p130-138Palavras-chave:
Image segmentation, K-means algorithm, Color space, LAB, HSVResumo
Smoke is the most significant feature in the process of fire, so it’s possible to rely on smoke detection to detect fire. While the smoke image segmentation is the most difficult and also indispensable step in the analysis of smoke image detection. In order to improve its accuracy and effectively exclude the disturbances of non-smoke image, and lower the false alarm rate, it puts forward a kind of smoke image segmentation based on color model. It uses K-means clustering in Lab color space and threshold segmentation in HSV color space, then merges the two results. Finally, it uses the method of shen filter and regional mark to denoise, Experimental results on segmentation of smoke image show that the proposed method is able to segment smoke from the background.
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