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A smoke detection algorithm based on K-means segmentation

机译:一种基于K-Means分割的烟雾检测算法

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The fire accident usually causes economical and ecological damage as well as cause danger to people's lives. Therefore, its early detection is must for controlling this damage. Also smoke is considered as main constituent of fire, thus an efficient smoke detection algorithm on sequences of frame obtained from static camera is proposed. It is based on computer vision based technology. This algorithm uses color feature of smoke & is comprised of following steps: reading the image, preprocessing, classify color pixels using k-means segmentation. This paper discusses mainly the segmentation problem. It adopts L*a*b* color space and k-means clustering algorithm to isolate the smoke from video sequences. Finally the K-means algorithm used is compared with the fuzzy c-means algorithm used previously.
机译:火灾事故通常会导致经济和生态的损害以及对人们的生活导致危险。因此,其早期检测必须控制这种损害。烟雾也被认为是火的主要组成部分,因此提出了一种高效的烟雾检测算法在静态相机获得的框架序列中。它基于计算机视觉的技术。该算法使用烟雾的颜色特征,由以下步骤组成:读取图像,预处理,使用k-means分割对颜色像素进行分类。本文主要讨论分割问题。它采用l * a * b *颜色空间和k均值聚类算法,将烟雾与视频序列隔离。最后将使用的K-means算法与先前使用的模糊C均值算法进行比较。

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