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Detection and Separation of Smoke From Single Image Frames

机译:从单个图像帧中检测和分离烟雾

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摘要

This paper proposes novel methods for detecting and separating smoke from a single image frame. Specifically, an image formation model is derived based on the atmospheric scattering models. The separation of a frame into quasi-smoke and quasi-background components is formulated as convex optimization that solves a sparse representation problem using dual dictionaries for the smoke and background components, respectively. A novel feature is constructed as a concatenation of the respective sparse coefficients for detection. In addition, a method based on the concept of image matting is developed to separate the true smoke and background components from the smoke detection results. Extensive experiments on detection were conducted and the results showed that the proposed feature significantly outperforms existing features for smoke detection. In particular, the proposed method is able to differentiate smoke from other challenging objects (e.g. fog/haze, cloud, and so on) with similar visual appearance in a gray-scale frame. Experiments on smoke separation also demonstrated that the proposed separation method can effectively estimate/separate the true smoke and background components.
机译:本文提出了一种从单个图像帧中检测和分离烟雾的新颖方法。具体地,基于大气散射模型导出图像形成模型。将帧分为准烟雾和准背景分量的公式化为凸优化,该优化分别使用对烟和背景分量的双字典来解决稀疏表示问题。一种新颖的特征被构造为用于检测的各个稀疏系数的串联。此外,还开发了一种基于图像消光概念的方法,以从烟雾检测结果中分离出真实的烟雾和背景成分。进行了广泛的检测实验,结果表明所提出的功能大大优于烟雾检测的现有功能。特别地,所提出的方法能够在灰度级框架中以相似的视觉外观将烟与其他挑战性物体(例如,雾/霾,云等)区分开。烟雾分离实验还表明,所提出的分离方法可以有效地估计/分离真实烟雾和背景成分。

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