摘要翻译：
颜色分析是基于图像的火灾探测算法中的一个关键步骤。许多在静止图像中提出的火灾检测算法都容易因颜色类似火灾的物体而产生假警报。为了设计一个具有较高虚警率的基于颜色的系统，提出了一种新的颜色微分转换矩阵，该矩阵能有效地处理高颜色复杂度的图像。该转换矩阵的元素是通过对具有高度火色相似背景的火灾样本图像进行K-模型聚类和粒子群优化算法得到的。然后利用所提出的转换矩阵构造了两个新的火灾颜色检测框架。第一种检测方法是两阶段非线性图像变换框架，而第二种检测方法是利用所提出的转换矩阵对图像进行直接变换。将所提出的方法与文献中的替代方法进行了性能比较。实验结果表明，线性图像变换方法在虚警率方面优于其他方法，而非线性两阶段图像变换方法在F-score指标上性能最好，并在漏检率和虚警率之间提供了较好的折衷。
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英文标题：
《Fire detection in a still image using colour information》
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作者：
Oluwarotimi Giwa and Abdsamad Benkrid
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Colour analysis is a crucial step in image-based fire detection algorithms. Many of the proposed fire detection algorithms in a still image are prone to false alarms caused by objects with a colour similar to fire. To design a colour-based system with a better false alarm rate, a new colour-differentiating conversion matrix, efficient on images of high colour complexity, is proposed. The elements of this conversion matrix are obtained by performing K-medoids clustering and Particle Swarm Optimisation procedures on a fire sample image with a background of high fire-colour similarity. The proposed conversion matrix is then used to construct two new fire colour detection frameworks. The first detection method is a two-stage non-linear image transformation framework, while the second is a direct transformation of an image with the proposed conversion matrix. A performance comparison of the proposed methods with alternate methods in the literature was carried out. Experimental results indicate that the linear image transformation method outperforms other methods regarding false alarm rate while the non-linear two-stage image transformation method has the best performance on the F-score metric and provides a better trade-off between missed detection and false alarm rate.
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PDF链接：
https://arxiv.org/pdf/1803.03828