期刊文献+

基于亮度守恒假设的火灾图像运动特征提取 被引量:1

Motion Feature Extraction of Fire Images based on Brightness Conservation Assumption
下载PDF
导出
摘要 论述了火灾火焰和烟雾图像特征分析及提取方法,分析了这些方法尚存在的问题,指出了火灾图像运动特征分析相对于静态特征分析的优势.针对亮度不变假设在提取火灾火焰和烟雾图像运动特征时存在的问题,提出将目前最新的光流计算方法——亮度守恒假设应用于视频火灾探测中,结合全局平滑性假设,推导了亮度守恒方程解的迭代形式,并对基于亮度守恒假设的火灾火焰和烟雾图像运动特征识别方法进行了分析和讨论,为开发更加准确可靠的视频火灾探测识别算法提供了理论支撑. The fire flame and smoke image feature analyzing and extraction methods are discussed, while the remaining problems of these methods are analyzed and the advantage of fire motion feature compared to static features is pointed out. Aiming at the specific application problems of brightness constancy assumption in fire flame and smoke motion feature extraction, the latest optical flow technique-brightness conservation assumption is put forward and applied in video fire detection. Together with the global smooth assumption, the iteration form of the solution of brightness conservation assumption equation is derived. The recognition method of fire flame and smoke motion feature based on brightness conservation assumption is analyzed and discussed, which can provide the theoretical support for the development of video fire detection recognition algorithm which is more accurate.
作者 李婷雪
出处 《沈阳大学学报(自然科学版)》 CAS 2013年第3期255-258,共4页 Journal of Shenyang University:Natural Science
关键词 视频火灾探测 光流法 亮度不变假设 亮度守恒假设 video fire detection optical flow method brightness constancy assumption brightness conservation assumption
  • 相关文献

参考文献13

  • 1Yu Chunyu , FangJun , Wang Iinjun, et al. Video Fire Smoke Detection using Motion and Color Features[J]. Fire Technology, 2010,46:651- 653.
  • 2Yu Chunyu, Zhang Yongrning , Wang Iinjun. Video Smoke Detection based on Optical Flow Features and Back-propagation Neural Networks[CJ II 14th International Conference on Automatic Fire Detection. Duisburg , Germany, 2009:327-34l.
  • 3Amiaz T, Fazekas S, Chetverikov D, et al. Detecting Regions of Dynamic Texture[CJ. International . Conference on Scale and Variational Methods in Computer Vision, Germany, 2007: 848 - 859.
  • 4Fazekas S, Chetverikov D. Normal Versus Complete Flow in Dynamic Texture Recognition: A Comparative Study[C]. Proceedings of the 4th International Workshop on Texture Analysis and Synthesis. Beijing, China, 2005: 37 - 42.
  • 5Chen T, Wu r. Chiou Y. An Early Fire-Detection Method Based on Image Processing[CJ. International Conference on Image Processing. Singapore, 2004: 1707 -1710.
  • 6Liu Chebin , Ahuja N. Vision based Fire Detection[C]11 Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK, 2004, 4 (4): 34 -137.
  • 7Marbach G, Loepfe M, Brupbacher T. An Image Processing Technique for Fire Detection in Video Images[n Fire SafetyJournal, 2006,41(4) :285 - 289.
  • 8Toreyin B U, Dedeoglu Y, Guddukbay U, et al. Computer Vision based Method for Real-time Fire and Flame Detection[J]. Pattern Recognition Letters, 2006, 27(1) :49 - 58.
  • 9Chen Thouho , Yin Yenhui , Huang Shifeng, et al. The Smoke Detection for Early Fire-Alarming System Base on Video Processing[CJ II Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, USA, 2006:427 - 430.
  • 10Toreyin B U, Dedeoglu Y, Cetin A E, et al. Wavelet based Real-time Smoke Detection in Video[C]. 13th European Signal Process Conference. Antalya , Turkey, 2005:4 - 8.

同被引文献2

引证文献1

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部