基于线增强主动轮廓的OH-PLIF图像火焰前检测

Huijie Fan, Wei Dong, Yandong Tang
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引用次数: 2

摘要

针对高速平面激光诱导荧光(PLIF)图像中火焰前沿边界的检测问题,提出了一种新的线增强主动轮廓模型(LEAC模型)。该模型首先利用直线增强滤波算法对火焰前缘的裂纹区域和梯度进行增强,然后利用CV模型从增强后的PLIF图像中提取火焰前缘边界和裂纹边缘。我们将LEAC模型与经典CV模型在不同PLIF图像上进行了比较。实验结果表明,该模型能够准确地检测出火焰前缘,并对经典CVmodel无法完全检测出的狭长裂纹区域的精确边缘有较好的检测效果。此外,它对图像噪声和曲线初始化不敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flame Front Detection by Line Enhance Active Contour from OH-PLIF Images
this paper presents a new Line Enhance Active Contour model (LEAC model) to detect the flame front boundaries from high speed Planar Laser Induced Fluorescence (PLIF) images. The model first enhances the crack region and the gradient of flame front by the Line Enhance Filtering algorithm, and then extracts flame front boundaries and crake edges from the enhanced PLIF images using the CV model. We compared the LEAC model with the classical CV model on different PLIF images. Experimental results show that our model can detect the flame front accurately, and it has good performance on detecting the exact edges of long and narrow crack regions, where the classical CVmodel can not detect completely. Moreover, it is insensitive to image noise and curve initialization.
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