Spray angle measurement method based on grayscale image differential gradient
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摘要:
雾场边界及雾化角作为雾场的重要特性参数，主要通过图像法进行测量。在图像处理过程中，一般是将灰度图转化为二值化图像，然后依次针对二值化图像进行处理和计算。由于雾场的多相流特性，得到的二值化阈值和图像与实际雾场是否一致缺少评判依据。提出根据喷雾的灰度图像直接处理，得到掩模板并作用于灰度图像，采用图像形态学和迭代方法，计算灰度图像的梯度值。通过得到梯度值最大时的灰度图像，计算雾场边界和雾化角。实验表明，该方法提供了一种雾场边界的数值判断依据，通过梯度最大值判断并提取雾场边界，从而通过程序自动实现雾场边界提取与雾化角拟合测量。
Abstract:The spray boundary and spray angle work as important characteristic parameters of spray field, which are mainly measured by image method. In image processing, the grayscale images are generally converted into binary images firstly. Then, the binary images are processed and calculated. Due to the multiphase flow characteristics of spray field, there is no judgement basis whether the obtained binary threshold and the image are consistent with the actual spray field. In order to digitally judge the spray field boundary, it was proposed to process directly according to the grayscale image of the spray. The mask templates were obtained and applied to the grayscale image, and then the gradient value of the grayscale image were calculated and compared by image morphology and iterative method. The spray field boundary and the spray angle were calculated by obtaining the grayscale image when the gradient value was maximum. Experimental results show that the proposed method provides a numerical judgement basis to judge and extract the spray field boundary through the gradient maximum value. Thus, the spray field boundary extraction and the spray angle fitting measurement are realized automatically by the program.
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Keywords:
- spray angle /
- grayscale image /
- gradient /
- iterative method
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表 1 灰度梯度迭代数据
Table 1 Results of gradient iteration on grayscale images
迭代次数 灰度梯度 迭代次数 灰度梯度 1 0.965 9 18.461 2 0.956 10 5.434 3 1.345 11 3.689 4 1.036 12 2.597 5 1.466 13 2.320 6 1.861 14 1.103 7 2.503 15 1.592 8 100.094 16 1.167 
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