# An Image-Processing Method for Extracting Kinematic Characteristics of Droplets during Pulsed GMAW
## 3 Extraction of Droplet Region by Image Processing
### The Period with a Detached Droplet  
Unlike the period with an undetached droplet, the droplet disconnects from the wire during the period with a detached droplet, as displayed in Figure 20. As a result, the process of the recognition of the droplet could be simplified, with no need to cut off the connection between the droplet and wire. Moreover, the interference of the arc was not as notable as before because of the decrease in current and arc brightness in this period. However, the first step was also to convert the color image into a grayscale image (see Figure 21).  
Figure 19: The effect of recognition of the target droplet.  
Figure 20: A typical image in the period with a detached droplet.  
Then, different edge operators were used to process the grayscale image directly. The edge extraction effect of the full image and the partial enlarged images near the droplet are shown in Figures 22 and 23, respectively.  
Figure 21: Grayscale of an original image in the period with a detached droplet.  
Figure 23: _Cont._  
Figure 22: Edge recognition effects in the period with a detached droplet: (**a**) Sobel operator; (**b**) Prewitt operator; (**c**) Roberts operator; (**d**) Laplacian of Gaussian (LoG) operator; (**e**) Canny operator.  
As displayed in Figure 22, the Sobel operator, Prewitt operator, and Roberts operator were not sensitive to background interference, and the background was relatively clean. However, some gaps could be found on the contour of droplet in Figure 23a-c. In contrast, the LoG operator and Canny operator had a strong ability to recognize edges. Although the background interference was relatively large in Figure 22d,e, it can be seen from Figure 23d,e that the droplet contour was complete and continuous.  
Therefore, a hole-filling algorithm could be used on the image shown in Figure 22d,e to fill the droplet region, and the result is shown in Figure 24. It can be seen that the droplet was filled with white pixels. There were barely other white connected domains in the background in Figure 24a, while a few dispersive white connected domains can be found in Figure 24b. However, in common, these areas of the connected domains in the background were too small for the droplet. When the biggest area of connected domains was found, the droplet was also found (see Figure 25).  
Figure 23: The partial enlarged images near the droplet after edge recognition: (**a**) [PERSON] operator; (**b**) [PERSON] operator; (**c**) [PERSON] operator; (**d**) Laplacian of Gaussian (LoG) operator; (**e**) Canny operator.  
Figure 24: The result of the hole-filling algorithm: (**a**) Laplacian of Gaussian (LoG) operator; (**b**) Canny operator.  
Figure 25: Recognition of the droplet: (**a**) Laplacian of Gaussian (LoG) operator; (**b**) Canny operator.  
As a result, thanks to the relative weak intensity of the arc, there was no need to divide the droplet artificially in the images. Hence, the whole image process in the period with a detached droplet was simpler than that in the period with an undetached droplet. The LoG operator and Canny operator both showed excellent effects for recognizing the complete droplet edge. Above all, recognition of the droplet during the period with a detached droplet can be summarized as follows:  
Step 1: Grayscale transformation;  
Step 2: Edge operator test;  
Step 3: Filling hollow areas;  
Step 4: Recognition of the droplet.