- Email: [email protected]
Simultaneous measurement of velocities and size distribution of fine atmospheric aerosols based on image processing and PTV techniques
ARTICLE IN PRESS
Atmospheric Environment 39 (2005) 3015–3021 www.elsevier.com/locate/atmosenv
Simultaneous measurement of velocities and size distribution of fine atmospheric aerosols based on image processing and PTV techniques Hailiang Zhao, Changfu You, Haiying Qi, Xuchang Xu Key Laboratory of Thermal Science and Power Engineering, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China Received 15 August 2004; received in revised form 17 December 2004; accepted 17 December 2004
Abstract An integrated measurement system was developed using high-speed photography, image recognition and PIV/PTV techniques to simultaneously measure the velocities and size distributions of fine atmospheric aerosols. The photographic system included a high-power (25 W) continuous laser, a microscope and a high-speed CCD camera (30–10,000 fps) could capture successive particle images on a microsecond time scale and micron space scale. The software system integrates improved image recognition algorithms for circular particles, various image-processing algorithms and advanced PIV/PTV algorithms. The information is extracted from the images in three steps: image preprocessing, particle recognition from images to obtain the diameters and positions of the particles, and the velocity vector calculation using PIV/PTV techniques. The system was tested using a computer-generated image. The results show that the particle extraction algorithms are robust and accurate. The system was also used to effectively measure fine atmospheric particles in the smoke from burning incense. Some problems are identified for future improvements. r 2005 Elsevier Ltd. All rights reserved. Keywords: Velocity and size measurement; Aerosols; PIV/PTV; Circle detection; Image processing
1. Introduction There is an increasing awareness of the influence that atmospheric particulate matter (PM) has on environmental systems and human health. Researchers in many countries have studied the formation, motion and coagulation mechanisms of fine atmospheric particles to find approaches to control their emission and formation in the atmosphere. These studies of the mechanisms of fine particle (PM10, PM2.5) emissions have shown the importance of the motion, size distribution and number density of fine particles in the particle formation process Corresponding author. Tel./fax: +86 10 6278 1740.
E-mail address: [email protected] (C. You).
(for example, in a flame) relative to the ambient atmosphere perhaps with external force fields, as the basic data for the analysis of particle formation, coagulation and elimination. These parameters usually must be measured simultaneously, along with the fluid phase velocity, to develop correlations relating all the key parameters. So suitable technology for simultaneously measuring these parameters is very necessary for experimental research of particulate control. However, there are few papers related to these kinds of measurements in the literature. Most research on the simultaneous measurement of particle size and velocity are for single particles, for example, Fincke et al. (1993) using a laser sizing system/LDV, Harada and Murakami (1991) using an image sensor and Plawsky and Hatton
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2004.12.048
ARTICLE IN PRESS 3016
H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
(1988) using optical fiber probes. For large numbers of particles, the measurements are limited to velocity measurements, for example, Chen et al. (1994). However, the rapid development of photographic systems, computers, image processing and PIV/PTV algorithms has provided a new approach for simultaneous measurements of several parameters. A technique based on image processing with PIV/PTV systems was reported by Song et al. (1999), but it was not able to deal with overlapping particles, so it was limited to sparse particle systems. Shen et al. (2001) proposed a similar method based on image processing and a fuzzy PTV analysis to simultaneously measure the velocities and sizes of falling particles which was able to distinguish overlapping particles. The measurements of fine atmospheric aerosols present some other special requirements for the measurement methods. For example, atmospheric particles can be both very fine and very coarse with poor light-scattering ability and the particle motion is easily disturbed. Therefore, an image-based technique is needed for measurements of fine aerosols, because an image-based method can simultaneously capture a large amount of information about the velocities and sizes of many particles without disturbing the flow. These techniques are based on advanced optical and photographic devices such as electron microscopes, high-power lasers and high speed CCD cameras. In addition, many image processing and pattern recognition algorithms have been developed together with efficient PIV/PTV algorithms to extract accurate information from particle images. This paper describes an integral image-based measurement system developed for simultaneous measurement of velocities and size distributions of fine atmospheric aerosols. Its hardware system includes a high-power continuous laser (Spectra-Physics BeamLoks2080-25S Argon Ion Laser System), a microscope and a high-speed CCD camera (Photron Fastcam Super 10 k, with camera speed of 30–10,000 fps). The software system combines an improved circle recognition method for digital images, image processing algorithms and various advanced PIV/PTV algorithms. The system can accurately measure velocities and size distributions of fine particles on a microsecond time scale and micron space scale. The system was used to measure the velocities and particle distributions in the flow of smoke particles from burning incense with satisfactory results obtained for the particle velocities and size distributions.
2. Image-processing software system 2.1. Extraction of particles from images 2.1.1. Circle extraction algorithms In the image-based measurement method for particle flows, the most important task is to extract the particle
information from the images (e.g. particle center positions and radii). This information is used to calculate all the other parameters, such as particle velocities, size distribution and number density. The recognition of round objects in digitized images is based on the detection of circular arcs from planar curves, which has attracted much attention in the fields of pattern recognition and computer vision for many years. The particle recognition method can be divided into three steps: Step 1: Detect edges of all particles. Step 2: Curve segmentation (group elements of each curve to make each segment belong to a unique circle according to some criteria). Step 3: Curve clustering and parameter calculation. The first and most important step in identifying the particles in the images is edge identification. There are many edge-identification algorithms that give good results for different images, such as the Roberts, Sobel, Prewitt, Kirsch and Canny methods. Various image processing algorithms, such as noise elimination and image enhancement, are used for preprocessing the particle images before using these edge-identification algorithms. After the edges are located, the edges are thinned to one pixel in width and the T-junctions are deleted. Then the particle contour curves are obtained by linking these edge pixels using the chain-code method. All the particle contours are then checked to identify overlapping contours. The overlapping contours must be divided into segments (arcs), each of which belongs to only one unique circle. This step is key to the successful operation of the software. The curve segmentation process has various algorithms depending on the purpose of the segmentation. Various algorithms have been developed for arc detection, such as the classical circular arc detection methods based on the Hough transformation (Duda and Hart, 1972; Kierkegaard, 1992; Pei and Horng, 1995). However, the Hough transformation methods have problems choosing accumulated peaks and require large amounts of computing time and memory. Other methods based for the detection of points of intersections are based on the identification of curvature extremes (Pla, 1996; Lim et al., 1995). However, the curvature at each point usually cannot be calculated accurately for an actual digitized curve. Another contour segmentation method proposed by Shen et al. (2000) identifies the intersections of two different arcs in a curve using geometric methods. The point joining two different circles in a curve was the pixel with a higher probability of being a local extrema as the curve is rotated from 01 to 3601. Suppose a curve is described by a series of pixels with the coordinates (x1, y1), (x2, y2),y, (xN, yN). A pixel (xi, yi)
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
3017
is a local extrema if it satisfies ðxi1 pxi Xxiþ1 Þ _ ðxi1 Xxi pxiþ1 Þ _ðyi1 pyi Xyiþ1 Þ _ ðyi1 Xyi pyiþ1 Þ:
(1)
Shen et al. (2000) showed that the ratio between the probability of intersections being extrema and the average probability of all other pixels is 4–5, so the intersections were easily distinguished by this ratio. However, in the actual calculations not all pixels with high probability are the real intersection between two different arcs even though the intersections have high probability values. Especially when the particle contours are not very smooth locally (for example, with fine, noncircular particles, the particle contours ended up as a series of short straight lines), many pixels which were not intersections were identified as extrema by Eq. (1). The curves were then broken into too many short segments, which substantially reduced the accuracy of the parameter calculation. Also, if two different arcs had a relatively smooth intersection, the probability values of the intersection points were also not easily distinguished from other pixels. These conditions frequently occur in the images of fine atmospheric aerosols because of their coarse surfaces and frequent overlaps. Two methods were used to identify intersection points. In the local averaging method, the neighboring points xi1 ; xiþ1 ; yi1 ; yiþ1 in Eq. (1) were replaced by the average value of several (three in this paper) local neighboring pixels. That is
Fig. 2. Computer-generated images for algorithm test.
xi1 ¼ ðxi1 þ xi2 þ xi3 Þ=3, yi1 ¼ ðyi1 þ yi2 þ yi3 Þ=3, xiþ1 ¼ ðxiþ1 þ xiþ2 þ xiþ3 Þ=3, yiþ1 ¼ ðyiþ1 þ yiþ2 þ yiþ3 Þ=3.
ð2Þ
After this local averaging, the intersection pixels were more outstanding from the other pixels so the probability threshold to identify intersections was more easily identified with few false results. A second method used to identify intersection points was a filtering method based on least-squares curve fitting. Referring to Fig. 1, point B is a potential intersection point and points A and C are neighboring candidate intersections (maybe the beginning or end point of a curve). The least-squares method was used to estimate the radii of candidate
segmentsAB; BC and AC asR1 ; R2 and R12 : If pixel B is not an intersection point, that is, AC belongs to one unique circle, these radii should satisfy ðR1 R12 Þ=R12 o _ðR2 R12 Þ=R12 o;
Fig. 1. A curve with some potential intersection points to be filtered.
(3)
where is a small error value (0.1 in this paper). Otherwise, pixel B is actually an intersection point and the curve should be broken there. This filtering method
eliminates most false intersections and greatly reduces excessive segmentation of the curves. The contour of a circle may be divided into several separate parts in the edge-finding or the segmentation process. To accurately calculate the circle parameters, all the arc segments belonging to the same circle must be clustered together. Then the particle center positions and radii can be calculated using the least-squares method based on these clustered segments.
2.1.2. Sample processing of a computer-generated image The recognition performance of the image-processing software was first tested using a computer-generated sample image, which was the same as that used by Shen et al. (2000) for comparison. As shown in Fig. 2, the image had two sizes of circles. The radii of the large circles were 32 pixels and while those of the small circles were 14 pixels. The gray-levels of the two kinds of circles were 64 for the small circles and 192 for the large circles. In the image, each set of circles with the same diameter was arranged in a compact hexagonal spacing, with the large and small particles overlapping. Every large
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
3018
Although the lower limit of the particle diameter is 5 pixels for the recognition from images, the limit for the real particle sizes depends on the optical magnification. Usually 1 mm is feasible to be recognized in this system.
particle was tangent to all its neighboring large particles and was overlapped by three small particles. The software based on the improved circle recognition method was used to identify the circles in the sample image. The extracted results are shown in Fig. 3. The center locations and radii of all 72 large circles and 72 small circles in the image were accurately extracted. Table 1 lists the recognition errors for this test, including the data from Shen et al. (2000) for comparison. In the table, jDrj stands for the average radius difference between the extracted circles and the original circles, Dd c denotes the average center position difference, and s is the standard deviation of the radius. jDrj=r and Dd c =r are the corresponding non-dimensional values. The recognition accuracy is very high. The absolute errors of the radii are less than 0.1 pixel and the relative errors are less than 0.5% for both the large and small circles. The absolute errors of the center errors were less than 0.3 pixels for all the circles and even for the small circles, the relative center errors were less than 2%. The accuracy was higher than that reported by Shen et al. (2000) whose algorithm did not include the filtering method of intersection points. This test shows that the recognition accuracy of different circles is very high, so the following calculation of other parameters, such as the particle size distribution and particle velocities, will also be very accurate.
2.2. Calculation of particle velocities After the center positions and radii of all particles have been extracted from the images, the particle velocities can be calculated using PIV/PTV algorithms. Various PIV/PTV algorithms were integrated into the software, including cross-correlation PIV, BICC PTV (Yamamoto et al., 1996), SpringModel PTV (Okamoto et al., 1995), 4-Frame PTV (Nishino and Torii, 1989) and velocity gradient tensor (VGT) PTV (Ishikawa et al., 1997). Therefore, different algorithms can be used for different problems. Because the particle velocity is magnified at the same time with the size magnifying, high-speed camera must be used even though the velocity is very small. So the system limit for velocity depends on both optical magnification and camera speed. In our experiments for fine particles the upper limit of 100 mm s1 is possible. (This is the condition using microscope to measure both velocity and size. If the microscope is not used in measuring just macroscopical velocity, high velocity limit can be reached.)
3. Photographic system The fine atmospheric aerosol measurements must be conducted on a microsecond time scale and a micron space scale. In the current measurement system, a microscope and a high-speed CCD camera (Photron Fastcam Super 10 k, with camera speed of 30–10000 fps) were utilized to capture the successive images of fine particles and a high-power continuous laser (SpectraPhysics BeamLoks 2080-25S Argon Ion Laser System) was used for the illumination. A sketch of the experimental setup is shown in Fig. 4. The particleladen air flowed through a rectangular channel made of optical glass, which was placed horizontally on the microscope platform. The illuminating laser was
Fig. 3. Circle extraction results for the test image.
Table 1 Comparison of recognition errors
Large circles (this method) Large circles (Ref.) Small circles (this method) Small circles (Ref.)
jDrj (pixel)
Dd c (pixel)
jDrj=r
Dd c =r
s
0.0495 0.278 0.0663 0.129
0.167 0.289 0.313 0.359
0.0015 0.0090 0.0047 0.0092
0.0052 0.0087 0.0224 0.0258
0.0517 0.2982 0.0505 0.0927
This method refers to the method improved by this paper; ‘Ref. ’refers to the data from Shen et al. (2000).
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
3019
Fig. 4. Experimental setup.
transmitted through an optical fiber and transformed into a sheet light to illuminate the horizontal plane in the flow field, which was vertical to the axes of the object lens. The high-speed CCD camera captured successive magnified images through the microscope.
4. Measurement of incense smoke particles This system was used to measure the particle size distributions and velocities of the smoke flow of burning incense as an example of fine atmospheric aerosols. The smoke flowed slowly mixed with air through the rectangular channel as shown in Fig. 4. The section size of the flow channel is 25 10 mm and the distance from the measurement point to the burning point is about 200 mm. Two successive frames of the captured images are shown in Fig. 5, which were captured with the 1 camera speed of 60 fps and the exposure time of 125 s: The backgrounds of the original images were very dark, so these images were processed with gray-level equalization. According to our observation, the smoke aerosols of burning incense are nearly all spherical liquid droplets. In the literature there are also some reports that the smoke aerosols of burning incense are liquid droplets (Jetter et al., 2002). But for the microscopic photography in which the focal length is very shallow, some particles were somewhat out of focus as flowing and thus became blurry in the images. This is really a difficult problem for microscopic photography of moving particles. Also because of the light diffraction of the very fine particles, the particle images had rather rough
Fig. 5. Two successive frames of the captured smoke images.
contours and much noise. For example, some circles in Fig. 5 have very rough edges and this condition is made more serious by the overlapping with other particle contours. These characteristics greatly complicate the edge and circle detection processes, which make the automatic information extraction difficult. The software was used to automatically detect the center positions and particle sizes and velocities from these two successive images. The particle recognition results are shown in Fig. 6, in which the black circles are the recognized particle contours overlapped with the original particle images for comparison. The velocity vectors calculated using the PTV algorithms are shown in Fig. 7. Even though the incense smoke particle images are very noisy and the particles are irregular, the automated analysis gave relatively satisfactory results. The particle recognition rate was above 95% (65 correctly recognized particles in image 1 with 66 particles in image 2). The PTV algorithms gave 48 correct velocity vectors with 5
ARTICLE IN PRESS 3020
H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
Fig. 6. Particle recognition results of the two successive images.
Fig. 7. Velocity vectors calculated using the PTV algorithms.
Fig. 8. The measured particle size distribution.
incorrect vectors. The range of the measured velocity magnitudes was from 18 to 743 mm s1. The calculated particle size distributions for images 1 and 2 are shown in Fig. 8. The measured particle size distributions of the two images ranged from 4 to 27 mm with most particles around 5–10 mm. Although the distributions of the two images were basically similar, some of the particle diameters varied a little between the two successive images. In our observation, the sizes of some particles varied as flowing in the visualization field. This variation could be possibly due to the sizes of the burning incense smoke particles actually varying as they are flowing or imaging errors due to the focusing problem. According to our observation, the imaging errors may be the most possibly important reasons. Compared to the thickness of the laser sheet, the depth of the focus of the microscope is very shallow. So when the particles flow out of the focus plane, their contours would become a little bigger in the images. This also suggests that there would be some error in the particle
sizes in the imaging due to this focusing problem. This is a troublesome problem in the dynamic imaging of moving aerosols by microscope. These phenomena need further analysis in future researches to improve the imaging quality for flowing aerosols.
5. Discussion and conclusions A high-speed photographic measurement system was integrated with image recognition and PIV/PTV techniques to simultaneously measure the velocities and size distributions of fine atmospheric aerosols. The photographic system included a high-power (25 W) continuous laser, a microscope and a high-speed CCD camera (30–10,000 fps). The software system integrates improved image-recognition algorithms for circular particles, various image-processing algorithms and advanced PIV/PTV algorithms.
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
The circle recognition algorithm uses a geometric method to detect the intersections of overlapped particle contours with a filtering method based on the leastsquares method for more accurate curve segmentation. A test showed that the software was efficient and accurately measured the circle sizes and locations from the images. The system can automatically analyze images of fine aerosols on a microsecond time scale and a micron space scale to accurately measure the particle velocities and size distributions. The system was used to measure the velocities and size distributions of smoke particles from burning incense, as an example, with satisfactory results. However, there are still some issues that need further analysis in future research for more accurate dynamic measurements of fine atmospheric aerosols. For example, the variation of the particle diameters in successive images needs further analysis to determine whether it is imaging error due to the focusing problem or real variations in the smoke particle diameters. Anyway, the imaging error is a serious problem which must be considered and resolved for more accurate size measurements and higher image quality. Also, some other measures should be taken to increase the quality of the images. In conclusion, this paper describes a system for automatic, simultaneous measurement of particle velocities and sizes that will greatly facilitate research on fine atmospheric aerosols.
Acknowledgment This research was supported by the Special Funds for Major State Basic Research Projects (No. 2002CB211604).
References Chen, C., Kocher, M., Jones, D., 1994. Granular particle linear flow velocity measurement using an electronic linear image sensor. Computers and Electronics in Agriculture 11, 117–129. Duda, R.O., Hart, P.E., 1972. Use of the Hough transformation to detect lines and curves in pictures. Communications of ACM 15 (1), 11–15.
3021
Fincke, J., Swank, W., Jeffery, C., Mancuso, C., 1993. Simultaneous measurement of particle size, velocity and temperature. Measurement Science and Technology 4, 559–565. Harada, Y., Murakami, T., 1991. Method for the time-resolved measurement of the size and position of a moving particle using an image sensor. Applied Optics 30 (33), 4921–4929. Ishikawa, M., Yamamoto, F., Murai, Y., Iguchi, M., Wada, A., 1997. A novel PIV algorithm using velocity gradient tensor. Proceedings of the Second International Workshop on PIV’97, Fukui, pp. 51–56. Jetter, J.J., Guo, Z., McBrian, J.A., Flynn, M.R., 2002. Characterization of emissions from burning incense. The Science of the Total Environment 295, 51–67. Kierkegaard, P., 1992. A method for detection of circular arcs based on the Hough transform. Machine Version and Applications 5, 249–263. Lim, K., Xin, K., Hong, G., 1995. Detection and estimation of circular segments. Pattern Recognition Letters 16, 627–636. Nishino, K., Torii, K.A., 1989. Fluid-dynamically optimum particle tracking method for 2-D PTV: Triple pattern matching algorithm. Transport phenomena in thermal engineering, 1411–1416. Okamoto, K., Schmidl, W.D., Hassan, Y.A., 1995. Least force technique for the particle tracking algorithm. Proceedings of the First International Workshop on PIV’95, Fukui, p. 21. Pei, S., Horng, J., 1995. Circular arc detection based on the Hough transform. Pattern Recognition Letters 16, 615–625. Pla, F., 1996. Recognition of partial circular shapes from segmented contours. Computer Vision and Image Understanding 63 (2), 334–343. Plawsky, J., Hatton, T., 1988. Optical fiber probes for particle size/velocity measurements: a simulation model. Part 1. Applied Optics 27 (20), 4200–4215. Shen, L., Song, X., Iguchi, M., Yamamoto, F., 2000. A method for recognizing particles in overlapped particle images. Pattern Recognition Letters 21, 21–30. Shen, L., Song, X., Murai, Y., Iguchi, M., Yamamoto, F., 2001. Velocity and size measurement of falling particles with fuzzy PTV. Flow Measurement and Instrumentation 12, 191–199. Song, X., Yamamoto, F., Iguchi, M., Kato, H., Murai, Y., Ishii, K., Sasaki, Y., 1999. Particle imaging velocimetry measurement of particle number rate and velocity for falling particles. ISIJ International 39 (5), 471–476. Yamamoto, F., Wada, A., Iguchi, M., Ishikawa, M., 1996. Discussion of the cross-correlation methods for PIV. Journal of Flow Visualization and Image Processing 3, 65–78.
Atmospheric Environment 39 (2005) 3015–3021 www.elsevier.com/locate/atmosenv
Simultaneous measurement of velocities and size distribution of fine atmospheric aerosols based on image processing and PTV techniques Hailiang Zhao, Changfu You, Haiying Qi, Xuchang Xu Key Laboratory of Thermal Science and Power Engineering, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China Received 15 August 2004; received in revised form 17 December 2004; accepted 17 December 2004
Abstract An integrated measurement system was developed using high-speed photography, image recognition and PIV/PTV techniques to simultaneously measure the velocities and size distributions of fine atmospheric aerosols. The photographic system included a high-power (25 W) continuous laser, a microscope and a high-speed CCD camera (30–10,000 fps) could capture successive particle images on a microsecond time scale and micron space scale. The software system integrates improved image recognition algorithms for circular particles, various image-processing algorithms and advanced PIV/PTV algorithms. The information is extracted from the images in three steps: image preprocessing, particle recognition from images to obtain the diameters and positions of the particles, and the velocity vector calculation using PIV/PTV techniques. The system was tested using a computer-generated image. The results show that the particle extraction algorithms are robust and accurate. The system was also used to effectively measure fine atmospheric particles in the smoke from burning incense. Some problems are identified for future improvements. r 2005 Elsevier Ltd. All rights reserved. Keywords: Velocity and size measurement; Aerosols; PIV/PTV; Circle detection; Image processing
1. Introduction There is an increasing awareness of the influence that atmospheric particulate matter (PM) has on environmental systems and human health. Researchers in many countries have studied the formation, motion and coagulation mechanisms of fine atmospheric particles to find approaches to control their emission and formation in the atmosphere. These studies of the mechanisms of fine particle (PM10, PM2.5) emissions have shown the importance of the motion, size distribution and number density of fine particles in the particle formation process Corresponding author. Tel./fax: +86 10 6278 1740.
E-mail address: [email protected] (C. You).
(for example, in a flame) relative to the ambient atmosphere perhaps with external force fields, as the basic data for the analysis of particle formation, coagulation and elimination. These parameters usually must be measured simultaneously, along with the fluid phase velocity, to develop correlations relating all the key parameters. So suitable technology for simultaneously measuring these parameters is very necessary for experimental research of particulate control. However, there are few papers related to these kinds of measurements in the literature. Most research on the simultaneous measurement of particle size and velocity are for single particles, for example, Fincke et al. (1993) using a laser sizing system/LDV, Harada and Murakami (1991) using an image sensor and Plawsky and Hatton
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2004.12.048
ARTICLE IN PRESS 3016
H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
(1988) using optical fiber probes. For large numbers of particles, the measurements are limited to velocity measurements, for example, Chen et al. (1994). However, the rapid development of photographic systems, computers, image processing and PIV/PTV algorithms has provided a new approach for simultaneous measurements of several parameters. A technique based on image processing with PIV/PTV systems was reported by Song et al. (1999), but it was not able to deal with overlapping particles, so it was limited to sparse particle systems. Shen et al. (2001) proposed a similar method based on image processing and a fuzzy PTV analysis to simultaneously measure the velocities and sizes of falling particles which was able to distinguish overlapping particles. The measurements of fine atmospheric aerosols present some other special requirements for the measurement methods. For example, atmospheric particles can be both very fine and very coarse with poor light-scattering ability and the particle motion is easily disturbed. Therefore, an image-based technique is needed for measurements of fine aerosols, because an image-based method can simultaneously capture a large amount of information about the velocities and sizes of many particles without disturbing the flow. These techniques are based on advanced optical and photographic devices such as electron microscopes, high-power lasers and high speed CCD cameras. In addition, many image processing and pattern recognition algorithms have been developed together with efficient PIV/PTV algorithms to extract accurate information from particle images. This paper describes an integral image-based measurement system developed for simultaneous measurement of velocities and size distributions of fine atmospheric aerosols. Its hardware system includes a high-power continuous laser (Spectra-Physics BeamLoks2080-25S Argon Ion Laser System), a microscope and a high-speed CCD camera (Photron Fastcam Super 10 k, with camera speed of 30–10,000 fps). The software system combines an improved circle recognition method for digital images, image processing algorithms and various advanced PIV/PTV algorithms. The system can accurately measure velocities and size distributions of fine particles on a microsecond time scale and micron space scale. The system was used to measure the velocities and particle distributions in the flow of smoke particles from burning incense with satisfactory results obtained for the particle velocities and size distributions.
2. Image-processing software system 2.1. Extraction of particles from images 2.1.1. Circle extraction algorithms In the image-based measurement method for particle flows, the most important task is to extract the particle
information from the images (e.g. particle center positions and radii). This information is used to calculate all the other parameters, such as particle velocities, size distribution and number density. The recognition of round objects in digitized images is based on the detection of circular arcs from planar curves, which has attracted much attention in the fields of pattern recognition and computer vision for many years. The particle recognition method can be divided into three steps: Step 1: Detect edges of all particles. Step 2: Curve segmentation (group elements of each curve to make each segment belong to a unique circle according to some criteria). Step 3: Curve clustering and parameter calculation. The first and most important step in identifying the particles in the images is edge identification. There are many edge-identification algorithms that give good results for different images, such as the Roberts, Sobel, Prewitt, Kirsch and Canny methods. Various image processing algorithms, such as noise elimination and image enhancement, are used for preprocessing the particle images before using these edge-identification algorithms. After the edges are located, the edges are thinned to one pixel in width and the T-junctions are deleted. Then the particle contour curves are obtained by linking these edge pixels using the chain-code method. All the particle contours are then checked to identify overlapping contours. The overlapping contours must be divided into segments (arcs), each of which belongs to only one unique circle. This step is key to the successful operation of the software. The curve segmentation process has various algorithms depending on the purpose of the segmentation. Various algorithms have been developed for arc detection, such as the classical circular arc detection methods based on the Hough transformation (Duda and Hart, 1972; Kierkegaard, 1992; Pei and Horng, 1995). However, the Hough transformation methods have problems choosing accumulated peaks and require large amounts of computing time and memory. Other methods based for the detection of points of intersections are based on the identification of curvature extremes (Pla, 1996; Lim et al., 1995). However, the curvature at each point usually cannot be calculated accurately for an actual digitized curve. Another contour segmentation method proposed by Shen et al. (2000) identifies the intersections of two different arcs in a curve using geometric methods. The point joining two different circles in a curve was the pixel with a higher probability of being a local extrema as the curve is rotated from 01 to 3601. Suppose a curve is described by a series of pixels with the coordinates (x1, y1), (x2, y2),y, (xN, yN). A pixel (xi, yi)
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
3017
is a local extrema if it satisfies ðxi1 pxi Xxiþ1 Þ _ ðxi1 Xxi pxiþ1 Þ _ðyi1 pyi Xyiþ1 Þ _ ðyi1 Xyi pyiþ1 Þ:
(1)
Shen et al. (2000) showed that the ratio between the probability of intersections being extrema and the average probability of all other pixels is 4–5, so the intersections were easily distinguished by this ratio. However, in the actual calculations not all pixels with high probability are the real intersection between two different arcs even though the intersections have high probability values. Especially when the particle contours are not very smooth locally (for example, with fine, noncircular particles, the particle contours ended up as a series of short straight lines), many pixels which were not intersections were identified as extrema by Eq. (1). The curves were then broken into too many short segments, which substantially reduced the accuracy of the parameter calculation. Also, if two different arcs had a relatively smooth intersection, the probability values of the intersection points were also not easily distinguished from other pixels. These conditions frequently occur in the images of fine atmospheric aerosols because of their coarse surfaces and frequent overlaps. Two methods were used to identify intersection points. In the local averaging method, the neighboring points xi1 ; xiþ1 ; yi1 ; yiþ1 in Eq. (1) were replaced by the average value of several (three in this paper) local neighboring pixels. That is
Fig. 2. Computer-generated images for algorithm test.
xi1 ¼ ðxi1 þ xi2 þ xi3 Þ=3, yi1 ¼ ðyi1 þ yi2 þ yi3 Þ=3, xiþ1 ¼ ðxiþ1 þ xiþ2 þ xiþ3 Þ=3, yiþ1 ¼ ðyiþ1 þ yiþ2 þ yiþ3 Þ=3.
ð2Þ
After this local averaging, the intersection pixels were more outstanding from the other pixels so the probability threshold to identify intersections was more easily identified with few false results. A second method used to identify intersection points was a filtering method based on least-squares curve fitting. Referring to Fig. 1, point B is a potential intersection point and points A and C are neighboring candidate intersections (maybe the beginning or end point of a curve). The least-squares method was used to estimate the radii of candidate
segmentsAB; BC and AC asR1 ; R2 and R12 : If pixel B is not an intersection point, that is, AC belongs to one unique circle, these radii should satisfy ðR1 R12 Þ=R12 o _ðR2 R12 Þ=R12 o;
Fig. 1. A curve with some potential intersection points to be filtered.
(3)
where is a small error value (0.1 in this paper). Otherwise, pixel B is actually an intersection point and the curve should be broken there. This filtering method
eliminates most false intersections and greatly reduces excessive segmentation of the curves. The contour of a circle may be divided into several separate parts in the edge-finding or the segmentation process. To accurately calculate the circle parameters, all the arc segments belonging to the same circle must be clustered together. Then the particle center positions and radii can be calculated using the least-squares method based on these clustered segments.
2.1.2. Sample processing of a computer-generated image The recognition performance of the image-processing software was first tested using a computer-generated sample image, which was the same as that used by Shen et al. (2000) for comparison. As shown in Fig. 2, the image had two sizes of circles. The radii of the large circles were 32 pixels and while those of the small circles were 14 pixels. The gray-levels of the two kinds of circles were 64 for the small circles and 192 for the large circles. In the image, each set of circles with the same diameter was arranged in a compact hexagonal spacing, with the large and small particles overlapping. Every large
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
3018
Although the lower limit of the particle diameter is 5 pixels for the recognition from images, the limit for the real particle sizes depends on the optical magnification. Usually 1 mm is feasible to be recognized in this system.
particle was tangent to all its neighboring large particles and was overlapped by three small particles. The software based on the improved circle recognition method was used to identify the circles in the sample image. The extracted results are shown in Fig. 3. The center locations and radii of all 72 large circles and 72 small circles in the image were accurately extracted. Table 1 lists the recognition errors for this test, including the data from Shen et al. (2000) for comparison. In the table, jDrj stands for the average radius difference between the extracted circles and the original circles, Dd c denotes the average center position difference, and s is the standard deviation of the radius. jDrj=r and Dd c =r are the corresponding non-dimensional values. The recognition accuracy is very high. The absolute errors of the radii are less than 0.1 pixel and the relative errors are less than 0.5% for both the large and small circles. The absolute errors of the center errors were less than 0.3 pixels for all the circles and even for the small circles, the relative center errors were less than 2%. The accuracy was higher than that reported by Shen et al. (2000) whose algorithm did not include the filtering method of intersection points. This test shows that the recognition accuracy of different circles is very high, so the following calculation of other parameters, such as the particle size distribution and particle velocities, will also be very accurate.
2.2. Calculation of particle velocities After the center positions and radii of all particles have been extracted from the images, the particle velocities can be calculated using PIV/PTV algorithms. Various PIV/PTV algorithms were integrated into the software, including cross-correlation PIV, BICC PTV (Yamamoto et al., 1996), SpringModel PTV (Okamoto et al., 1995), 4-Frame PTV (Nishino and Torii, 1989) and velocity gradient tensor (VGT) PTV (Ishikawa et al., 1997). Therefore, different algorithms can be used for different problems. Because the particle velocity is magnified at the same time with the size magnifying, high-speed camera must be used even though the velocity is very small. So the system limit for velocity depends on both optical magnification and camera speed. In our experiments for fine particles the upper limit of 100 mm s1 is possible. (This is the condition using microscope to measure both velocity and size. If the microscope is not used in measuring just macroscopical velocity, high velocity limit can be reached.)
3. Photographic system The fine atmospheric aerosol measurements must be conducted on a microsecond time scale and a micron space scale. In the current measurement system, a microscope and a high-speed CCD camera (Photron Fastcam Super 10 k, with camera speed of 30–10000 fps) were utilized to capture the successive images of fine particles and a high-power continuous laser (SpectraPhysics BeamLoks 2080-25S Argon Ion Laser System) was used for the illumination. A sketch of the experimental setup is shown in Fig. 4. The particleladen air flowed through a rectangular channel made of optical glass, which was placed horizontally on the microscope platform. The illuminating laser was
Fig. 3. Circle extraction results for the test image.
Table 1 Comparison of recognition errors
Large circles (this method) Large circles (Ref.) Small circles (this method) Small circles (Ref.)
jDrj (pixel)
Dd c (pixel)
jDrj=r
Dd c =r
s
0.0495 0.278 0.0663 0.129
0.167 0.289 0.313 0.359
0.0015 0.0090 0.0047 0.0092
0.0052 0.0087 0.0224 0.0258
0.0517 0.2982 0.0505 0.0927
This method refers to the method improved by this paper; ‘Ref. ’refers to the data from Shen et al. (2000).
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
3019
Fig. 4. Experimental setup.
transmitted through an optical fiber and transformed into a sheet light to illuminate the horizontal plane in the flow field, which was vertical to the axes of the object lens. The high-speed CCD camera captured successive magnified images through the microscope.
4. Measurement of incense smoke particles This system was used to measure the particle size distributions and velocities of the smoke flow of burning incense as an example of fine atmospheric aerosols. The smoke flowed slowly mixed with air through the rectangular channel as shown in Fig. 4. The section size of the flow channel is 25 10 mm and the distance from the measurement point to the burning point is about 200 mm. Two successive frames of the captured images are shown in Fig. 5, which were captured with the 1 camera speed of 60 fps and the exposure time of 125 s: The backgrounds of the original images were very dark, so these images were processed with gray-level equalization. According to our observation, the smoke aerosols of burning incense are nearly all spherical liquid droplets. In the literature there are also some reports that the smoke aerosols of burning incense are liquid droplets (Jetter et al., 2002). But for the microscopic photography in which the focal length is very shallow, some particles were somewhat out of focus as flowing and thus became blurry in the images. This is really a difficult problem for microscopic photography of moving particles. Also because of the light diffraction of the very fine particles, the particle images had rather rough
Fig. 5. Two successive frames of the captured smoke images.
contours and much noise. For example, some circles in Fig. 5 have very rough edges and this condition is made more serious by the overlapping with other particle contours. These characteristics greatly complicate the edge and circle detection processes, which make the automatic information extraction difficult. The software was used to automatically detect the center positions and particle sizes and velocities from these two successive images. The particle recognition results are shown in Fig. 6, in which the black circles are the recognized particle contours overlapped with the original particle images for comparison. The velocity vectors calculated using the PTV algorithms are shown in Fig. 7. Even though the incense smoke particle images are very noisy and the particles are irregular, the automated analysis gave relatively satisfactory results. The particle recognition rate was above 95% (65 correctly recognized particles in image 1 with 66 particles in image 2). The PTV algorithms gave 48 correct velocity vectors with 5
ARTICLE IN PRESS 3020
H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
Fig. 6. Particle recognition results of the two successive images.
Fig. 7. Velocity vectors calculated using the PTV algorithms.
Fig. 8. The measured particle size distribution.
incorrect vectors. The range of the measured velocity magnitudes was from 18 to 743 mm s1. The calculated particle size distributions for images 1 and 2 are shown in Fig. 8. The measured particle size distributions of the two images ranged from 4 to 27 mm with most particles around 5–10 mm. Although the distributions of the two images were basically similar, some of the particle diameters varied a little between the two successive images. In our observation, the sizes of some particles varied as flowing in the visualization field. This variation could be possibly due to the sizes of the burning incense smoke particles actually varying as they are flowing or imaging errors due to the focusing problem. According to our observation, the imaging errors may be the most possibly important reasons. Compared to the thickness of the laser sheet, the depth of the focus of the microscope is very shallow. So when the particles flow out of the focus plane, their contours would become a little bigger in the images. This also suggests that there would be some error in the particle
sizes in the imaging due to this focusing problem. This is a troublesome problem in the dynamic imaging of moving aerosols by microscope. These phenomena need further analysis in future researches to improve the imaging quality for flowing aerosols.
5. Discussion and conclusions A high-speed photographic measurement system was integrated with image recognition and PIV/PTV techniques to simultaneously measure the velocities and size distributions of fine atmospheric aerosols. The photographic system included a high-power (25 W) continuous laser, a microscope and a high-speed CCD camera (30–10,000 fps). The software system integrates improved image-recognition algorithms for circular particles, various image-processing algorithms and advanced PIV/PTV algorithms.
ARTICLE IN PRESS H. Zhao et al. / Atmospheric Environment 39 (2005) 3015–3021
The circle recognition algorithm uses a geometric method to detect the intersections of overlapped particle contours with a filtering method based on the leastsquares method for more accurate curve segmentation. A test showed that the software was efficient and accurately measured the circle sizes and locations from the images. The system can automatically analyze images of fine aerosols on a microsecond time scale and a micron space scale to accurately measure the particle velocities and size distributions. The system was used to measure the velocities and size distributions of smoke particles from burning incense, as an example, with satisfactory results. However, there are still some issues that need further analysis in future research for more accurate dynamic measurements of fine atmospheric aerosols. For example, the variation of the particle diameters in successive images needs further analysis to determine whether it is imaging error due to the focusing problem or real variations in the smoke particle diameters. Anyway, the imaging error is a serious problem which must be considered and resolved for more accurate size measurements and higher image quality. Also, some other measures should be taken to increase the quality of the images. In conclusion, this paper describes a system for automatic, simultaneous measurement of particle velocities and sizes that will greatly facilitate research on fine atmospheric aerosols.
Acknowledgment This research was supported by the Special Funds for Major State Basic Research Projects (No. 2002CB211604).
References Chen, C., Kocher, M., Jones, D., 1994. Granular particle linear flow velocity measurement using an electronic linear image sensor. Computers and Electronics in Agriculture 11, 117–129. Duda, R.O., Hart, P.E., 1972. Use of the Hough transformation to detect lines and curves in pictures. Communications of ACM 15 (1), 11–15.
3021
Fincke, J., Swank, W., Jeffery, C., Mancuso, C., 1993. Simultaneous measurement of particle size, velocity and temperature. Measurement Science and Technology 4, 559–565. Harada, Y., Murakami, T., 1991. Method for the time-resolved measurement of the size and position of a moving particle using an image sensor. Applied Optics 30 (33), 4921–4929. Ishikawa, M., Yamamoto, F., Murai, Y., Iguchi, M., Wada, A., 1997. A novel PIV algorithm using velocity gradient tensor. Proceedings of the Second International Workshop on PIV’97, Fukui, pp. 51–56. Jetter, J.J., Guo, Z., McBrian, J.A., Flynn, M.R., 2002. Characterization of emissions from burning incense. The Science of the Total Environment 295, 51–67. Kierkegaard, P., 1992. A method for detection of circular arcs based on the Hough transform. Machine Version and Applications 5, 249–263. Lim, K., Xin, K., Hong, G., 1995. Detection and estimation of circular segments. Pattern Recognition Letters 16, 627–636. Nishino, K., Torii, K.A., 1989. Fluid-dynamically optimum particle tracking method for 2-D PTV: Triple pattern matching algorithm. Transport phenomena in thermal engineering, 1411–1416. Okamoto, K., Schmidl, W.D., Hassan, Y.A., 1995. Least force technique for the particle tracking algorithm. Proceedings of the First International Workshop on PIV’95, Fukui, p. 21. Pei, S., Horng, J., 1995. Circular arc detection based on the Hough transform. Pattern Recognition Letters 16, 615–625. Pla, F., 1996. Recognition of partial circular shapes from segmented contours. Computer Vision and Image Understanding 63 (2), 334–343. Plawsky, J., Hatton, T., 1988. Optical fiber probes for particle size/velocity measurements: a simulation model. Part 1. Applied Optics 27 (20), 4200–4215. Shen, L., Song, X., Iguchi, M., Yamamoto, F., 2000. A method for recognizing particles in overlapped particle images. Pattern Recognition Letters 21, 21–30. Shen, L., Song, X., Murai, Y., Iguchi, M., Yamamoto, F., 2001. Velocity and size measurement of falling particles with fuzzy PTV. Flow Measurement and Instrumentation 12, 191–199. Song, X., Yamamoto, F., Iguchi, M., Kato, H., Murai, Y., Ishii, K., Sasaki, Y., 1999. Particle imaging velocimetry measurement of particle number rate and velocity for falling particles. ISIJ International 39 (5), 471–476. Yamamoto, F., Wada, A., Iguchi, M., Ishikawa, M., 1996. Discussion of the cross-correlation methods for PIV. Journal of Flow Visualization and Image Processing 3, 65–78.