A large depth-of-field Particle Image Velocimeter (PIV) has been developed at NASA GSFC to characterize dynamic dust environments on planetary surfaces. This instrument detects and senses lofted dust particles. We have been developing an autonomous image analysis algorithm architecture for the PIV instrument to greatly reduce the amount of data that it has to store and downlink. The algorithm analyzes PIV images and reduces the image information down to only the particle measurement data we are interested in receiving on the ground - typically reducing the amount of data to be handled by more than two orders of magnitude. We give a general description of PIV algorithms and describe only the algorithm for estimating the velocity of the traveling particles

Antonille, Scott R.

Bos, Brent J.

Memarsadeghi, Nargess

NASA Technical Reports Server

CHARACTERIZATION OF MOVING DUST PARTICLES 
Brent J. Bos, Scott R. Antonille, and Nargess Memarsadeghi 
NASA Goddard Space Flight Center, Greenbelt, Maryland, 20771. 
ABSTRACT 
A large depth-of-field Particle Image Velocimeter (PJV) has 
been developed at NASA GSFC to characterize dynamic dust 
environments on planetary surfaces. This instrument detects 
and senses lofted dust particles. We have been developing an 
autonomous image analysis algorithm architecture for the PJV 
instrument to greatly reduce the amount of data that it has to 
store and downlink:. The algorithm analyzes PIV images and 
reduces the image information down to only the particle mea-
surement data we are interested in receiving on the ground -
typically reducing the amount of data to be handled by more 
than two orders of magnitude. We give a general description 
. of PJV algorithms and describe only the algorithm for estimat-
ing the velocity of the traveling particles. 
Index Terms- Planetary lander, velocimetry, compres- . 
sion, filtering, deconvolution. 
1. INTRODUCTION 
A large depth-of-field Particle Image Velocimeter (PIV) was 
primarily developed at NASA GSFC for characterizing fluxes 
of wind-borne dust particles in Martian atmosphere in par-
ticular, and planetary surfaces in general [1]. This instru-
ment could also be adapted to terrestrial use in measuring size 
and velocities of opaque particles carried by natural winds 
and industrial gases. Examples of potential terrestrial appli-
cations include monitoring of airborne industrial pollutants 
and airborne particles in mine shafts. This instrument detects 
lofted dust particles and senses the number of particles per 
unit volume, measures their sizes, velocities (both speed and 
direction) and shape factors when the particles are large. To 
measure these particle characteristics in-flight, the instrument 
gathers two-dimensional image data at a high frame rate, typ-
ically greater than 4000 Hz, generating large amounts of data 
for every second of operation ( -6 GB/s). High data rates are 
required to suffid.ently measure dynamic dust events such as 
dusty plumes and dust vortices. Events such as these will re-
quire several minutes of observation time by the instrument, 
generating more than a terabyte of data per event. Given cur-
rent technology, this amount of data would be very difficult 
Corresponding author: Brent J. Bos. NASNGSFC. Code 551, Green-
belt, MD, 20771.Email:Brent.J.Bos@nasa.gov, Tel: 301-286-5670. 
Fig. 1. PIV images of 10/.tm diameter particles in free-fall. 
to store onboard a spacecraft and downlink: to Earth. To miti-
gate this problem we have developed a set of image process-
ing algorithms to dramatically reduce the amount of data that 
needs to be stored and downlinked. The algorithm analyzes 
PIV images and automatically reduces the image information 
down to only the particle measurement data we are interested 
in receiving on the ground. We have· a functional algorithm 
architecture along with several key pieces of algorithm logic 
that have been proven out on 2007 and 2009 field test data 
acquired with a proof-of-concept PIV instrument. 
The rest of this paper is organized as follows. Section 
2 gives an overview of various PIV particle characterization 
algorithms. Then, we discuss one of these algorithms, in par-
ticular that of determining the travel direction and velocity of 
dust particles, in Section 3. We describe our experimenlc; for 
velocity determination in Section 4. Results are presented in 
Section 5. Section 6 concludes the paper. · 
2. PIV CHARACTERIZATION ALGORITHMS 
For small particles, approximately less than 150 µmin diam· 
eter, the dust signatures that PIV captures are dominated by 
rotationally symmetric near-field diffraction patterns (Fig. 1). 
Several image processing algorithms were designed and de-
veloped for PIV in order to characterize dust particles. Char-
acterization algorithms include counting and determining lo-
cation of dust particles, their sizes, as well as direct.ion and 
velocity of their movement. Fig, 3 illustrates the architecture 
of these algorithms. The algorithm processing begins by read-
ing in the raw PIV images output by the instrument and then 
correcting the images' bad pixel signals and making the im-
ages flat fielded (Process l). In other words, the spatial irreg-
ularities in detector characteristics and/or illumination non-
uniformities are compensated for. This is a conventional im-
https://ntrs.nasa.gov/search.jsp?R=20180000035 2019-08-30T13:32:59+00:00Z
age processing step typically applied to planetary image data. 
Next, the images are filtered (Process 2) to remove the signals 
of any particles that have become stuck to the PIV optics and 
to decrease the noise from individual pixels. This piece of al-
gorithm logic is a novel algorithm that significantly improves 
the instrument's detection of small particles. We have named 
it the Temporal Habituation Filter (THF) since it habituates 
to the background noise and isolates the dust signatures. Af-
ter processing images with the THF, previously undetectable 
dust signatures can be detected in the images. Due to the 
complexity and significance of this new image filter, it will be 
described in detail in a separate paper. 
After the PIV images have been filtered, they are used as 
direct inputs into three separate processes. Process 3 ana-
lyzes the images to determine which particle signatures are 
not approximately rotationally symmetric. Those signatures 
are then removed from the images and processed using exist-
ing image segmentation techniques (Process 4b). But if the 
signatures are found to be approximately rotationally sym-
metric, a radial profile fitting routine (Process 4a) analyzes 
the signatures to determine the signature center and calculate 
a best fit to the diffraction pattern. Processes 4a-b generate 
the first actual particle data that we are interested in: parti-
cle positions and the number of particles per frame. Once the 
particle identification is completed, the particle location data 
and the filtered image data are input to Processes 5a-b. 
Process 5b determines the particle size. For large parti-
cles, whose signatures are not dominated by near-field diffrac- · 
tion, their size and shape can be analyzed using conventional 
geometric analysis approaches. We have devoted a consider-
able amount of effort to develop efficient logic to determine 
particle sizes based on the diffraction patterns of small parti-
cles - since this is an area without much prior art. We have 
investigated several fast but noise-resistant particle size met-
rics. A statistical metric that we have found to be useful is 
one that includes the standard deviation of the image inten-
sity values. This metric can be normalized in various ways 
but it always relates to particle diameter with a monotonic 
function (Fig. 2). We have also investigated metrics that mea-
sure the number and locations of the diffraction pattern oscil-
lation points and developed an understanding of how to relate 
those to particle size. Of particular note is a result that we 
have obtained using the Hilbert-Huang Transform (HHT) [2]. 
Our results indicate that there is a relationship between parti-
cle size and the number of zero crossings from one of the HHT 
intrinsic mode functions generated by particle signatures. The 
most physically meaningful particle size determination logic 
we have developed uses conversation of energy. The images 
and diffraction patterns that the PIV instrument generates are 
shadows caused by dust particles when the particles pass in 
between the collimated PIV light source and the detector col-
lection optics. So, the amount of light that is blocked from 
reaching the instrument's detector plane is related to the par-
ticle size. The final piece of particle information that needs to 
10 
Standard Deviation vs. Particle Diameter 
210 410 610 
Dlameter(um) 
810 
Fig. 2. Standard Deviation vs. Simulated Particle's Diameter 
be extracted from the PIV image set is particle velocity - both 
speed and direction of travel. This is done in two steps with 
Process 5a and 6. Process 5a is described in Section 3. 
The. information obtained from Process 5a is used in Pro-
cess 6 to efficiently and more accurately determine particle 
speed and resolve the 180° ambiguity in the particle direction 
of travel, whenever possible. If the particle was moving too 
fast, it will only appear in a single frame. If this is determined 
to be the case, then the algorithm will not search for a signa: 
ture match in the next frame. If Process 6 determines that a 
particle could be present in the next frame, then it only looks 
for a signature match in those particular areas consistent with 
the previously determined speed and line of travel while also 
considering the accompanying uncertainty. This is an impor-
tant piece of the Process 6 logic for small particles because 
as the particles get smaller, their signatures start to look more 
and more alike. Once particle signatures have been paired 
up frame-to-frame, Process 6 calculates an updated speed and 
travel direction for those particles that appeared in more than 
one frame based on the known PIV detector frame rate. For 
those particles that appeared in only one frame, the informa-
tion generated in Process 5a is saved. The amount of data 
compression that PIV algorithi:11s can achieve depends on the 
number of particles in each frame. For a typical scene of 20 
particles per frame the amount of data is reduced by a factor 
more than 103 • 
3. VELOCITY DETERMINATION APPROACH 
We worked on an algorithm for estimating the travel direc-
tion and velocity of each identified particle (Process 5a and 
6 in Fig. 3). The logic for this algorithm depends on the fact 
that the particle signatures, if blurred, will produce two areas 
that are noticeably brighter than others (Fig. 3). Our empiri-
cal results obtained by convolving a static simulated particle 
pattern with a blur function confirms this. We have also ob-
served that elongated camera exposure times result in blurred 
shadow patterns of the entrained particles while the shortest 
exposures show nearly symmetric diffraction shadows. This 
indicates that the extended shadow from the entrained parti-
" 
.. / .............. _ 
NO•·m~ii.-.. , 
• ioi>l! .. ally •• 
·<7.~· 
:t:· 
.3 
Fig. 3. PIV Data Compression Algorithm Architecture 
cles is most likely from movement, and not due to their shape 
or material properties. Additionally, because the average ef-
fect of the particles is to lower the amount of energy that 
reaches the corresponding shadowed area on the detector, the 
particles' motion must have followed the direction in which 
the shadow is extended. Finally, the extent of the blurring ef-
fect is known to be consistent with both the entrained particle 
velocity (using anemometers) and the camera exposure time. 
Process 5a talces advantage of the mentioned image smear 
in a single frame. Even though the PIV operates with very 
short detector exposure times, typically 20µs, fast moving 
particles will still produce a blurred signature that can be mea-
sured. Fig. 3 shows a PIV dust diffraction signature for a 
lOOµm diameter particle traveling in the direction shown by 
the semi-transparent arrow. There are two bright points on 
opposite sides of the center of the signature. A line orthog-
onal to the line that connects the two brightest areas will be 
parallel to the direction of travel. So Process 5a determines 
the particle direction of travel by locating the centroid of the 
two brightest areas on either side of the signature center, and 
calculating the orthogonal slope to the line that connects these 
two centroids. We calculated these centroids by averaging top 
ten brightest points in these two brightest areas. This calcula-
tion cannot determine if the particle is moving in the positive 
or negative direction along the line. 
After the path of motion is determined, Process 5a also 
estimates how fast the particle was traveling. It does this 
by generating a series of blur kernels consisting of a single 
line of various lengths with a slope that is equal to the di-
rection of the travel slope. These kernels are iteratively con-
volved with ideal signatures consistent with the particle size 
and shape, which were previously determined, until maxi-
mum agreement is achieved between the convolution and the 
measured signature. The known PIV exposure time can then 
be used with the PIV sample volume pixel scale factor to es-
timate the particle speed. In other words, the initial clear dust 
particle image I has been blurred by a filter image B resulting 
in a blurry image of.a particle similar to that of Fig. 3. 
(l) 
, where * represents the convolution operator, and B is a ma-
trix of the same size as I whose elements are either black 
or white along a line (travel vector). While the direction of 
the blur (the filter in image B) is perpendicular to the line 
connecting the two brightest areas in Bl, its length which is 
a function of the particle's exposure time within the frame 
should be determined by solving for the blur filter that best 
fits Eq. 1. Since we have already determined the direction 
of the velocity vector, we only_have to solve for its length. 
We expect the length of this vector to be short based on our 
knowledge of the frame rates and typical particle velocities. 
Thus, we perform an exhaustive search among several filter 
lengths along the estimated direction, and find the best match. 
Eq. 1 and the convolution theorem [3] imply 
FFT(BJ) = FFT(l) X FFT(B) 
FFT(J) = FFT(BJ)/FFT(B) 
[ 1 == IFFT(FFT(J)), (2) 
where x and / represent the Schur product and division ac-
cordingly, and I' is the estimated clear image. We search for 
the blur function B that minimizes Ill - I'll, This is possible 
for simulated data, for which the initial I is known. For real 
data, we only have access to blurred image Bl. We instead 
benefit from the particle size estimation provided via Process 
5b. We then use the simulated clear image for that particle 
size as the clear I image and repeat the same process for find-
ing the blur image B. 
4. EXPERIMENTS 
We performed our velocity determination experiments on 
simulated data that were generated by a commercially avail-
able optical ray tracing and analysis code [4]. We designed 
two sets of experiments: one on clear simulated dust parti-
cles, and the other on simulated data in presence of noise. For 
clear images, we first generated a set of horizontal filters of 
lengths equal to 3, 5, 7, 9 and 11 pixels located in the middle 
of the image frames. We then rotated the filter from 0° to 
360° in 5° intervals. Finally, we convolved the clear image 
with the filter to create a blurry image for each case. That is, 
we generated 365 blurred images. 
""·-·· .. 
. ·-----·--·--
::••;,•:• 
::.: ", :, 
(:;::: .. 
Fig. 4. Blurred 100 µ.m particle signature. 
For noisy images, we repeated the experiments for filters 
oflength 7 and 9 pixels only, and rotated them from 0° to 180° 
in 10° intervals to obtain initial blurred images B10 • Then, 
the final blurred and noisy images B In were generated: 
Bln = Blo + s X Ni X Blo, (3) 
wheres E {[O : 0.01 : 0.1], [0.2 : 0.1 : 2], [2.5 : 0.5 : 5]}, 
and for i E {1 ... 10}, Ni is a random white noise image 
whose values are within [-0.5, 0.5] and have been smoothed 
by averaging values over 9 x 9 boxes. We then estimated the 
direction and length of the velocity vectors of these blurred 
images via algorithms described in Section 3. 
5. RESULTS 
Our extensive experiments on simulated data indicate that as 
the exposure time Oength of the blurring filter) increases, our 
algorithm makes better direction estimations for traveling par-
ticles. Tab. I reports maximum and mean absolute error val-
ues of these results in degrees for simulated data in absence of 
noise. For each filter length, the algorithm was run 73 times 
for filters along 0° : 5° : 360°. While the mean absolute error 
for filter of length 3 is "' 10°, this error is less than 4 ° in all 
other cases. Also, for 73% of all cases, we estimated the exact 
· filter length. Our velocity determination algorithms are sen-
Table 1. Direction estimation for simulated particles traveling 
along 0° to 360° angles, with 5° increments 
Filter Absolute Error in Degrees 
Length Max Mean 
3 32.57 9.56 
5 12.42 3.64 
7 08.71 2.73 
9 05.13 J,99 
II 03,73 1.25 
sitive to noise. Our results for noisy simulated data indicate 
that when s in Eq. 3 is less than or equal to 0.6, the direc-
tion is almost always estimated within 10° absolute error. We 
examined our algorithm by using IO different random noise 
images, Ni, for each scale values. Fig. 5 illustrates how for 
a simulated particle traveling in 40° direction, increasing the 
noise affects the estimation error. 
Bl 
70 
60 
50 
10 
•10 
Fslimated hiAleo '18 ~01.-s Soela ror 1C RllflOOIT' Raaliz.alrons 
Aeh.111tAngla ,.0 Oagree, F1tt11r l,11ng1b 7 
.... ,~ 
.,. 
' 
·L./ 
-~.o'-,-------o"',-------1'L...--~,;-~---13,......,4-!s 
Nci1S8Scal• 
Fig. 5. Direction estimation for noisy simulated particles 
(travel angle: 40°). 
6. CONCLUSION 
We presented particle characterization algorithms that deter-
mine the location, size, and velocity of traveling particles via 
image analysis. We described the particle velocity determi-
nation algorithms. Our experiments on simulated particles 
imply an average absolute error of less than 4° for direction 
estimation of traveling particles when the blurring filter length 
was greater than 3 pixels. Our algorithms performed well for 
simulated data in presence of small amount~ of noise. 
7, ACKNOWLEDGMENTS 
We thank Catherine Walker and Semion Kizhner for their 
work on particle size determination algorithms. 
8. REFERENCES 
[I] B. J. Bos and W. M. Farrell, "Dust flux instrumenta-
tion for mars landers," in 7th International Conference 
on Mars, Pasadena, CA, July 2007. 
[2] N. E. Haung and S. S. P. Shen, Eds., Hilbert-Haung 
Transfonn and Its Applications, ser. Interdisciplinary 
Mathematical Sciences. World Scientific Publishing 
Company, Sep. 2005, vol. 5. 
[3] R. Bracewell, The Fourier Transfonn and Its Applica-
tions, 3rd ed. NY: McGraw-Hill, June 1999. 
[4) R. Fischer, Optical System Design, 2nd ed. McGraw-
Hill, Jan. 2008. 


Characterization of Moving Dust Particles

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