Isı Bilimi ve Tekniği Dergisi, 37, 2, 61-74, 2017
J. of Thermal Science and Technology©2017 TIBTD Printed in Turkey
ISSN 1300-3615

DYNAMIC MASKING TECHNIQUES FOR PARTICLE IMAGE VELOCIMETRY
F. Gökhan ERGİN
Dantec Dynamics A/S
Tonsbakken 16-18, 2740, Skovlunde, Denmark, gokhan.ergin@dantecdynamics.com(Geliş Tarihi: 01.02.2016, Kabul Tarihi: 10.01.2017)
Abstract: Objects and surfaces often appear in Particle Image Velocimetry (PIV) images. Unless masked, thefeatures on these contribute to the cross correlation function and introduce an error in the vectors as a result of the
PIV analysis in the vicinity of the phase boundary. Digital masking of objects has appeared numerous times in theliterature as part of the analysis chain, with a growing focus on isolating moving features using dynamic masks. One
aim of this article is to provide a summary of milestones achieved in dynamic masking covering a wide range ofapplications. Another aim is to show the difference between image masking and vector masking. Finally, twodifferent dynamic masking examples are described in detail and compared. The examples used are selected fromswimming microorganisms in small channels. In the first example, a histogram thresholding-based dynamic masking
is used, while, in the second example, a novel technique employing a feature tracking-based dynamic masking isused. Results show that histogram thresholding-based masking provides better results for swimmers which randomlychange shape and direction; whereas, feature tracking-based masking provides better results for swimmers which do
not change shape or direction significantly. In order to show the improvement due to dynamic masking, acomparison is made between PIV results a) with no masking, b) with just image masking and c) with both image andvector masking. Results show that the best approach is to use both image and vector masking.
Keywords: Dynamic masking, Image masking, Vector masking, Histogram thresholding based dynamic masking,Feature tracking-based dynamic masking.

PARÇACIK GÖRÜNTÜLEMELİ HIZ ÖLÇME TEKNİĞİ İÇİNDİNAMİK MASKELEME TEKNİKLERİ
Özet: Cisimlere ve yüzeylere Parçacık Görüntülemeli Hız Ölçümü (PIV) imajlarında sıkça rastlanır. Bunlar
maskelenmediği sürece, çapraz korelasyon fonksiyonunu etkiler ve PIV analizi sonucu elde edilen hız vektörlerinde
faz sınırına yakın yerlerde hatalı sonuçlar oluşturur. Cisimlerin dijital ortamda maskelenmesi, özellikle hareketli
cisimlerin dinamik maske kullanılarak imajlardan çıkarılması hesap zincirinin bir parçası olarak literatürde pek çok
kez yayınlanmıştır. Bu yazının bir amacı dinamik maskelemede ulaşılan kilometre taşlarının geniş bir uygulama
yelpazesini de kapsayan bir özetini vermektir. Bir diğer amaç ise dinamik imaj ve vektör maskelemenin farklarını
göstermektir. En son olarak, iki ayrı dinamik maskeleme örneği ayrıntılı olarak anlatılmakta ve karşılaştırılmaktadır.
Kullanılan örnekler küçük kanallarda yüzen mikroorganizmalardan seçilmiştir. İlk örnekte histogram eşikleme ile
dinamik maskeleme ve ikincide ise yeni bir teknik olan, özellik takibi ile dinamik maskeleme kullanılmıştır. Sonuçta,
histogram eşikleme ile maskelemenin yüzme yönü ve şekli rastgele değişen yüzücülerde; özellik takibi ile
maskelemenin ise şekli ve yönü pek değişmeyen yüzücülerde daha iyi sonuçlar verdiği ortaya çıkmaktadır. Dinamik
maskelemenin yararını göstermek için maskeleme yapılmadan elde edilmiş PIV sonuçları, sadece imaj maskelemesi
yapılarak elde edilmiş PIV sonuçları ve hem imaj hem de vektör maskelemesi yapılarak elde edilmiş PIV sonuçları
karşılaştırılmıştır. Sonuçta hem imaj hem de vektör maskelemesinin kullanılmasının daha uygun olduğu ortayaçıkmıştır.
Anahtar Kelimler: Dinamik maskeleme, İmaj maskeleme, Vektör maskeleme, Histogram eşikleme ile dinamik
maskeleme, Özellik takibi ile dinamik maskeleme.
INTRODUCTION
Masking is an important step during PIV processing
and, in many cases, manually-drawn static masks are
sufficient to remove stationary objects from PIV
images. Masking can be a relatively easy process if theunwanted section or object is stationary, however, it

becomes an extremely cumbersome and time-consuming
process if the object is moving. Especially in the case oftime resolved PIV systems,—where thousands of images
can be acquired in an image ensemble—manual maskingof moving objects from each image is simply notpractical. Static digital masking is rather straightforward
and has appeared numerous times in the PIV literature;therefore, any reference to static masking techniques isomitted here, and the focus will be on dynamic
masking.

Size-based separation
The first study using moving masks for phase-separated
PIV processing was by Gui and Merzkirch (1996) in a
bubbly flow experiment. In their analysis, a digital mask
was used to track & separate larger gas phase objects(bubbles) from the smaller seeding particles in the liquid
phase for double-frame PIV recordings. The separation
technique relied on the existence of a significant
difference in size distribution between the bubbles andthe seeding particles. In another bubbly flow
investigation from the same research group, Lindken
and Merzkirch (2000) used a similar digital masking
technique. In this study, a secondary high-speed imaging
system was added to reconstruct the bubble shape andposition in three-dimensions. The light sheets of the twosystems were perpendicular to one another, and the 3D
bubble shapes were reconstructed using the 2D bubble
contours produced by each of the high-speed imaging
systems. The use of this secondary imaging systemallowed phase-separated measurements of simultaneous,
multiparameter information: (i) two-component planar
velocity field, (ii) bubble positions and (iii) 3D bubble
shapes.

With the introduction of high-speed PIV systems,
time-resolved flow field information has become more
readily available. Along with generating large numbersof images, this has also driven a necessity for aneffective dynamic masking process when phaseseparated image processing is required in various
multi-phase flow investigations. There are often
significant velocity gradients across phase boundaries,
which can cause cross-correlation based PIV methods
to fail. Ironically, phase boundaries are often the focus
of investigations where important fluid dynamics
phenomena occur. In interrogation windows which
overlap the boundary, the PIV computations are more
likely to represent the displacements of the phase withthe greater density of particle images. Therefore, it isessential to identify the phase boundary accurately inorder to perform phase-separated PIV evaluations fora more accurate representation of the flow field whereinteresting flow phenomena occur.
To date, many multi-phase flow investigations haveused some form of dynamic masking and these coveralmost all phase combinations: gas in liquid (e.g.
bubbly flow reactors, boiling flows), liquid in gas (e.g.
sprays, free surface flows), liquid in liquid (e.g.
droplet formation, mixing), gas in gas (e.g. combustion
diagnostics, flame front investigations), solid in liquid(e.g. sediment transport, swimming objects), solid ingas (e.g. flapping and flying objects) and solid, liquidand gas (e.g. landslide investigations). Separation ofphases can be accomplished using optical methods,
digital methods, and using additional hardware
components (e.g. a secondary illumination mode,
Lindken and Merzkirch, 2002). Phase separation can,of course, also be achieved using a combination ofthese. Of particular interest—and the focus of thecurrent report—are the digital methods which create
dynamic masks of moving objects by applying a
number of image processing functions using theoriginal image ensemble. Although a number ofdifferent image processing techniques can be used intandem, digital separation methods can be grouped
under three main categories: (i) size-based separation,(ii) greyscale histogram thresholding methods and (iii)boundary detection methods. This study is a firstattempt to summarize existing literature on the three
digital separation methods. Additionally, two
examples are provided of recent dynamic masking
applications from bio-micro-fluidics. The first
application example demonstrates masking of a
uniflagellate
microorganism
using
histogram
thresholding and the second details masking of a
copepod microorganism using a novel feature
tracking-based approach.

Separation of three phases was achieved by Fritz et al.
(2003), who performed digital dynamic masking in alandslide wave tank without using additional hardware
components. First, the seeding particles in the waterphase were separated from the sliding granular matterand air, using image-processing functions. Second, thepixel value fluctuations arising from illumination
intensity were removed using a sliding backgroundsubtraction. Finally, the water phase was isolated fromthe other two phases using digital dynamic masks
similar to techniques used in Lindken & Merzkirch(2000). The ramp and water surface were masked to
avoid biased correlation signals caused by totalreflections and light scattering from floating seeding
particles. This masking technique was used successfullyin un-separated and separated flow conditions, meaning
the mask was able to follow morphological changes inthe two-phase flow.
The size-based separation methods naturally rely on asignificant size difference between the phases. If the size
difference is small, i.e. if the size of the bubbles areclose to the size of the particles, phase separation
becomes quite difficult. Histogram thresholding
methods, in combination with other image processing
functions, have certain advantages in such situations andcan be more effective when applied to other challenging
flow configurations.
Histogram thresholding methods
Lindken and Merzkirch (2002) improved upon theirprevious size-based separation method by adding ahistogram thresholding-based separation approach. They
used the digital mask as an operator in the PIV
62

evaluation algorithm in which the separation of phases
is performed on individual image histograms.
Furthermore, a secondary light source in transmission
mode was used in combination with PIV in order toget distinct background levels between particles andbubble areas. The detailed image processing steps are
implemented such that they can be applied to large
image ensembles in a systematic fashion (automatedmasking).

the use of additional systems or hardware components.
In 2010, Deen et al. (2010) and Hammad (2010)
reported masking of stationary and moving objects using
only image processing functions, i.e. without the use ofadditional hardware components. Deen et al. (2010)
described a combined image processing approach that
deals with uneven illumination and masking, where a
two-phase flow between the filaments of a spiral woundmembrane module was digitally separated. The
approach consisted of (i) intensity normalization to copewith uneven illumination, (ii) background subtraction toremove stationary objects and (iii) image masking to
remove the moving bubbles in two-phase flow. The
removal technique of moving bubbles—which appear as
bright rings on the image—was similar to what wasdeveloped by Seol & Socolofsky (2008). Hammad(2010)
performed
phase-separated
velocity
measurements in a two-phase flow experiment in which
the turbulent bubbly flow was produced by an impinging
water jet on a horizontal air-water interface. Dynamic
masking was performed using a sequence of low-pass,high-pass and morphology digital filters to yieldbackground subtraction, bubble detection and phaseseparation. The liquid-phase containing the seeding
particles was produced first by identifying the areas
occupied by bubbles using median filtering, and then by
subtracting the bubbles from contrast-enhanced images.
Each phase was evaluated separately using an adaptive
correlation-based PIV algorithm.

In the realm of PIV literature, the term “dynamic
masking” was first coined by Sveen and Daziel (2005).
In their approach, dynamic masking allowed
simultaneous measurements of velocity and density
gradients by separating PIV and synthetic Schlieren
signals in a gravity wave tank. In the experiment,
backlit particle shadows were superimposed onsynthetic schlieren images. The particle images were
removed using a 3-step masking procedure. First, abackground subtraction was performed based on pixel
maximum to remove static features on the tank walls.
Second, a histogram thresholding was performed tolocate the particles, and third, an erosion filter wasapplied to increase the imaged particle size. The
resulting image was used for masking the synthetic
Schlieren images. Sveen and Daziel (2005) evenmeasured the performance of their dynamic masking
technique by comparing the RMS signal of a) a nonseeded measurement, b) a seeded measurement
without a mask and c) the seeded measurement withdynamic masking. They observed that the masking
technique reduces RMS errors by 7%.

In general, the dynamic masks generated in the literature
have been accurate down to a single pixel. Wosnik andArndt (2013) applied a slightly different dynamic
masking technique based on a local threshold filter
applied on the interrogation area. The experiment was
performed in a bubbly wake produced by ventilated
supercavitation, where only the velocity fields of the gas
phase in the cavitating flow were obtained by PIV
analysis of the bubbles. In order to retain vectors
associated with bubbles, the original PIV image wasthresholded not pixel-by-pixel, but by the size of the
final interrogation area. The masked and unmasked
regions were defined based on a pixel value summation
over the final interrogation area compared to a defined
threshold value. In other words, if the sum of the pixel
values was above a certain threshold, the mask was“on”, and if below the mask was “off”. Therefore, the1024x1024 original image was converted to a 63x63
binary image mask for 32x32-pixel final interrogation
area and 50% overlap. That approach naturally
produced a mask with coarser resolution.

Another performance assessment was made by Seol &Socolofsky (2008) using a PIV/LIF (Laser Induced
Fluorescence) experimental setup. In this study,
experiments were performed in a bubbly two-phaseflow and made a three-way comparison between a)
optical phase separation, b) digital phase separation(masking) and c) mixed-phase PIV analysis with
vector post processing. As a result of the error analysis
among the data sets, it was found that the vector postprocessing algorithm performed well, but contained
small errors in the fluid-phase velocity field aroundsome bubbles. A five-step image processing chain wasused to identify the bubble signatures for masking: (i)a 3x3 median filter, (ii) histogram thresholding at 5%of peak pixel value, (iii) binarization, (iv) opening /closing filters and (v) a dilation filter were used togenerate the masking algorithm. The binary image
mask was then multiplied by the original image toseparate the bubbles from the fluorescent particles. In
the processed particle image, the missing bubble
regions were filled with an average background pixel
value instead of a zero-pixel value so that the PIV
algorithm is not biased by the sharp edges created by
the masked bubble regions.

Histogram thresholding-based dynamic masking has also
been applied in other challenging applications. Amongthese are (i) the masking of organisms duringlocomotion, (ii) the masking of droplets during break-up
and (iii) the masking of reactants and products in
combustion diagnostics.

Up until 2010, all the histogram thresholding-based
techniques used in bubbly flow experiments relied on
63

Wadhwa et al. (2014) used a dynamic masking
algorithm to investigate the flow field around A.
Tonsa, a sub-millimeter sized microorganism, duringlocomotion. A two-step masking procedure wasapplied to remove the organism from the particle
images: The procedure consisted of a sliding
averaging of the pixel intensity values followed bythresholding to remove those pixels from the analysis
which corresponded to the organism. The masking
process made it “impossible” to get measurements inthe close vicinity of the organism, especially aroundthe swimming appendages. As a result, the masking
parameters had to be adjusted for each recording
manually in order to minimize the loss of useful data.
After every pass of the processing and during postprocessing, outliers were removed using a medianfilter and de-noising. Later Ergin et al. (2015a) applieda feature tracking-based dynamic masking approach toachieve better results using the same raw images. Thisapproach will be described in detail in Section 3.

Stevens et al. (1998) performed phase-separated PIV
measurements in a combustion application. The
experiment consisted of a premixed turbulent methaneair flame in a stagnation plate configuration, where adynamic masking technique was used to improve the
PIV results for interrogation areas located along theflame front. In order to separate the reactant and productzones, local seeding density was used as thedifferentiator. The seeding density is often higher before
combustion, and lower afterwards due to a sudden
expansion of gases. As a result of the seeding denistychange, local mean pixel intensity in the PIV images ishigher in the air-fuel mixture before combustion thanafter. Stevens et al. (1998) were able to determine thethin flame front using the Light Sheet Tomography
technique, which is based on the local intensity of Mie
scattering from the seeding particles. A suitable
threshold applied on the mean pixel values was used toseparate the two zones and a cross-correlation-based
PIV algorithm was applied in each zone separately.

Carrier et al. (2015) used dynamic image and vector
masking successfully in a droplet formation
experiment where the dynamic mask was used to reject
spurious vectors in the non-seeded continuous phase.
The first step was to isolate the features in motion (thedroplet interface and the seeding particles) from thestatic background. For this purpose, the harmonicmean was subtracted from the inverted pixel values of
the raw shadow images. In addition to this, a static
mask was applied to remove residual wall reflections.
Finally a suitable histogram threshold level was
selected so that only the features in motion had a nonzero pixel value. Then a sufficient number of dilations(bright pixels flooding the dark pixels) were
performed to fill the gaps between the particles and theinterface entirely. A natural side effect of this dilationis the ‘fattening’ of the finger, which can be remediedby an equal number of erosions (dark pixels flooding
the bright pixels). The masks generated in Carrier etal. (2015) were successful in following themorphological changes during the droplet formation,
especially during the break-up stage (Fig. 1). A movieof the droplet break-up is available in Ref M1.

The histogram-thresholding techniques seem to be more
frequently applied in the literature; particularly for themasking of multiple medium-sized features in the flow
field, such as bubbles, microorganisms, and droplets.
Due to their flexibility and their ability to handle morechallenging situations, they can even produce successful
results along more complex phase boundaries such as aflame front in combustion applications.
Boundary detection methods
Boundary detection methods are more suitable forseparation of phases across longer, single-phaseboundaries such as flame fronts, surface instabilities
and liquid free surfaces. For example, Coron et al.
(2004) used a boundary detection method for phaseseparation in a turbulent premixed flame, very similar
to what is described by Stevens et al. (1998). Bothgroups were successful in (i) obtaining the phaseseparated instantaneous velocity fields simultaneously,and (ii) improving the accuracy of the PIV
measurements in the vicinity of the flame front.
Additionally, Coron et al. (2004) were able to measure

Figure 1. Snapshot of the flow field at rupture instant during the droplet break-up experiment performed by Carrier etal. (2015). Colors indicate velocity magnitude, max velocity 270 mm/s. Histogram thresholding based dynamic mask isable to follow morphological changes during droplet formation.

64

the flame front contour location. Coron et al. (2004)
also perform a phase separation between fuel andreactants based on the fact that Mie scattering lightintensity from the seeding particles is lower inproducts than in reactants—due to the gas expansion
across the flame front. In this study however, theactive (deformable) contours technique was used forboundary detection rather than a histogram
thresholding method. These deformable contours canbe breifly described as curves or surfaces definedwithin a multi-dimensional domain which can movetoward desired features (usually edges). The
preferred model for their study employed parametricactive contours, which allowed a compact
representation of data. The boundary detection
technique using active contours is an iterative
procedure, in which an initial contour on the image
domain is allowed to deform. Coron et al. (2004)
used a contour obtained by the reliable averaging
windows technique as an initial guess. After the final
flame front contour was obtained, a mask was
produced on one side of the contour at a time with
PIV analysis performed in the fuel mixture phase andthe combustion products phase separately. As aresult, in regions where the interrogation windows
stretch across the flame front, the densely seeded
fuel phase did not influence the correlation functionin the reactants phase.

separation methods including (i) probability of centre,(ii) convex perimeter, (iii) curvature profile and (iv)
Shen’s method. The last three are breakpoint detection
methods and were found to be more efficient than theprobability of centre method for bubbly flows. Theirsuperior performance is due to the fact that they searchfor connecting points of outlines of individual objects on
the perimeter of the segment. Among all four methods
the curvature profile method located the connecting
points most accurately and most reliably, and it was also
the least sensitive to noise. It should be noted that thesefindings only apply for bubbly flows where smooth
contours are normally encountered during PIV
recording.
As can be seen in the literature, there are a great numberof different masking approaches used by researchers,
and it is quite difficult to universally comment whether
one approach is better than the other. In the current
work, the aim is not to name the best masking techniquethat works in every scenario. On the contrary, based on
previous studies, the dynamic masking approach should
be tailored for each application. Regardless of theapproach being used, mask definition is the same;
information from one or more phases must be
suppressed while leaving information from theremaining phase(s). In the next section the mask
definition for image masking and vector masking ispresented.

Another application where boundary detection
methods prove to be more suitable are flows withfree liquid surfaces. Sanchis and Jensen (2011) used
Radon transformation for automatic boundary
detection and performed phase separation in a
stratified two-phase flow through a circular pipe.
The boundary was identified by the seeding particles
floating at the free surface, which appeared as a longconnected dotted line in PIV recordings. The Radontransform is a mathematical tool well suited for thedetection of linear features in noisy images and iscommonly applied in computer vision. This
technique tends to suppress pixel intensity
fluctuations due to noise by the process of
integration. A normalization procedure was included
to take into account the aspect ratio of rectangular
input
images.
Duringimage
processing,segmentation was necessary and an increased
number of image segments were used in areas withincreased curvature. Hermite cubic interpolation
betweenadjacent
segments
allowedthereconstruction of the interface piece by piece acrossthe entire image. This technique was able to track theinterface with an accuracy of +/- 0.67 pixels underworst-case scenarios; i.e. in low particle density andwith a noisy background.

Mask definition
The goal of dynamic masking is to get a specific pixel
value (such as “0”) on the object that is to be masked
and retain the pixel value information everywhere else
for each image in the ensemble. This can be achieved ina 3-step procedure. In step one; a new image ensemble
is produced by filtering, thresholding etc. to obtain apixel value of 0 on the object and 1 everywhere else
(Gui and Merzkirch 1996). In step two, each time stepof the new image ensemble is multiplied by thecorresponding time step of the original ensemble. If theoriginal image background pixel value is nonzero, thisresults in a sharp pixel value difference between themask and the image background. As a remedy, in step
three, background subtraction techniques can be used toobtain a 0 pixel value in the masked ensemble’s
background, or the masked area can be padded with theaverage pixel value of the background (Seol &Socolofsky 2008). Finally, the mask ensemble can beapplied to mask either the raw images (image masking)and/or the PIV results (vector masking). In short, thepreferred image masking definition here is following
Gui and Merzkirch (1996), where the mask is defined asa binary image and it is applied on the raw images using
pixel-by-pixel image multiplication:
Δ (i,j)= 0 if the pixel, p(i,j) belongs to the phase that is
to be masked (Phase 1)
Δ (i,j)= 1 if the pixel, p(i,j) belongs to the phase that is
subject to PIV evaluation (Phase 2).

Honkanen and Saarenrinne (2003) provided a good
overview of other digital object separation methods
and their application in PIV analysis of turbulent
bubbly flows. They described four different object
65

A typical phase-separated PIV evaluation of a twophase flow starts with image masking of phase 1 andPIV evaluation in phase 2. Then the binary mask isinverted to mask phase 2 using a simple pixel
inversion and PIV evaluation is performed on phase 1.
An additional vector masking step is usually applied
after each evaluation in order to clean up the vectors in
the masked phase close to the phase boundary (seeFig. 6). The vector masking definition is similar toimage masking:Δ (i,j)= 0 if the vector V(i,j) belongs to the phase thatis to be masked (Phase 1)
Δ (i,j)= 1 if the vector V(i,j) belongs to the phase thatis subject to PIV evaluation (Phase 2)

Flagellum

Photoreceptor

Nucleus

Finally, both vector maps are merged to represent thetwo-phase flow field. For multiphase flows wheremore than 2 phases are present, the same approach canbe used a number of times until each phase isrepresented in the flow field. In some cases, one of thephases could be a solid boundary in motion (e.g.
flapping wing, rotating vane etc.) where PIV
evaluation may not be necessary. In the following two
sections, different dynamic masking strategies aredescribed using the above mask definition. In the
Section 2, flow around a uniflagellate swimmer,
Euglena Gracilis (E. Gracilis), is isolated using
histogram thresholding-based dynamic masking. In the
Section 3, flow around a breaststroke swimmer,
Acartia Tonsa (A. Tonsa), is isolated using a novel
tracking-based dynamic masking technique. Recently,
similar dynamic masking procedures for E. Gracilis
and A. Tonsa have been reported in Ergin (2015) andin Ergin et al. (2015a), respectively. The current work
uses the same two raw particle image ensembles, but
provides improved comparisons and descriptions(including flow charts for data analysis) of masking
and velocimetry. In the case of E. Gracilis, theimportance of dynamic masking is demonstrated byproviding a three-way comparison between PIV resultswith a) no masking, b) image masking and c) vector
masking. Similarly for A. Tonsa, a comparison
between unmasked and masked PIV results isprovided.

Chloroplast

Figure 2. Schematic of E. Gracilis. Flagellum is added
manually as it cannot be resolved by the optics. The
photoreceptor, nucleus and chloroplast are visible under the
microscope.

The experimental measurement setup used in thismasking example is a MicroPIV system manufactured
by Dantec Dynamics. The system components include
an inverted fluoresence Microscope (HiPerformance), a
sensitive CMOS detector (SpeedSense M310), a
synchronization device (80N77 Timer Box), and apulsed LED illumination system (Microstrobe). In
biological flows, high-power pulsed laser illumination isoften not preferred as this can disable the organism or
influence its normal locomotion behaviour. For thisreason, a lower-power LED-based pulsed illumination
was used in backlit transmission mode which produced
shadow particle images. 1µm-diameter seeding particles
were introduced in small quantities until a sufficientseeding density was achieved for PIV. The seeding
density was kept at a low level in order to avoid achange in normal swimming behaviour. The particle
images were recorded at a frame rate of 12.5 fps, with aresolution of 1280 x 800 pixels. The images wereacquired using a 40x magnification objective, producing
a 0.64 mm x 0.4mm field of view (FoV). Later a smaller
region of interest (ROI) is extracted with a resolution of
323 x 529 pixels, corresponding to 162µm x 265µm inthe object space. Single-frame image acquisition was
performed with a constant time difference of 80ms.
Since the FoV was relatively small (~0,25mm2), it wasoften necessary to wait until an organism swam throughthe FoV with the measurement system in operation. The
images were continously acquired and stored in a ringbuffer with the acquisition stopped manually after theorganism had passed through the FoV. 83 consecutive
frames were analyzed to produce 82 flow fieldmeasurements. Total recording time was 6.56s. Further
details can be found in Ergin (2015).

HISTOGRAM THRESHOLDING-BASEDDYNAMIC MASKING
The microorganism E. Gracilis is known to use asingle whip-like structure, called flagellum, in
combination with rolling, stretching and contracting its
flexible body. A schematic of this mono-flagellate isshown in Fig. 2. Its body length without the flagellum
can vary between 20µm and 100µm. The flagellum islocated at the end close to the photoreceptor, and thecell nucleus is centrally located in its body. Althoughimaging the flagellum is challenging, thephotoreceptor and the cell nucleus can be detectedeasily under a microscope (Fig. 2).

66

(a)

(b)

(c)

(d)

Figure 3. E. Gracilis during locomotion in water with 1-µm diameter seeding particles at t=1.92 s. (a) Raw image before
background subtraction (b) after pixel inversion and background subtraction (re-inverted) (c) dynamic mask (d) particle image
after image masking (re-inverted)

Fig. 3a shows the raw PIV recording of a 47,5µm-tall
E. Gracilis in the sparsely seeded flow. In the acquired
ensemble, E. Gracilis meanders upwards throughfiltered water; covering a net distance ofapproximately 256µm (Fig. 4). Use of filtered waterprovides a cleaner, foreign-object-free background in
the raw images and allows more precise seeding
density adjustments. Furthermore, other marineorganisms (predator or prey to E. Gracilis) that canchange E. Gracilis’ normal locomotion behaviour are
excluded.

subtraction is performed using the minimum pixel value
found in the inverted ensemble (Fig. 3b). Next, ahistogram thresholding-based dynamic mask is produced
using the ensemble in the second step (Fig. 3c) andfinally image masking is performed (Fig. 3d). A flow
chart describing the complete analysis chain for dynamic
masking of E. Gracilis is shown in Fig. 5.

5 ,7 6 ss
4 ,8 0 ss
3 ,8 4 ss
2 ,8 8 ss
1 ,9 2 ss
0 ,9 6 s

Figure 5. Flow chart describing the analysis chain for
dynamic masking of E. Gracilis.

In the current study, the following image processing
chain produced an acceptable dynamic mask: a 9x9
median filter, a closing filter with 10 iterations,thresholding (min:125 max:4096), pixel inversion,
thresholding (min:3970 max:4096), erosion filter with 2
iterations, thresholding (min:0 max:1). The final
thresholding step produces the binary image mask. In
order to demonstrate the quality of the dynamic mask,
position of E. Gracilis (Fig. 4a) and the used mask (Fig.
4b) are shown side by side at selected time steps. One
immediate observation is that the mask is slightly larger
than the organism. This is intentional in order to keep asmall margin around the masked object, and the margin
thickness can be controlled by the number of erosions
and dilations. Another observation is that the used mask
may produce non-ideal results around image boundaries(see for example the top of Fig. 4b at t=5,76s). Apart

t=0s

(a)

(b)

Figure 4. Quality of dynamic masking (a) Recorded position(b) dynamic mask of E. Gracilis every 0.96s.

The first step in image pre-processing is pixel
inversion in order to work with positive particle
images rather than particle shadows. Althoughworking with (inverted) positive particle images
during processing, it is preferable to present (reinverted) shadow particle images for better visibility(Fig. 3b and 3d). In the second step, a background67

from these, the dynamic mask captures the position
and the shape of E. Gracilis in a successful fashion.
For velocity calculations, an adaptive PIV algorithm isused, which is an advanced particle displacement
estimator implemented in DynamicStudio (Dantec
Dynamics, Skovlunde, Denmark). Briefly, theimplementation is a cross-correlation based, adaptive
and iterative procedure employing vector validationand deforming windows: First, the displacement iscalculated on an initial interrogation area (IA), whichis larger in size compared to the final IA. In each step
the IA is shifted by the displacement calculated in theprevious step. For the case of E. Gracilis the final
interrogation windows of 32x32 pixel are used with
75% overlap. Window deformation is performed byadapting the IA shape to velocity gradients, with
|du/dx|, |dv/dx|, |du/dy|, |dv/dy| < 0.25.

( a)

Several passes can be made to further shift & deformthe windows to minimize the in-plane particle dropout.
For each IA size, this procedure is repeated until aconvergence limit in pixels or a maximum number of
iterations is reached. Then a 9-point, two-dimensional
Gaussian fit is performed on the highest correlation
peak to obtain the displacement field with subpixel
accuracy in each pass. A number of FFT window(Hanning, Hamming etc.) and filter functions can beapplied during the analysis. Between passes, spurious
vectors are identified and replaced with a number ofvalidation schemes including peak height, peak heightratio, SNR & Universal Outlier Detection (UOD)(Westerweel and Scarano 2005). Following
Westerweel and Scarano (2005), UOD is performed
between passes in a 5x5 neighbourhood with 0.1
minimum normalization level and a detection
threshold of 2.0. The vectors are considered valid if
the peak height ratio is larger than 1.25. In otherwords, the displacement calculation is considered
reliable when the highest peak (the assumed signalpeak) is at least 1.25 times higher than the second
highest peak (assumed to be noise) in the crosscorrelation function. This is certainly not the only
vector validation method, but it is one of the oldest. A
threshold value of 1.2 is often used in the literature, soin this respect the present threshold value of 1.25 ismore conservative. The subpixel positioning accuracyof the Adaptive PIV algorithm is reported as 0.06
pixels with 95% confidence (Ergin et al. 2015b). The
0.06 pixels correspond to a 27.5nm displacement in
the object space, and the velocity uncertainty is
estimated as 0.34µm/s. An average filter in a 5x5
neighbourhood and vector masking is applied after
Adaptive PIV computations.

(b )

( c)

A close up of the flow field around E. Gracilis at
t=1.92s is shown in Fig. 6. In this figure, vectors
represent the u and v components of the flow field andcolors represent the magnitude of local velocity, where
blue areas represent stagnant flow regions. Figure 6

Figure 6. Flow field around E. Gracilis at t=1.92s.
(a) Without masking, (b) with image masking only, and(c) with both image masking and vector masking.
Max velocity is 12µm/s in b and c.

68includes three subfigures in order to make a three way
comparison of the flow field first without theapplication of masking (Fig. 6a), second with image
masking but without vector masking (Fig. 6b), andthird with both image and vector masking (Fig. 6c).
The raw particle image is also included in Fig. 6c inorder to show the position of E. Gracilis with respect
to the flow field.
It is clear from Fig. 6a that without any masking theflow field in the immediate vicinity the organism iscontaminated by the upward motion of the organism
itself. This is simply because the features on theorganism produce a strong correlation peak ininterrogation areas that overlap the organism and thefluid around it. Application of image masking (Fig.
6b) improves the situation significantly for the liquid
phase, but this time some erroneous vectors areregistered on the organism; vectors indicate adownward motion while the organism swims upwards.
These spurious vectors can be cleaned with theapplication of vector masking (Fig. 6c), which leaves
us with the flow field around E. Gracilis where theinformation is only coming from the liquid phase. The
flow field reveals that the fluid is drawn towards theorganism upstream and downstream, and fluid isexpelled from the organism on the sides. The
downstream flow field can be explained as the wake inthe aft of the swimmer, and the upstream flow field is
produced most likely by the flagellum pulling a stroke,
the main source of propulsion. Due to continuity
around the organism, the fluid is expelled outwards
from the sides. This flow field also produces four
small vortices, one at each corner of the image, i.e. due
Southwest, Southeast, Northwest and Northeast of theorganism.

Figure 7. Schematic of A. Tonsa nauplius at the beginning ofa power stroke. From Ergin et al. (2015a).

vicinity of the organism, especially around theswimming appendages. As a result, Wadhwa et al.
(2014) had to adjust the masking parameters for eachrecording manually in order to minimize the loss ofuseful data. Later Ergin et al. (2015a) made some
improvements on the masking strategy of Wadhwa et al.
(2014) and provided some phase-locked averaged
results. In the current study, the same particle image
ensemble is used, but a more effective approach for bothmasking and velocimetry is employed. Although the newstrategy is not successful in masking the swimming
appendages, it enables more accurate measurements inthe close vicinity of the organism without having toadjust masking parameters manually for each image andwithout having to apply phase-locked averaging. In thecurrent study, an improved tracking algorithm is used,which tracks both the horizontal and the vertical positionof A. Tonsa, whereas, Ergin et al. (2015a) performed
tracking only in the vertical direction. Since A. Tonsa ismoving slightly to the left (see Fig. 11), a larger mask
was used in Ergin et al. (2015a). Second, the analysis
consists of 16x16 final IA size with 50% overlap
followed by a UOD scheme with a detection thresholdof 0.5. This enabled the comparison of masked andunmasked flow fields at any instant, without resorting tophase-locked averaging.

The histogram thresholding-based dynamic masking
example described above proves to be quite powerfulas it is able to tackle several important challenges
encountered in the image ensemble: uneven
illumination (Fig. 3a), random object trajectory,
random object shape, and random object velocity (Fig.
4). Although quite powerful, histogram-thresholding
based dynamic masking strategies may not work forcertain applications. In the following section, thefeature tracking-based dynamic masking strategy wasused, which proved to be more successful for theapplication.
FEATURE
MASKING

TRACKINGBASEDThe experimental setup for the second application
example is described in Wadhwa et al. (2014) and issummarized here briefly: Copepods A. Tonsa were
cultured at 18°C and were transferred before
experiments to the test aquarium containing filteredseawater. Only a few specimens were transferred inorder to avoid possible interactions between them. The
test aquarium is a glass cuvette (10x10x40mm) placedon a horizontal stage and kept at room temperature,
between 18°C and 20°C.

DYNAMIC

Recently the hydrodynamics of a ~220-μm-long A.
Tonsa (Fig. 7) nauplius were analyzed in Wadhwa etal. (2014) using time-resolved MicroPIV/PTV(Particle Tracking Velocimetry), in which a two-stepmasking technique was applied to remove theorganism from the particle images.

The experimental measurement setup is a long-distanceMicro Particle Image Velocimetry (LDµPIV) system
where the light sheet propagation direction and thecamera viewing direction are perpendicular. As
described in the previous application, high-power visible
laser illumination is often not preferred in biological
69

(a)

(b)

(c)

Figure 8. (a) First, (b) 33rd (middle), and (c) 65th (last) frames of the raw particle image ensemble. Boxes show the approximate
borders of the ROI extracted around the organism.

flows. For this reason, a low-power, continuous-wave
infrared laser (Oxford Lasers Ltd, 808nm wavelength)
was used. Sheet forming optics were assembled to
produce a 150µm thick light sheet, defining themeasurement depth of the experiments. TiO2 seeding
particles smaller than 2µm were introduced in small
quantities until a sufficient seeding density wasachieved. The particle images were recorded on ahigh-speed CMOS detector (Phantom v210, Vision
Research Inc.) at a resolution of 1280 x 800 pixels.
Single frame image acquisition was performed with aconstant time difference of 500µs between frames,
corresponding to 2000fps. The images were acquiredwith 11.65x magnification producing a 2.2mm x
1.4mm FoV (approx 3mm2). Once again, the images
were acquired and stored continuously in a ring buffer
and the acquisition was stopped manually after theorganism had passed through the FoV. Consecutive
frames were used for two-frame PIV processing - quite
typical for time-resolved PIV measurements: 65
frames were analyzed to produce 64 flow fieldmeasurements. Further details can be found in Ergin etal. (2015a).

the ensemble. The idea behind this tracking technique isto move to an object-fixed coordinate system in which
the microorganism is fixed and the surrounding fluid is
in motion. When the object is fixed, conventional static
masking techniques can be applied on the images and/oron the calculated vectors. This was achieved in threesteps: First, a feature was defined using the organism’s
image in the first frame (Fig. 8a). It was possible toidentify the organism throughout the ensemble becausesome of the seeding particles were stuck on theorganism (Fig. 9). For this reason, the tracked feature
was selected as the body of the organism, excluding theappendages.

The first, middle and last frame of the image ensemble
are shown in Fig. 8, where a ~0.22mm tall A. Tonsa is
in motion. During the experiment, A. Tonsa propels
itself upwards through filtered seawater by pulling
three breaststrokes and covering a distance ofapproximately 650µm (Average swim speed approx.
20mm/s). It is observed that A. Tonsa moves in analmost-vertical straight line and its angular orientation
does not change significantly (Fig. 8 and 10).
Subsequent image analysis includes feature tracking,image masking, velocity field calculation, vector
masking and image masking.
Several different histogram thresholding-based
dynamic masking strategies proved unsuccessful in alaboratory-fixed coordinate system; i.e. the “real-life”
situation where the fluid is stationary and themicroorganism is in motion. Since the microorganism
did not rotate around its axis or change shape andmoved in a relatively straight trajectory, a pixelaccurate, cross-correlation based tracking method was
implemented in order to track its position throughout

Figure 9. Close up of the A. Tonsa showing particles stuck on
its body.

70

(a)

(b)

(c)

Figure 10. Correlation of the organism with itself in the (a) first, (b) 33 rd (middle), and (c) 65th (last) frames.

Second, a new pixel-accurate tracking algorithm was
implemented in Matlab, which locates the peak of thecross-correlation function between the defined featureand the entire image (Fig. 8). This essentially searches
for the defined feature within the entire image. The
algorithm is only pixel accurate because no subpixel
fitting was performed during the computations. Figure
10 shows the calculated cross correlation function for
the first (Fig. 10a), the middle (Fig. 10b) and the last(Fig. 10c) frames in the ensemble, and the position of
the cross correlation peak can be compared to theposition of the organism in Fig 8a, 8b and 8c. The
maximum pixel value of the cross correlation functionin the ensemble reveals the nearly-linear trajectory of
A. Tonsa (Fig. 11).

50% overlap for PIV processing. Here, the uncertaintyof 0.055 pixels corresponds to a 94.4 nm displacementin the object space, and the velocity uncertainty is
estimated as 189 µm/s; i.e. 1% compared to the averageswim velocity. A flow chart describing the analysis
chain for dynamic masking and PIV analysis of A.
Tonsa is shown in Fig. 12.

Figure 12. Flow chart describing the analysis chain for
dynamic masking of A. Tonsa.

The organism’s swim velocity history could bemeasured by probing a vector in the far field, upstreamof or beside the organism. A time history of this vector
showed that the nauplius is an almost perfectly periodic
swimmer, and that three full breaststroke cycles wererecorded (Ergin et al. 2015a). Figure 13 shows the flow
field around the organism during the power stroke in thevicinity of the maximum swim speed. This figure
includes two subfigures in order to make a comparisonof the flow field without tracking and masking (Fig.
13a), and with tracking-based image and vector masking(Fig. 13b) at the same time instant. In Fig. 13b, theinstantaneous swim velocity value is subtracted to show
the flow field details around the organism. Similar to thecase for E. Gracilis, it is clear from Fig. 13a that,without any masking, the flow field in the immediate
vicinity of the organism is contaminated by the upward
motion of the organism itself. This error is due to thefact that the features on the organism produce a strong
correlation peak in interrogation areas which overlap
both the organism and the fluid surrounding it.
Application of image and vector masking (Fig. 13b)
provides a cleaner picture where the flow field
information around A. Tonsa is only extracted from theliquid phase.

Figure 11. A. Tonsa’s nearly linear trajectory during theexperiment.

Third, once the organism location was established on
all frames, a constant-size ROI, (576x384 pix) was
extracted around it (Fig. 8). The vertical ROI
dimension (384 pixels) was the maximum value which
could be used in all frames. The limitation was due tothe first and the last frames, in which the imaged
distance fore and aft of the organism must stay within
the FoV throughout the ensemble (Fig. 8a and 8c). The
horizontal ROI dimension (576 pixels) was a value
which fixed the organism approximately in the centerof the ROI horizontally, and reached sufficiently far
into the flow field. This three-step procedure fixed thecoordinate system on the organism and allowed theapplication of a conventional static masking procedure
to remove the organism (image masking) and,eventually, the spurious vectors on the organism(vector masking). In this application, final
interrogation windows of 16x16 pixel were used with71

actual phase boundaries and warrants some future
investigations into the quantification of mask
performance.
It was shown in both application examples that masking
improves the accuracy and understanding of the flow
field, by removing the swimming object from theanalysis. Furthermore, it is shown in the case of E.
Gracilis that vector masking should almost always be
accompanied with image masking for a better
representation of flow around the organism. It is
described here as “a better representation” of the actual
flow field, because the accuracy can still be improved.
For instance, there are often sharp velocity gradients inthin boundary layers and the size of the interrogation
window where the cross-correlation is applied isrelatively large. In such situations, the displacement
estimation is often biased towards the faster moving
fluid particles which are located away from the wall in
the correlation window. Vector repositioning, wall
windowing or particle tracking techniques can further
improve the accuracy close to these boundaries.
Achieving improved accuracy using dynamic masking in
conjunction with the above mentioned boundary
techniques will be the subject of future investigations.

(a)

(b)

It is clear that the use of image processing functions is
key for dynamic masking. Some image processing
functions are more preferrable than others in theliterature—median, opening, closing, erosion, dilationand threshold filters—and are widely used for histogram
thresholding-based masking algorithms. Since thenumber of experimental conditions is infinite, it isessential to use these flexible image-processing
functions to generate appropriate image masks.
Additionally, manual mask generation is a userdependent process and should be avoided if possible.
Instead, masking should be performed based on
algorithms in a systematic and traceable fashion.
Another recommendation is to work with positive
particle images during algorithm development, which
can be achieved by a simple pixel inversion in caseswith particle shadows.

Figure 13. Flow field during the power stroke at t=3.5ms,(a) without masking (b) with image and vector masking.
Swim speed at this instant is ~40mm/s. Both subfigures are a
result of 16x16 final IA size, 50% overlap followed by UOD
scheme with a detection threshold of 0.5.

In both masked and unmasked results, the wake behindthe organism and two vortices are detected; one oneach side of the organism and each with opposite
rotation directions. The counter-rotating vortex system
is an indication of a toroidal (ring) vortex system inthree-dimensions which is in agreement with theprevious findings in Wadhwa et al. (2014). The spatial
dimension of the toroidal vortex is similar to the lengthof the organism. The observed toroidal vortex ring ismore clearly visible in Fig. 13b when compared to Fig.
2 in Wadhwa et al. (2014). This is primarily due to theimproved image processing functions used here. The
masking process successfully removes the contributionof moving particles stuck on the organism.

Based on the results provided here, and in the author’s
opinion, a histogram thresholding-based dynamic masking
is recommended if the object / surface is deforming andchanging direction. On the other hand, feature trackingbased dynamic masking should be a better choice fordynamic masking if the object is rigid and does not rotate
within the FoV. In practice, this is often not the case,because the object is either deforming or rotating. In order
to cope with these situations, future investigations will
focus on more advanced feature-tracking techniques
where the tracked feature is changing shape and/or
orientation from one frame to the next.

DISCUSSION, RECOMMENDATIONS &FUTURE WORK
Masking will continue to be an important step in PIV
analysis
becauseit
allowsphase-separated
measurements which improve the velocity accuracy
along phase boundaries. Phase boundaries arelocations where many interesting flow phenomena
occur, such as velocity gradients, flow separations, gas
expansions, impinging flows and many others.
Ironically, this is where masking algorithms often failand generate the most measurement uncertainty. This
necessitates the evaluation of mask quality along theCurrently, no automated dynamic masking technique isreported which is capable of working globally for every
application. There is a general demand for a robust72technique that performs automatic object recognitionand phase separation, both for double-frame and fortime-resolved acquisitions. This warrants future
investigations which focus on a hybrid technique(histogram thresholding + boundary detection +tracking) for automated dynamic masking withminimum input from the user. Edge detection methods
are few and far between in the literature, which may
indicate more possibilities for better algorithms. In
particular, the actively deforming contours technique isinteresting for investigation because of its applicability
to both smooth and rough contours.

Deen NG, et al. On image pre-processing for PIV of
single-and two-phase flows over reflecting objects. Exp
Fluids, 2010, 49.2: 525-530.
Ergin FG “Flow field measurements during
microorganism locomotion using MicroPIV anddynamic masking” Proc. 11th Int. Symp. on PIV, Santa
Barbara, California, September 14-16, 2015
Ergin FG, Watz BB and Wadhwa N “Pixel-accurate
dynamic masking and flow measurements around small
breaststroke-swimmers using long-distance MicroPIV”
Proc. 11th Int. Symp. on PIV, Santa Barbara, California,
September 14-16, (2015a)

One final remark can be made on the effect ofmeasurement plane thickness and object thickness on
the accuracy of the masking techniques described here.
(The thickness of the measurement plane is defined by
the depth of field of the imaging system in the case for
E. Gracilis and by the thickness of the light sheet in thecase for A. Tonsa.) If the thickness of the object wasmuch smaller than the measurement plane thickness,some particles may have been present in the illuminatedzone between the object and the objective and could
potentially be registered on the PIV images. This would
have added some noise on top of the object image andmay have had a negative influence on the accuracy forboth masking techniques. Fortunately, in both dynamic
masking applications presented here, the imaged objects
are are thicker than the measurement plane, so noisefree object images were recorded.

Ergin FG, Watz BB, Erglis K and Cebers A “Timeresolved velocity measurements in a magnetic
micromixer” Exp. Therm. Flu. Sci. (2015b) 67, pp. 6-13
Fritz HM, Hager WH and Minor HE ”Landslide
generated impulse waves. 1. Instantaneous flow fields”
Exp Fluids 35 (2003) 505-519
Gui L and Merzkirch W (1996) Phase-separation of PIV
measurements in two-phase flow by applying a digitalmask technique. ERCOFTAC Bulletin 30: 45-48
Hammad KJ. “Liquid jet impingement on a free liquidsurface: PIV study of the turbulent bubbly two-phaseflow.” In: ASME 2010 3rd Joint US-European Fluids
Engineering Summer Meeting collocated with 8thInternational
Conferenceon
Nanochannels,
Microchannels, and Minichannels. American Society of
Mechanical Engineers, 2010. p. 2877-2885.

ACKNOWLEDGMENTS
The author wishes to thank Dr. Giovanni Noselli forproviding the culture for E. Gracilis, Dr. Navish
Wadhwa for providing the raw images of A. Tonsa,
Dr. Denis Funfschilling for providing the raw images
for Figure 1, Mr. Bo Watz for tracking software
implementation, and Dr. Samuel Hellman forreviewing this manuscript.

Honkanen M and Saarenrinne P “Multiphase PIV methodwith digital object separation methods” 5th International
Symposium on PIV, Busan Korea, Sept 2003

REFERENCES

Lindken R and Merzkirch W (2000) Velocity
measurements of liquid and gaseous phase for a systemof bubbles rising in water. Exp Fluids 29, 194-201

Carrier O, Ergin FG, Li HZ, Watz BB and Funfschilling
D “Time-resolved mixing and flow-field measurements
during droplet formation in a flow-focusing junction” J.
Micromech. Microeng. (2015) 15 084014

Lindken R and Merzkirch W (2002) A novel PIV
technique for measurements in multiphase flows and its
application to two-phase bubbly flows. Exp Fluids 33,
814-825

M1 - Movie of the droplet break-up experiment
performed by Carrier et al.
http://www.dantecdynamics.com/microfluidicscategory/time-resolved-velocity-measurements-ofdroplet-formation-in-a-flow-focusing-junction

Sanchis A and Jensen A “Dynamic masking of PIV
images using the Radon transform in free surface flows”
Exp in Fluids 2011 51(4) 871-880
Seol DG and Socolofsky SA (2008) Vector postprocessing algorithm for phase discrimination of twophase PIV. Exp Fluids 45:223

Coron X, Champion JL and Champion M: Simultaneous
measurements of velocity field and flame front contour
in stagnating turbulent premixed flame by means of
PIV. In 12th International Symposium on Applicationsof Laser Techniques to Fluid Mechanics, 2004.

Stevens EJ, Bray KNC and Lecordier B “Velocity andscalar statistics for premixed turbulent stagnation flames
using PIV” Proc. 27th International Symposium on
Combustion (1998) pp. 949-955.
73

Sveen JK and Dalziel SB “A dynamic masking
technique for combined measurements of PIV andsynthetic schlieren applied to internal gravity waves”
Measurement Science and Technology, (2005) 16,
1954-1960
Wadhwa N, Andersen A and Kiørboe T“Hydrodynamics and energetics of jumping copepod
nauplii and copepodids” J. Exp. Biol. (2014) 217,
pp.3085-3094
Westerweel, J. and & Scarano, F. (2005). Universal
outlier detection for PIV data. Experiments in
Fluids, 39(6), 1096-1100.
Wosnik, M., and Arndt, R. E. A., (2013),"Measurements in High Void-Fraction Bubbly Wakes
Created by Ventilated Supercavitation," ASME J.
Fluids Eng. 135(1), pp. 011304-011304-974