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Instructor: Lichuan Gui lichuan-gui@uiowa lcgui

Students are encouraged to attend the class. You may not be able to understand by just reading the lecture notes. Measurements in Fluid Mechanics 058:180:001 (ME:5180:0001) Time & Location: 2:30P - 3:20P MWF 218 MLH Office Hours: 4:00P – 5:00P MWF 223B-5 HL. Instructor: Lichuan Gui

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Instructor: Lichuan Gui lichuan-gui@uiowa lcgui

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  1. Students are encouraged to attend the class. You may not be able to understand by just reading the lecture notes. Measurements in Fluid Mechanics058:180:001 (ME:5180:0001)Time & Location: 2:30P - 3:20P MWF 218 MLHOffice Hours: 4:00P – 5:00P MWF 223B-5 HL Instructor: Lichuan Gui lichuan-gui@uiowa.edu http://lcgui.net

  2. Lecture 35. Particle image identification & tracking

  3. Particle Image Identification Purposes - To determined image specifics for particle tracking - To analyze particle size and distribution in flows - To separated phases according to image size - To mask unexpected objects in the flow field - To mask flow boundaries - Others

  4. Particle Image Identification Step 1: Preprocess particle images - Increase contrast of the PIV recording - Increase brightness of dark images - Binarize particle images

  5. Particle Image Identification Step 2: Identify particle images (method in EDPIV) - Number every image pixel and give a large number to background pixels - Reconstruct number field with minimum in 3×3 neighborhood of image pixels - Iterate until no changes can be made

  6. F H  Particle Image Specifics - Particle image position (xs,ys) determined at center of mass - Particle size in pixels (F) - Average brightness of all pixels belong to one image (H) - Particle shape coefficient, e.g. roundness =F/(R2max)

  7. Particle Image Specifics - Insect image identification example

  8. 1. Original 2. Binary 3. Big particles 4. Expanded 5. Inverted Create Phase Mask

  9. Original Mask Effect Pasted Mask Effect Painting Tools Paste – help for generating masks

  10. Original Line cut Effect Effect Painting Tools Line - help for identifying big particles

  11. Original Fill boundary with 0 Fill flow field with 250 Painting Tools Fill – Used to create a boundary mask

  12. - Information from particle image identification Recording pair: 1st frame 2nd frame Particle image number: l1=1,2,···, L1 l2=1,2,···, L2 Size in pixels of particle images F1(l1) F2(l2) Average brightness H1(l1) H2(l2) Shape coefficient 1(l1) 2(l2) Position of particle image x1 (l1),y1 (l1) x2 (l2),y2 (l2) - Tracking function Tracking of Big Particle images Tow-frame tracking of big particles - Weighting coefficients: F +H +=1

  13. - Pairing criterion: - Deformation limits: - Particle image displacement: Tracking of Big Particle images Tow-frame tracking of big particles

  14. 1st frame 2nd frame Tracking results Tracking of Big Particle images Example

  15. Tracking of Insect Motion Example: Tracking fire ant in successive image frames Fire ant trace Fire ant speed time history

  16. B One pixel for one particle C Maxima search Erosion B B C C Combine Position of LID particle images

  17. Evaluation with large window Evaluation at identified particles Evaluation of LID recordings • Individual particle image pattern tracking LID recordings LID recordings with small interrogation window

  18. Simulated PIV recording pair of uniform flow Particle image diameter: 24 pixels Particle image displacement: Sx=2.3, Sy=1.5 pixels Particle tracking results RMS error=0.25 pixels Pattern tracking results 4x4-pixel pattern RMS error=0.06 pixels Tracking of small particle images - Nearest displacement criterion (distance between particles >> displacement) - Pairing with help of neighbor particle images (e.g. Okamoto et al. 1995) - Multi-frame tracking (e.g. Hassan & Canaan 1991 ) - Displacement determined according to particle image center positions - Individual particle image pattern tracking recommended for LID PIV

  19. References • GuiL, Merzkirch W, Shu JZ (1997) Evaluation of low image density PIV recordings with the MQD method and application to the flow in a liquid bridge. Journal of Flow Visualization and Image Processing, vol. 4: 333-343  • Gui L and Seiner JM (2009) Application of an Image Tracking Algorithm in Fire Ant Motion Experiment. Algorithms 2, no. 2:735-749

  20. Fast computation of unsharp mask - Compute Gsm(x,y) close to the edges of the image forx  r+1 or x> nx-r or y< r+1 or y>ny-r A

  21. Fast computation of unsharp mask - Compute Gsm(x,y) away from the edges of the image

  22. Fast computation of unsharp mask function [F]=unsharpmask(G,fr) % G - gray value distribution of the image [nxny]=size(G); % fr- radius of the filter G=double(G); for y=1:ny for x=1:nx nr=0; % used to count pixels in effective area A Gm(x,y)=0; for i=-fr:fr for j=-fr:fr if x+i>0 & x+i<=nx & y+j>0 & y+j<=ny% limit calculation in the image Gm(x,y)=Gm(x,y)+double(G(x+i,y+j)); nr=nr+1; end end end Gm(x,y)=Gm(x,y)/nr; % average gray value end end F=double(G)-double(Gm); % remove unsharp mask • y A • ny • x • nx

  23. Fast computation of unsharp mask function [F]=unsharpmask2(G,fr) % G - gray value distribution of the image [nxny]=size(G); % fr- radius of the filter G=double(G); for y=1:ny for x=1:fr+1 nr=0; Gsm(x,y)=0; for i=-fr:fr for j=-fr:fr if x+i>0 & x+i<=nx & y+j>0 & y+j<=ny Gsm(x,y)=Gsm(x,y)+G(x+i,y+j); nr=nr+1; end end end Gsm(x,y)=Gsm(x,y)/nr; end end • r+1

  24. Fast computation of unsharp mask function [F]=unsharpmask2(G,fr) % G - gray value distribution of the image [nxny]=size(G); % fr- radius of the filter G=double(G); for y=1:ny for x=1:fr+1 nr=0; Gsm(x,y)=0; for i=-fr:fr for j=-fr:fr if x+i>0 & x+i<=nx & y+j>0 & y+j<=ny Gsm(x,y)=Gsm(x,y)+G(x+i,y+j); nr=nr+1; end end end Gsm(x,y)=Gsm(x,y)/nr; end End for y=1:ny for x=nx-fr:nx nr=0; Gm(x,y)=0; for i=-fr:fr for j=-fr:fr if x+i>0 & x+i<=nx & y+j>0 & y+j<=ny Gm(x,y)=Gm(x,y)+G(x+i,y+j); nr=nr+1; end end end Gm(x,y)=Gm(x,y)/nr; end end • nx-r • r+1

  25. Fast computation of unsharp mask for y=1:fr for x=fr+2:nx-fr-1 nr=0; Gm(x,y)=0; for i=-fr:fr for j=-fr:fr if x+i>0 & x+i<=nx & y+j>0 & y+j<=ny Gm(x,y)=Gm(x,y)+G(x+i,y+j); nr=nr+1; end end end Gm(x,y)=Gm(x,y)/nr; end end • r • nx-r-1 • r+2

  26. Fast computation of unsharp mask for y=1:fr for x=fr+2:nx-fr-1 nr=0; Gm(x,y)=0; for i=-fr:fr for j=-fr:fr if x+i>0 & x+i<=nx & y+j>0 & y+j<=ny Gm(x,y)=Gm(x,y)+G(x+i,y+j); nr=nr+1; end end end Gm(x,y)=Gm(x,y)/nr; end end for y=ny-fr:ny for x=fr+2:nx-fr-1 nr=0; Gm(x,y)=0; for i=-fr:fr for j=-fr:fr if x+i>0 & x+i<=nx & y+j>0 & y+j<=ny Gm(x,y)=Gm(x,y)+G(x+i,y+j); nr=nr+1; end end end Gm(x,y)=Gm(x,y)/nr; end end • r • nx-r-1 • r+2

  27. Fast computation of unsharp mask nr=(2*fr+1)*(2*fr+1); for y=fr+1:ny-fr-1 for x=fr+2:nx-fr-1 D=0; x1=x-fr-1; x2=x+fr; for j=-fr:fr D=D-G(x1,y+j)+G(x2,y+j); end Gm(x,y)=Gm(x-1,y)+D/nr; end End F=G-Gm; % function output • ny-r-1 • r+1 • nx-r-1 • r+2

  28. Fast computation of unsharp mask Test program clear; A=imread('D001_1.bmp'); % input image file G=img2xy(A); % convert image to gray value distribution fr=5; F=unsharpmask2(G,fr); D=xy2img(F); imshow(D);

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