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Imageprocessing

Imageprocessing. An introduction. What is image processing?. image analysis patron recognition graphical manipulation datacompression data transmission multi media applications. 2. Global Image operation. Histogram Stretching Histogram Equalization Binarization/ Thresholding

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Imageprocessing

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  1. Imageprocessing An introduction

  2. What is image processing? • image analysis • patron recognition • graphical manipulation • datacompression • data transmission • multi media applications

  3. 2. Global Image operation • Histogram • Stretching • Histogram Equalization • Binarization/ Thresholding • Math on images

  4. Histogram

  5. Histogram with MATLAB %y=imread('zand.jpg'); zon=zongray('mushroom2.jpg'); %zon equals contents of 'picuter' arraywaarde=zeros(1,256); % make an empty array[l,b]=size(zon); % measure picture size figure(1); % make a new picture image(zon); % show picture colormap(gray(256)); % set gray colormap for i=1:l % Go for every pixel from 1 to for j=1:b % Take care MATLAB arrays cannot start with 0! a=double(zon(i,j)); % Convert pixelvalue to double calculating with pixelvalues waarde(a+1)=waarde(a+1)+1; % if value is certain value add 1 for that value end end figure(2); % Make new (second figure) bar(waarde); % Give a bargraph of the result

  6. Stretching

  7. Stretching(2) y=(x-64)*4

  8. 3. Local Operations • Smoothing • Low pass filtering • Edge detection • Directional edge detecting • Min-max operation • Sharpening • Special filters

  9. Local operation • Make a new image depending on pixels in the neigtbourhood • filtering.gif

  10. Smoothingwith mean filter filtering.gif

  11. Smoothing with Gaussian Low pass

  12. Edge detectionwith Laplacian operator

  13. Edge detectionwith Laplacian operator(2) L[f(x,y)] = d2f / dx2 + d2f / dy2 d2f / dx2 = f(x+1, y) - 2f(x, y) + f(x-1, y) d2f / dy2 = f(x, y+1) - 2f(x, y) + f(x, y-1) L[f(x,y)] = -4f(x, y) + f(x+1), y) + f(x-1, y) + f(x, y+1) + f(x, y-1) (approx.)

  14. Directional Edge Detection

  15. demo Filters.exe

  16. 4. Morphologie • Erosion • Dilitation • Opening / closing • Conditional erosion • Skeleton

  17. Erosion and Dilation8 and 4 connect influence 8-connect 4-connect

  18. Erosion

  19. Dilation

  20. Erosion 8-connected

  21. Dilation 8 connected

  22. Opening and closing

  23. Erosion and Dilation with thresholdthreshold=1 (at least 8 must be there)

  24. Erosion Dilation applications • Opening and closing. (For correct counting) • Deletes noise pixels • Makes connection at border lines • Skeleton • Perimeter determination

  25. Conditional Erosion • Keep the last pixel • Keep connectednes • Keep the end-pixel of a string of pixels with 1 pixel

  26. Keep the last pixelReduction to 1 point

  27. Skeletonexample application:characterrecognition

  28. Image analysis • Labeling • Contour analysis

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