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Imageprocessing An introduction. What is image processing? image analysis patron recognition graphical manipulation datacompression data transmission.

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Presentation on theme: "Imageprocessing An introduction. What is image processing? image analysis patron recognition graphical manipulation datacompression data transmission."— Presentation transcript:

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 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 Smoothing with mean filter filtering.gif

11 Smoothing with Gaussian Low pass

12 Edge detection with Laplacian operator

13 Edge detection with Laplacian operator(2) L[f(x,y)] = d 2 f / dx 2 + d 2 f / dy 2 d 2 f / dx 2 = f(x+1, y) - 2f(x, y) + f(x-1, y) d 2 f / dy 2 = 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 Dilation 8 and 4 connect influence 8-connect4-connect

18 Erosion

19 Dilation

20 Erosion 8-connected

21 Dilation 8 connected

22 Opening and closing

23 Erosion and Dilation with threshold threshold=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 pixel Reduction to 1 point

27 Skeleton example application:characterrecognition

28 Image analysis Labeling Contour analysis


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