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Published byIzaiah Rollie Modified over 4 years ago

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Imageprocessing An introduction

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

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

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Histogram

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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

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Stretching

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

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

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

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Smoothing with mean filter filtering.gif

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Smoothing with Gaussian Low pass

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Edge detection with Laplacian operator

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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.)

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Directional Edge Detection

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demo Filters.exe

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4. Morphologie Erosion Dilitation Opening / closing Conditional erosion Skeleton

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Erosion and Dilation 8 and 4 connect influence 8-connect4-connect

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Erosion

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Dilation

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Erosion 8-connected

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Dilation 8 connected

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Opening and closing

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Erosion and Dilation with threshold threshold=1 (at least 8 must be there)

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

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

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Keep the last pixel Reduction to 1 point

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Skeleton example application:characterrecognition

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Image analysis Labeling Contour analysis

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Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright 1996, 1999, 2000-2006. All rights reserved.

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