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

• Math on images

Histogram

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 sizefigure(1); % make a new pictureimage(zon); % show picturecolormap(gray(256)); % set gray colormapfor 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

Stretching

Stretching(2)

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y=(x-64)*4

3. Local Operations

• Smoothing

• Low pass filtering

• Edge detection

• Directional edge detecting

• Min-max operation

• Sharpening

• Special filters

Local operation

• Make a new image depending on pixels in the neigtbourhood

• filtering.gif

Smoothingwith mean filter

filtering.gif

Smoothing with Gaussian Low pass

Edge detectionwith Laplacian operator

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.)

Directional Edge Detection

demo

Filters.exe

4. Morphologie

• Erosion

• Dilitation

• Opening / closing

• Conditional erosion

• Skeleton

Erosion and Dilation8 and 4 connect influence

8-connect 4-connect

Erosion

Dilation

Erosion 8-connected

Dilation 8 connected

Opening and closing

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

Erosion Dilation applications

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

Conditional Erosion

• Keep the last pixel

• Keep connectednes

• Keep the end-pixel of a string of pixels with 1 pixel

Keep the last pixelReduction to 1 point

Skeletonexample application:characterrecognition

Image analysis

• Labeling

• Contour analysis