Introduction to Image Processing: Concepts and Techniques
This guide provides an overview of image processing, covering fundamental concepts like image analysis, pattern recognition, graphical manipulation, data compression, and multimedia applications. It includes global image operations such as histogram stretching and equalization, as well as local operations like smoothing and edge detection. Detailed examples in MATLAB demonstrate key techniques. The guide also explores morphological operations like erosion and dilation, highlighting their applications in image analysis and noise reduction. Aimed at beginners and enthusiasts, this resource simplifies complex topics.
Introduction to Image Processing: Concepts and Techniques
E N D
Presentation Transcript
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 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
Stretching(2) 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
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.)
demo Filters.exe
4. Morphologie • Erosion • Dilitation • Opening / closing • Conditional erosion • Skeleton
Erosion and Dilation8 and 4 connect influence 8-connect 4-connect
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
Image analysis • Labeling • Contour analysis
