1 / 28

Imageprocessing

This comprehensive introduction to image processing covers essential concepts such as image analysis, pattern recognition, and graphical manipulation. It explores techniques like histogram stretching, equalization, and binarization, alongside local operations such as smoothing, edge detection, and morphological methods (e.g., erosion and dilation). The use of MATLAB for practical implementations, including reading images and performing pixel-wise operations, is illustrated. Real-world applications in multimedia and data transmission are also discussed, making it an essential guide for students and professionals in the field.

eden-faulkner
Télécharger la présentation

Imageprocessing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

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 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

More Related