1. An Introduction to Digital Image Processing Dr.Amnach Khawne Department of Computer Engineering, KMITL

2. Score Programming Assignment + Homework 30% Examination – Final 45% Project – Proposal is proposed by STUDENTs 25% Ref. Books : An Introduction to Digital Image Processing with Matlab ; Alasdair McAndrew Tools: MATLAB or others programs

3. 1.1 Images and pictures we can identify a face in an instant; we can differentiate colors; we can process a large amount of visual information very quickly. “an image is a single picture which represents something.”

4. 1.2 What is image processing? Image processing involves changing the nature of an image in order to either 1. improve its pictorial information for human interpretation, 2. render it more suitable for autonomous machine perception.

5. 1.2 What is image processing? We shall be concerned with digital image processing, which involves using a computer to change the nature of a digital image. (1)a procedure which makes an image look better (2) Humans like their images to be sharp, clear and detailed; machines prefer their images to be simple and uncluttered.

6. Example of (1) Image sharpening

7. Example of (1) Removing noise from an image

8. Example of (1) Removing motion blur from an image.

9. Example of (2) Obtaining the edges of an image.

10. Removing detail from an image.

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12. 1.3 Image Acquisition and sampling Sampling refers to the process of digitizing a continuous function. For example, suppose we take the function

13. Effects of sampling

14. Producing a digital image Light is the predominant energy source for images; simply because it is the energy source which human beings can observe directly. We are all familiar with photographs, which are a pictorial record of a visual scene. Many digital images are captured using visible light as the energy source; this has the advantage of being safe, cheap, easily detected and readily processed with suitable hardware. Two very popular methods of producing a digital image are with a digital camera or a flat-bed scanner.

15. CCD camera http://www.sensorcleaning.com/whatisasen sor.php

16. Flat bed scanner http://i1-news.softpedia- static.com/images/extra/large/scan ner-large.gif http://en.wikipedia.org/wiki/File:CPT_Hard ware-Input-scanner-flatbed.svg

17. Other energy sources Visible light is part of the electromagnetic spectrum http://mynasadata.larc.nasa.gov/science-processes/electromagnetic-diagram/

18. Sources of images Sources of images: computer graphics software, scanners, digital cameras, digital video recorder, other equipment… Image formation process (X, Y, Z): the world coordinate of a point on the object (x, y): the image coordinate of a point on the image plane (through perspective projection) Image plane: an array of charged coupled devices (CCD)

19. A digital image A digital image differs from a photo in that the x, y and f(x,y) values are all discrete.

20. Image Formation (Gray Scale) Intensity Image ( ความเข้ม ของสี ) มีค่าตั้งแต่ 0…255 ( ในระดับ Gray Scale) 0 –> Black, 100 –> Gray, 255 -> White 200220190100225180 90245180150180255 244230020130140 230122349011267 …....………… 20

21. Pixel  21 -The word pixel is an abbreviation of ‘picture element’.

22. 1.4 Images and digital images MATLAB commands: h = imshow('bag.png'); info = imfinfo('bag.png'); imageinfo(h,info);

23. 1.5 Some applications of image processing Registering Multimodal MRI Images

24. 1.5 Some applications of image processing

26. General Framework of image processing

27. 1.6 Aspects of image processing Image enhancement. This refers to processing an image so that the result is more suitable for a particular application. Example include: – sharpening or de-blurring an out of focus image, – highlighting edges, – improving image contrast, or brightening an image – removing noise

28. Image restoration Image restoration. This may be considered as reversing the damage done to an image by a known cause, for example: removing of blur caused by linear motion, removing of optical distortions,

29. 1.7 An image processing task Acquiring the image Preprocessing – enhancing the contrast, – removing noise, – Identifying regions Segmentation Representation and description Recognition and interpretation

30. 1.8 Types of digital images Binary image : Each pixel is just black or white. Since there are only two possible values for each pixel, we only need one bit per pixel.

31. Grayscale image Grayscale. Each pixel is a shade of grey

32. A true color image RGB model: red, green, and blue primaries

33. Indexed Color

34. Spatial Resolution