Fundamentals of Digital Image Processing

1. Fundamentals of Digital Image Processing Dr. Hiren D. Joshi Dept. of Computer Science Rollwala Computer Centre Gujarat University

2. Introduction • Digital Image processing methods were introduced in 1920. • Use of Digital Computers for improving the quality of images began at Jet Propulsion Laboratory in 1964.

3. Aspects of Image Processing • It is convenient to subdivide different image-processing algorithms into broad subclasses. • There are different algorithms for different tasks and problems and often we would like to differentiate the nature of the task

4. Aspects of Image Processing • Image Enhancement • Image Restoration • Image Segmentation

5. Image Enhancement • Processing an image so that the result is more suitable for a particular application is called image enhancement. • For example • Sharpening or de-blurring an out-of-focus image • Highlighting edges • Improving image contrast or brightening an image • Removing noise

6. Image Restoration • An image may be restored by the damage done to it by a known clause • For example • Removing or blur caused by linear motion • Removal of optical distortions • Removing periodic interference

7. Image Segmentation • Segmentation involves subdividing an image into constituent parts or isolating certain aspects of an image, including • Finding lines, circles or particular shapes in an image • Identify cars, tress, building or roads in an aerial photograph.

8. Aspects of Image Processing • These classes are not disjoint • A given algorithm may be used for both image enhancement or for image restoration. • However, we should be able to decide what it is that we are trying to do with our image: simply make it look better (enhancement) or remove damage (restoration)

9. Steps in Image Processing • Image acquisition • Preprocessing • Segmentation • Representation and feature extraction • Recognition and interpretation

10. Steps in Image Processing Representation and Description Segmentation Image Preprocessing Knowledge Base Recognition and Interpretation Result Problem Domain Image Acquisition

11. Steps in Image Processing • The job is to obtain, by an automatic process, the postal codes from envelopes.

12. Image Acquisition • First we need to produce a digital image from a paper envelope. This can be done using either a CCD camera or a scanner.

13. Preprocessing • This is the step taken before the major image processing task. • The problem here is to perform some basic tasks in order to render the resulting image more suitable for the job to follow. • In this case it may involve enhancing the contrast, removing noise, or identifying regions likely to contain the postal code. • A process to enhance the image in order to make it suitable for further processing.

14. Segmentation • Here is where we actually get the postal code • In other words we extract from the image that part of it that contains only the postal code

15. Representation and description • These terms refer to extracting the particular features that allow us to differentiate between objects. • Here we will be looking for curves, holes and corners that allow us to differentiate the different digits that constitute a postal code.

16. Recognition and interpretation • This means assigning labels to objects based on their descriptors (from the previous step) and assigning meanings to those labels. • We identify particular digits and we interpret a string of digits at the end of the address as the postal code.

17. Types of Digital Images • Binary • Grayscale [ 0 to 255 ; black to white] • True color or RGB [ 0 to 255 ] • 24-bit color images • Indexed

18. Building Blocks of a DIP System • Acquisition • Storage • Processing • Display and communication interface

19. Digital Image Representation • Any monochrome image can be represented by means of a 2-D light intensity function f(x,y) where x and y denotes spatial coordinates and the value of x at any point (x,y) is the gray level or the brightness of the image at that point. • The origin is taken at the top left corner and the horizontal line and the vertical line through the origin are taken as y and x axes.

20. A digital image can be as a matrix whose rows and columns are used to locate a point in the image and the corresponding element value give the gray level at that point. • Each element in this matrix/digital array is called as image element or pixels.

21. f(x,y) = f(0,0) f(0,1) … f(0,N-1) f(1,0) f(1,1) … f(1,N-1) . . … …. . . … …. . . … … f(N-1,0) f(N-1,1) … f(N-1,M-1) • The function f(x,y) may consist of 2 components • The amount of light incident on the scene being viewed • The amount of light reflected by the object in the scene • The light incident and reflected can be denoted as i(x,y) and r(x,y) respectively. • f(x,y) = i(x,y) x r(x,y) • Where 0 < f(x,y) < Infintiy and 0 < r(x,y) < 1 • The value of i(x,y) is determined by the light source and r(x,y) is determined by the characteristics of object in a scene.

22. Example1: On a clear sunny day the illumination on the surface of the earth is about 9000-foot candles and decreases below 1000-foot on a cloudy day. • Full moon light is about 0.01-foot candles. • Office environment is about 100-foot candles.

23. The reflected component of light from snow is 0.93-foot candles. • Flat white wall paint is 0.8 foot candles • Stainless steel is 0.65 foot candles.

24. Sampling and Quantization • Sampling and quantization are the two important processes used to convert continuous analog image into digital image. • Discretization: A process in which signals or data samples are consider at regular intervals.

25. Sampling and Quantization • Sampling: Image sampling referes to discretization of spatial coordinates. • Quantization: Quantization refers to the discretization of image intensity(gray level) values. • Normally, the sampling and quantization deals with integer values. • After the image is sample, with respect to x and y coordinates the number used

26. Sampling and Quantization • Normally, the sampling and quantization deals with integer values. • After the image is sampled, with respect to x and y coordinates the number of samples used along the x and y directions are denoted as N and M respectively. • The N and M are usually the integer powers of 2.

27. Sampling and Quantization • M = 2n andM = 2k • Similarly, when we discretize the gray levels, we use the integer values

28. Some Applications • Medicine • Inspection and interpretation of images obtained from X-rays, MRI or CAT scans. • Analysis of cell images and Chromosome karyotypes.

29. Some Applications • Agriculture • Satellite/aerial views of land, for example to determine how much land is being used for different purposes or to investigate the suitability of different regions for different crops • Inspection of fruit and vegetables – distinguishing good and fresh product from old.

30. Some Applications • Industry • Automatic inspection of items on a production line • Inspection of paper samples

31. Some Applications • Law enforcement • Fingerprint analysis • Sharpening or deblurring of speed-camera images