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Fundamentals of Digital Image Processing

Fundamentals of Digital Image Processing. Dr. Hiren D. Joshi Dept. of Computer Science Rollwala Computer Centre Gujarat University. Introduction. Digital Image processing methods were introduced in 1920.

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Fundamentals of Digital Image Processing

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  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

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