1 Digital Image Processing Dr. Saad M. Saad Darwish Associate Prof. of computer science

2 What is an image? - The bitmap representation  Also called “raster or pixel maps” representation  An image is broken up into a grid pixel Gray level Original picture Digital image f(x, y) I[i, j] or I[x, y] x y

3 What is an image? - The vector representation  Object-oriented representation  Does not show information of individual pixel, but information of an object (circle, line, square, etc.) Circle(100, 20, 20) Line(x a1, y a1, x a2, y a2 ) Line(x b1, y b1, x b2, y b2 ) Line(x c1, y c1, x c2, y c2 ) Line(x d1, y d1, x d2, y d2 )

4 Comparison  Bitmap –Can represent images with complex variations in colors, shades, shapes. –Larger image size –Fixed resolution –Easier to implement  Vector –Can only represent simple line drawings (CAD), shapes, shadings, etc. –Efficient –Flexible –Difficult to implement

5 Why image processing?  Application –Fingerprint retrieval –Automatic target recognition –Industrial inspection –Medical imaging –and more …  Can commercial software do all the work?

6 Some clarification  Image & Graphics Graphics are visual presentations on some surface, examples are photographs, drawings, graphs, diagrams, symbols, geometric, maps, engineering drawings, or other images. photographsdrawingsgraphsdiagramssymbolsgeometricmaps engineering drawingsimages  Image processing & Computer vision Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images.  Image processing & Image understanding Image understanding produces descriptions of both the images (extracting features from images) and the world scenes that the images represent."  Image processing & Pattern recognition

7 Goals of image processing  Image improvement –Improving the visual appearance of images to a human viewer  Image analysis –Preparing images for measurement of the features and structures present

8 What to learn? Image Acquisition Image Enhancement Image Restoration Image Compression Image Segmentation Representation & Description Recognition & Interpretation Knowledge Base Preprocessing – low level Image Improvement Image Coding Morphological Image Processing Wavelet Analysis High-level IP Image Analysis

 Video camera  Infrared camera  Range camera  Line-scan camera  Hyperspectral camera  Omni-directional camera  and more … 9 Image acquisition

10 Some simple operations

11 Image enhancement

12 Movie film restoration

13 Image restoration The purpose of image restoration is to "compensate for" or "undo" defects which degrade an image.

14 Image correction  Geometric correction: Operations intended to restore or compensate the distortions of an image in geometry  Radiometric correction: Operations intended to remove systematic or random noise affecting the amplitude (brightness) of an image

Image warping geometric transformation Image warping is the process of digitally manipulating an image such that any shapes portrayed in the image have been significantly distorted.

16 Image warping – another example

17 Image Segmentation The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image The goal of image segmentation is to cluster pixels into salient image regions,

18 Image description  OCR – Optical character recognition, license plate recognition

19 Beyond  Content-based image retrieval  Human identification  Multi-sensor data fusion  Hexagonal pixel  Steganography

20 Image processing for fine arts

21 Real-world reasoning demo

22 How to address pixels of an image? int i, j, k; int nr, // number of rows nc, // number of columns nchan;// number of channels nr = 128; nc = 128; nchan = 3; for (i=0; i<nr; i++) { for (j=0; j<nc; j++) { for (k=0; k<nchan; j++) { do the processing on (i,j,k); ……… }

23 j (i, j)(i, j+1)(i, j-1) (i-1, j)(i-1, j+1) (i+1, j+1)(i+1, j) (i-1, j-1) (i+1, j-1) i (row) (column) 4-neighborhood 8-neighborhood Types of neighborhoods  Neighbors of a pixel