Digital Image Processing (DIP) Lecture # 5 Dr. Abdul Basit Siddiqui Assistant Professor-FURC

1. Digital Image Processing (DIP) Lecture # 5 Dr. Abdul Basit Siddiqui Assistant Professor-FURC FURC-BCSE7

2. Classification of DIP and Computer Vision Processes • Low-Level Process: (DIP) – Primitive operations where inputs and outputs are images; major functions: image pre-processing like noise reduction, contrast enhancement, image sharpening, etc. • Mid-Level Process (DIP and Computer Vision) – Inputs are images, outputs are attributes (e.g., edges); major functions: segmentation, description, classification / recognition of objects • High-Level Process (Computer Vision) – Make sense of an ensemble of recognized objects; perform the cognitive functions normally associated with vision FURC-BCSE7

3. Image Processing Steps FURC-BCSE7

4. DIP Course • Digital Image Fundamentals and Image Acquisition (briefly) • Image Enhancement in Spatial Domain – Pixel operations – Histogram processing – Filtering • Image Enhancement in Frequency Domain – Transformation and reverse transformation – Frequency domain filters – Homomorphic filtering • Image Restoration – Noise reduction techniques – Geometric transformations FURC-BCSE7

5. DIP Course • Wavelets and Multi-Resolution Processing – Multi-resolution expansion – Wavelet transforms, etc. • Image Segmentation – Edge, point and boundary detection – Thresholding – Region based segmentation, etc FURC-BCSE7

6. Image Representation • Image • Two-dimensional function f(x,y) • x, y : spatial coordinates • Value of f : Intensity or gray level FURC-BCSE7

7. Digital Image • A set of pixels (picture elements, pels) • Pixel means • pixel coordinate • pixel value • or both • Both coordinates and value are discrete FURC-BCSE7

8. Example • 640 x 480 8-bit image FURC-BCSE7

9. FURC-BCSE7

10. Digital Image Processing (DIP) Digital Image Fundamentals and Image Acquisition FURC-BCSE7

11. Image Acquisition FURC-BCSE7

12. Image Description f (x,y): intensity/brightness of the image at spatial coordinates (x,y) 0< f (x,y)<∞ and determined by 2 factors: illumination component i(x,y): amount of source light incident reflectance component r(x,y):amount of light reflected by objects f (x,y) = i(x,y)r(x,y) Where 0< i(x,y)<∞: determined by the light source 0< r(x,y)<1: determined by the characteristics of objects FURC-BCSE7

13. Sampling and Quantization FURC-BCSE7

14. Sampling and Quantization Sampling: Digitization of the spatial coordinates (x,y) Quantization: Digitization in amplitude (also called gray-level quantization) 8 bit quantization: 28 =256 gray levels (0: black, 255: white) Binary (1 bit quantization):2 gray levels (0: black, 1: white) Commonly used number of samples (resolution) Digital still cameras: 640x480, 1024x1024, up to 4064 x 2704 Digital video cameras: 640x480 at 30 frames/second 1920x1080 at 60 f/s (HDTV) FURC-BCSE7

15. Sampling and Quantization Digital image is expressed as FURC-BCSE7

16. Sampling FURC-BCSE7

17. Effect of Sampling and Quantization FURC-BCSE7

18. RGB (color) Images FURC-BCSE7

19. Image Acquisition FURC-BCSE7

20. Basic Relationships between Pixels FURC-BCSE7

21. Basic Relationships between Pixels FURC-BCSE7

22. Basic Relationships between Pixels FURC-BCSE7

23. Basic Relationships between Pixels FURC-BCSE7

24. Distance Measures Chessboard distance between p and q: FURC-BCSE7

25. Distance Measures • D4 distance (city-block distance): • D4(p,q) = |x-s| + |y-t| • forms a diamond centered at (x,y) • e.g. pixels with D4≤2 from p D4 = 1 are the 4-neighbors of p FURC-BCSE7

26. Distance Measures • D8 distance (chessboard distance): • D8(p,q) = max(|x-s|,|y-t|) • Forms a square centered at p • e.g. pixels with D8≤2 from p D8 = 1 are the 8-neighbors of p FURC-BCSE7