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

2. 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

3. FURC-BCSE73 Image Processing Steps

4. FURC-BCSE74 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

5. FURC-BCSE75 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

6. Image Representation Image –Two-dimensional function f(x,y) –x, y : spatial coordinates Value of f : Intensity or gray level FURC-BCSE76

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-BCSE77

8. Example 640 x 480 8-bit image FURC-BCSE78

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10. 10 Digital Image Processing (DIP) Digital Image Fundamentals and Image Acquisition

11. FURC-BCSE711 Image Acquisition

12. FURC-BCSE712 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

13. FURC-BCSE713 Sampling and Quantization

14. FURC-BCSE714 Sampling and Quantization Sampling: Digitization of the spatial coordinates (x,y) Quantization: Digitization in amplitude (also called gray-level quantization) 8 bit quantization: 2 8 =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)

15. FURC-BCSE715 Sampling and Quantization Digital image is expressed as

16. FURC-BCSE716 Sampling

17. FURC-BCSE717 Effect of Sampling and Quantization

18. FURC-BCSE718 RGB (color) Images

19. FURC-BCSE719 Image Acquisition

20. FURC-BCSE720 Basic Relationships between Pixels

21. FURC-BCSE721 Basic Relationships between Pixels

22. FURC-BCSE722 Basic Relationships between Pixels

23. FURC-BCSE723 Basic Relationships between Pixels

24. FURC-BCSE724 Distance Measures Chessboard distance between p and q:

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

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