Introduction to Image Processing

1. tdh.net@pmail.ntu.edu.sg 15.03.2009 Introduction to Image Processing

2. Outline • * Image Processing (IP), Computer Vision (CV), Computer Graphics (CG) • * Image Formation • * Fourier Transform in Image Processing

3. IP vs. CV vs • IP: (roughly) a process: input – images, output – images. • CV: a branch of AI - use computers to emulate human vision: learning, making inferences, taking actions based on visual inputs. • No general agreement of difference between IP & CV. • Image Analysis (IA) (aka. Image Understanding) – between IP & CV.

4. IP vs. CV (contd)From IP to CV • Low-level: primitive IP operations – reduce noise, contrast enhancement and image sharpening... Input & output: images. • Mid-level: segmentation, description of objects in suitable forms, classification of individual objects… Input: images. Output: attributes extracted from images: edges, contours, identity of individual objects,… • High-level: making sense of an ensemble of recognized objects; cognitive functions as in human vision.

5. CV vs. CG • CG – (wiki) graphics created by computers; the representation and manipulation of pictorial data by computers. • Difference between CV & CG (roughly): • CG – creating an image of something that looks realistic (3D -> 2D). • CV – taking an image and extracting from it some information about the scene (2D -> 3D).

6. Image Formation • What is an image? • How is an image presented? • How to transform information from real world to an image?

7. Image Formation

8. Image Formation Image of object

9. Image formation Projection onto discrete sensor array Digital camera

10. Image Formation Sensors register average color Sampled Image

11. Image Formation Continuous Colors, Discrete Locations Discrete Real-value Image

12. Digital Image Formation:Sampling & Quantization Discrete Color Output Quantization: continuous colors mapped to a finite, discrete set of colors. Continuous Color Input Real Image Sampled Quantized Sampled and Quantized

13. Digital Image (Less formal) A digital Image: a grid of squares, each of which contains a single color. • An image – a function f(x, y). • x, y – spatial coordinates. • amplitude of f(x, y) – intensity of the image or gray level. • Digital Image: x, y, f(x, y) are all finite. Color images have 3 values per pixel; Monochrome images have 1 value per pixel.

14. Digital Images (contd) Color images • Are constructed from three intensity maps. • Each intensity map is projected through a color filter (e.g., red, green, or blue, or cyan, magenta, or yellow) to create a monochrome image. • The intensity maps are overlaid to create a color image. • Each pixel in a color image is a three element vector.

15. Digital Images (contd) Color Images on a CRT (Cathode Ray Tube)

16. Fourier Transform in Image Processing • Explains why down-sampling can add distortion to an image and shows how to avoid it. • Useful for certain types of noise reduction, deblurring, and other types of image restoration. • For feature detection and enhancement, especially edge detection.

17. The 2D Fourier Transform of a Digital Image

18. The 2D Fourier Transform of a Digital Image

19. The 2D Fourier Transform of a Digital Image

20. FT of an edge

21. Relationship between Image and FT

22. Features in FT and Image

23. More visually relevant information: magnitude or phase original image Fourier log magnitude Fourier phase

24. Reconstruction Only phase Only magnitude

25. What is not concerned… • Image compression. • Segmentation. • Image sampling. • Spatial filtering. • Noise reduction. To be continued…!

26. References • Richard Alan Peter, EECE/CS 253 Lecture Notes Image Processing. • Gonzalez R. C, Woods R. E, Eddins S. L, Digital Image Processing using Matlab, Prentice Hall, 2004. • Image Processing Fundamentals, Lecture Notes, [online], http://www.ph.tn.tudelft.nl/Courses/FIP/frames/fip.html