This document provides an overview of topics to be covered in an image processing course, including image formation, point processing, color correction, Fourier transforms, convolution, sampling and warping, spatial and frequency domain filtering, noise reduction, mathematical morphology, and image compression. Example images are used to illustrate concepts like color perception, color balance, filtering, noise removal, and image compositing.
1. EECE/CS 253 Image Processing Richard Alan Peters II Department of Electrical Engineering and Computer Science Fall Semester 2007 Lecture Notes: Introduction and Overview This work is licensed under the Creative Commons Attribution-Noncommercial 2.5 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/2.5/ or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.
2. Introduction and Overview This presentation is an overview of some of the ideas and techniques to be covered during the course.
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4. Wallace and Gromit Wallace and Gromit will be subjects of some of the imagery in this introduction. Wallace Gromit likes cheese reads Electronics for Dogs http://www.aardman.com/wallaceandgromit/index.shtml Visit:
13. Digital Image a grid of squares, each of which contains a single color each square is called a pixel (for picture element ) Color images have 3 values per pixel; monochrome images have 1 value per pixel.
19. Color Sensing / Color Perception These are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye.
20. Color Sensing / Color Perception These are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye. The simultaneous red + blue response causes us to perceive a continuous range of hues on a circle. No hue is greater than or less than any other hue.
21. Color Sensing / Color Perception luminance hue saturation photo receptors brain The eye has 3 types of photoreceptors: sensitive to red, green, or blue light. The brain transforms RGB into separate brightness and color channels ( e.g. , LHS).
22. Color Perception all bands luminance chrominance red green blue 16 × pixelization of: luminance and chrominance (hue+saturation) are perceived with different resolutions, as are red, green and blue.
23. Color Perception all bands luminance chrominance red green blue 16 × pixelization of:
24. Color Balance and Saturation Uniform changes in color components result in change of tint. E.g., if all G pixel values are multiplied by > 1 then the image takes a green cast.
26. The 2D Fourier Transform of a Digital Image Let I ( r,c ) be a single-band (intensity) digital image with R rows and C columns. Then, I ( r,c ) has Fourier representation where are the R x C Fourier coefficients. these complex exponentials are 2D sinusoids.
27. 2D Sinusoids: ... are plane waves with grayscale amplitudes, periods in terms of lengths, ... A = phase shift r c orientation
28. 2D Sinusoids: ... specific orientations, and phase shifts. orientation r c r c
29. The Value of a Fourier Coefficient … … is a complex number with a real part and an imaginary part. If you represent that number as a magnitude, A , and a phase, , … ..these represent the amplitude and offset of the sinusoid with frequency and direction .
32. Continuous Fourier Transform The continuous Fourier transform assumes a continuous image exists in a finite region of an infinite plane. The BoingBoing Bloggers
33. Discrete Fourier Transform The discrete Fourier transform assumes a digital image exists on a closed surface, a torus. The BoingBoing Bloggers
34. Convolution Sum times 1/5 Sums of shifted and weighted copies of images or Fourier transforms.
35. Convolution Property of the Fourier Transform The Fourier Transform of a product equals the convolution of the Fourier Transforms. Similarly, the Fourier Transform of a convolution is the product of the Fourier Transforms
36. Sampling, Aliasing, & Frequency Convolution aliasing (the jaggies) no aliasing (smooth lines)
56. Nonlinear Processing: Binary Morphology Cross-hatched pixels are indeterminate. “ L” shaped SE O marks origin Foreground: white pixels Background: black pixels
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58. Nonlinear Processing: Grayscale Morphology Cross-hatched pixels are indeterminate. “ L” shaped SE O marks origin Foreground: white pixels Background: black pixels
62. Forensic Analysis of Photographs Photographs by Robert Fenton of a battlefield in the Crimean war taken on 23 April 1855. From Morris, Errol, “Which Came First, the Chicken or the Egg?”, Parts 1-3, New York Times , Zoom Editorial Section , 25 Sept. 2007 (pt.1), 7 Oct. 2007 (pt.2), 30 Oct. 2007 (pt.3). Which came first?
63. Forensic Analysis of Photographs Photographs by Robert Fenton of a battlefield in the Crimean war taken on 23 April 1855. From Morris, Errol, “Which Came First, the Chicken or the Egg?”, Parts 1-3, New York Times , Zoom Editorial Section , 25 Sept. 2007 (pt.1), 7 Oct. 2007 (pt.2), 30 Oct. 2007 (pt.3). Which came first?
64. Forensic Analysis of Photographs Photographs by Robert Fenton of a battlefield in the Crimean war taken on 23 April 1855. From Morris, Errol, “Which Came First, the Chicken or the Egg?”, Parts 1-3, New York Times , Zoom Editorial Section , 25 Sept. 2007 (pt.1), 7 Oct. 2007 (pt.2), 30 Oct. 2007 (pt.3). Which came first?
65. Image Compression Yoyogi Park, Tokyo, October 1999. Photo by Alan Peters. Original image is 5244w x 4716h @ 1200 ppi: 127MBytes