CS 474/674 Digital Image Processing
Copyright: August, 2001


Professor Carl Grant Looney
 Computer Science Department, UNR
SEM 237, tel. 775-784-4313, fax. 775-784-1877
email: looney@cs.unr.edu
Web: http://ultima.cs.unr.edu/index.php
Office Hours: 3pm MWF

Reference Book: Nick Efford, Digital Image Processing, Addison-Wesley
[imprint of Pearson Education, London], Reading, MA, 2000.


        In this course you will first learn the basics of processing gray scale images: image file formats, contrasting, equalizing, sharpening, smoothing, edge detection and segmentation. Then comes spectral analysis and frequency filtering of digital images for special purposes. You will also learn how to combine frames in different spectral bands for special purposes. Morphological methods are next, followed by color images and their special formats, the color models, and color image proessing. We will use the tools: Matlab, Xview and LView Pro, and also programming algorithms in C and Java, especially in your term project final report, which provides project experience in image processing. Click on the desired unit below to bring up the basic notes for that topic.

   S y l l a b u s

Week                 Topic                                                                            

Part 1 - Images and Files

  1                   Unit 1. What is a Digital Image? Arrays, Capturing an Image, Gray Scale and Color, Range

  2                   Unit 2. File Formats for Images: PBM, PGM, GIF, PNG, JPEG, TIFF

Part 2 - Processing Gray Scale Images

  3                   Unit 3. Point Processes: Gray Scale, Contrast, Histograms, Transformations, Equalization, Specials

  4                   Unit 4. Tools and Programming: Matlab, XView, LView Pro, C Programs and Java Programs, Exercises

  5                   Unit 5a. Neighborhood Processes: Smoothing, Bluring, Sharpening, Refocusing

  6                   Unit 5b. Neighborhood Processes Continued: Edge Detection and Segmentation, Matlab Exercises


  7                   Midterm Week: Review and Midterm

Part 3 - Spectral Analysis - Fourier Methods

  8                   Unit 6a. Fourier Analysis: Cycles in Data, Fourier Series, Fourier Integrals, DFT's, FFT's

  9                   Unit 6b.Fourier Analysis Continued: 2-D FFT's, Low and High Pass Filters, Exponential Filters, Convolution, Matlab Exercises

Part 4 - Frame Processes and Special Techniques

  10                   Unit 7. Frame Processes: Combining Frames, Ratios, Fusion

  11                   Unit 8. Special Techniques: Erosion and Dilation, Opening and Closing, Morphology

Part 5 - Color Processing

  12                   Unit 9a. Color Images: Color Models, Color File Formats and Pseudo Color

  13                   Unit 9b. Color Images Continued: Processing Color Images, Matlab Exercises

  14                   Matlab Exercises and Term Projects

  15                   Matlab Exercises and Term Projects

G r a d i n g:   Midterm Examination: 50%        Term Project Final Report: 50%