Lectures: FL-320, Wednesdays, 6pm - 8:50pm
Instructor: Prof. Emmanuel Agu, FL-139, 508-831-5568, firstname.lastname@example.org
Office Hours: Wednesdays 4 - 5PM; Others by appointment
Required Text: Digital Image Processing: An Algorithmic Introduction using Java by Wilhelm Burger and Mark J. Burge, Springer Verlag
Supplemental texts (Optional):
- Digital Image Processing (3rd Edition) by Rafael C. Gonzalez and Richard E. Woods, Prentice-Hall
- Digital Image Processing Using MATLAB, 2nd ed. by Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins Gatemark Publishing
- Introduction to Digital Image Processing with MATLAB by Alasdair McAndrew, Course Technology
- Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab by Chris Solomon and Toby Breckon, Wiley
Facilities: You should do your assignments in Java but may choose to develop your code on either Unix or Windows. Very important: No matter what platform you write your code on, the final executable must run on the Windows machines in the WPI Zoolab with clear instructions in your documentation on how to run it. Your submitted code will be compiled, tested and graded on the machines in the zoolab. Make sure your code runs well on those machines before submitting it. Points will be deducted if you do not check that your code works on those machines.
Class Websites: The class website is at http://web.cs.wpi.edu/~emmanuel/courses/cs545/S14/. A myWPI class website has also been set up. Please post your questions on the discussion board to avoid excessive emails and so that everyone can benefit from answers given. You may send email to me if you have questions on matters that concern only you.
Software Utilities: Your programs will be written in ImageJ and Java, which are installed on the machines in the WPI Zoolab.
Grade Policy: 50% exams (2 exams), 50% assignments (5 projects)
Late Assignment Credit: Late programming assignments will be penalized 15 percent per day (per 24 hours). Assignments later than 4 days late will not be accepted.
- Reading is mandatory, working ahead is encouraged.
- Exams shall be based on lectures, readings and a bit of project knowledge, so class attendance is strongly encouraged.
- Working and discussions in pairs is okay. However, each student must turn in different and unique projects.
- Cheating is strictly forbidden
- Cheating (a.k.a., academic dishonesty), defined as taking credit for work you did not do or knowledge you do not possess, is strictly forbidden. First offenders will receive a zero grade for the assignment or exam in question and an academic dishonesty report will be filed with the Office of Student Affairs. Repeat offenders will receive an F for the course and the case will be brought before the campus hearing board (see Student Handbook).
- All assignments should be submitted electronically. Hard copies or submissions on disks will not be accepted. Both your executable and source code must be turned in. Your documentation MUST include the structure of your project, what each file contains and instructions for compiling and running the program. Typically, a well-organized README ASCII text file is sufficient. Insufficient documentation will result in a loss of points. Data files should include a comment line at the start giving your name, the assignment for which it is intended, and the most recent date in which the file was changed. Please do NOT turn in hardcopies!! Your README file should be ASCII text, Microsoft Word or PDF.
Wk 1 (Jan 22): Introduction to Image Processing, Image Processing Fundamentals, Image File Formats and ImageJ Ref: Burger & Burge Ch 1-3, G&W Ch 1-2 Homework 0 Not to be submitted Wk 2 (Jan 29): Snow day, no class Wk 3 (Feb 5): Histograms and Point Operations (Part 1) Ref: Burger & Burge Ch 4-5, G&W 3.1 - 3.3 [ Homework 1 PDF ] [ runway.jpg ] [ spine.jpg ] Due February 12 Wk 4 (Feb 12): Filters (Neighborhood and Spatial Processing) Ref: Burger & Burge Ch 6, G&W Ch 3 Wk 5 (Feb 19): Edges and Contours (Segmentation) Ref: Burger & Burge Ch 7, G&W Ch 10 Wk 6 (Feb 26): Corner Detection Ref: Burger and Burge Ch 8, G&W Ch 10 [ Homework 2 PDF ] Due March 5, 2014 Question 3 images: [ Degraded_A.jpg ] [ Degraded_D.jpg ] [ Desired.jpg ] Question 4 images: [ brain.tif ] [ lizard.tif ] [ pattern.tif ] [ pronghorn.tif ] Wk 7 (Mar 5): Midterm [ Homework 3 PDF ] Due March 26, 2014 Images: [ hoos_cow.tif ] [ sign1.tif ] [ sign2.tif ] Mar 12: Term Break Wk 8 (Mar 19): Curve Detection and Morphological Filters (Part 1) Ref: Burger and Burger Ch 9-10, G&W Ch 9 Wk 9 (Mar 26): Morphological Filters (Part 2) & Regions in Binary Images (Part 1) Ref: Burger and Burge Ch 10-11, G&W Ch 9, 11 [ Homework 4 PDF ] Due April 9, 2014 Image: [ mannequin-dots.png ] Wk 10 (Apr 2): Regions in Binary Images (Part 2) & Color Images Ref: Burger & Burge Ch 11-12, G&W Ch 11, 6 Wk 11 (Apr 9): Introduction to Spectral Techniques (Fourier Transform, DFT, DCT) (Part 1) Ref: Burger & Burge Ch 13-15, G&W Ch 4 [ Homework 5 PDF ] Due April 23, 2014 Wk 12 (Apr 16): Introduction to Spectral Techniques (Fourier Transform, DFT, DCT) (Part 2) & Geometrical Transformations Ref: Burger Ch 13-16, G&W Ch 4 Wk 13 (Apr 23): Comparing Images & Pattern Recognition (Computer vision?): Brief Overview of topics not covered: Image Coding and Compression, wavelets and multiresolution processing What's next (brief):Medical Imaging, computational photography, digital forensics, computer vision Review for final exam Ref: Burger and Burge Ch 17, G&W Ch 7-8 , Ch 12 Wk 14 (Apr 30): Final examClass Slides
- Lecture 1 [ Introduction to Image Processing and ImageJ ]
- Lecture 2 [ Histograms & Point Operations (Part 1) ]
- Lecture 3 [ Point Operations (Part 2) & Filters (Part 1) (Neighborhood and Spatial Processing)]
- Lecture 4 [ Filters (Part 2) & Edges and Contours ]
- Lecture 5 [ Edge Detection (Part 2) & Corner Detection ]
- Midterm review [ Midterm review slides ]
- Lecture 6 [ Curve Detection & Morphological Filters (part 1)]
- Lecture 7 [ Morphological Filters (part 2) & Regions in Binary Images (Part 1)]
- Lecture 8 [ Regions in Binary Images (Part 2) & Color Images (Part 1)]
- Lecture 9 [ Color Images (Part 2) & Introduction to Spectral Techniques]
- Lecture 10 [ Discrete Fourier Transform ]
- Lecture 11 [ Geometric Operations, Comparing Images and Future Directions ]
- Sample Final Exam [ Fall 11 Final Exam ]
Main Web Resources
- ImageJ Website
- Eclipse IDE Website
- How to Set up ImageJ in the Eclipse IDE
- Youtube Tutorial on intalling and basics of ImageJ
- Writing ImageJ plugins - A Tutorial by Werner Bailer