ECGR6118              	Applied Digital Image Processing      	Fall 2008

Prof. Weldon: 222B Woodward,  
See home website for office hours:
Course Web site:

	Required: "Digital Image Processing: PIKS Scientific Inside," 4th Ed.  
	   by William K. Pratt, Wiley-Interscience, 2008, ISBN: 0471767778.
	Optional: Teach Yourself C++, 4th Ed.,  A. Stevens, MIS Press, 1995. 
	Optional: "The Java Tutorial, 3rd Ed., A Short Course on the Basics,"
	 	M. Campione, K. Walrath, A. Huml, Addison Wesley, 2001. 
	Optional: "Fundamentals of Digital Image Processing," Anil K. Jain,
		Prentice Hall, 1989.

Grading will be based on  projects/quizzes/assignments (60%) and
Final Project (40%).  Grading scale: 90-100 A, 80-89 B, 70-79 C,  
with ``curve,'' if any, entirely at the discretion of the instructor.    
Quizzes may be given without warning.  

Collaboration (not copying!) on homework and computer projects is encouraged. 
However, students may NOT share program code or report material.   
Class attendance and  participation are expected, and the instructor reserves
the right to reduce the grade on any work, or the course, for poor attendance.  
You must read the textbook; it is impossible to cover all material during class.  

The course will roughly follow the outline below; key topics are noted.

Week/Chap.    	Topic

1/1-6	    	Image Fundamentals
            	Vision, images, color, coordinates, matrix forms
2/7-8		Convolution and transforms
		Convolution, Fourier transform, Cosine transform, Hadamard, 
		Project 1: Transforms 
3-4/-		Sub-band coding and pyramid/wavelet representations
		Filtering, QMF, subband, pyramid, wavelet, compression
		Project 2: Pyramids, sub-bands, compression 		

5/9-12          Image enhancement:
                Spatial, frequency domain, histogram eq., local methods,
                subtraction, filtering 
		Project 3: Enhancement		

6/14		Morphology:
		Dilation, erosion, open, close, gray-scale, skeleton 
		Project 4: Morphology

7/15		Edge Detection:
		Edges, gradient, laplacian, Hough transform
		Project 5: Edges	
8-9/16		Feature Extraction
		B-distance, error, amplitude features, texture, Gabor filter
		Project 6: Texture features

9-10/17		Segmentation
		Thresholding, region growing, split/merge, clustering
		Project 7: Segmentation
11/-		3D Images and Image Volumes
                Project 8 3D

12		Final Project proposals due

12-14/8 	Final projects
15		Final project presentations

Final reports due: During scheduled final exam period   (see published schedules)
Presentation make-up: During scheduled final exam period 

We cannot devote much time in class to programming.  If you are
not familiar with C++ or Java or Mathcad, you must become proficient early
in the semester.  Use my office hours to address any questions that 
you may have on C++ or java or Mathcad: DO NOT PUT THIS OFF!!   

If you miss an exam/quiz for any reason, you will receive a grade of zero  
(exceptional circumstances must be documented and/or approved by the instructor 
at least 24 hours prior to the exam).  Exams may be open book, NO 
calculators.  Late homeworks/projects will not be accepted.  Frequent 
absence from class or disruptive behavior will result in a severe grade
reduction.   Late projects will not be accepted, or may be penalized
up to 20 percent per day, solely at the instructor's discretion.  Poor or 
late project proposals will adversely affect project grade. Absence from 
each project session will result in 20% grade reduction, beyond 1 absence.
Absence from final project sessions will each result in 20% reduction 
in final project grade, beyond 1 absence.  

It is the responsibility of the student to be familiar with the academic
regulations, degree requirements, and course requirements as outlined in
the current University Catalog.  The official university 
guidelines supersede any contradictions that may exist in this handout.


published in the UNCC Catalog.

Prerequisite or corequisite: 

You should be familiar with Fourier transforms and filtering concepts.  Look
over the textbook during the first week of class to determine if you have
sufficient background for this course.


Students have the responsibility to know and observe the requirements of The UNCC
Code of Student Academic Integrity .  This code forbids
cheating, fabrication or falsification of information, multiple submission of 
academic work, plagiarism, abuse of academic materials, and complicity in academic 
dishonesty.  Grades in this course will be adversely affected by academic dishonesty,
for example zero credit for work involving dishonesty and/or course grade reduced 
to F.