CS5814 - Digital Picture Processing
Fall 2007, CRN 96310
TuTh 3:30pm, Shultz 105
|
|
|
Instructors
R.W. Ehrich, KW2 129, Office Hours: M,W 1:00-3:00 in KW2-129, or by appointment
Seung In Park, Office Hours: W 10:00 - 12:00 in McB 133A, or by appointment
Objectives
Digital picture processing involves the acquisition, enhancement, display,
and understanding of digital images. The field dates back to the work of
Roberts at MIT in the late '50s and has evolved slowly into the AI image
understanding systems being investigated today. This course covers what is
generally called low level vision and provides a survey of the most
well-known techniques in the field. The approach in the course will focus
on spatial rather than on frequency domain techniques. However, some
frequency-domain concepts will be introduced where that is essential to
understanding ideas such as sampling and filtering.
A scan of the course text will confirm that the course is not intended to focus
on signal processing. On the other hand, without mathematics it would be
difficult to understand many of the ideas and techniques that are basic to the
field. Although concepts such as complex numbers, convolution, norms, inner
product spaces, and orthogonal expansions will be reviewed, these ideas should
not be completely new to those taking the course. The Petrou text in the course
references is a particularly good introduction to many of the mathematical
preliminaries, and some of the lecture material will be taken from this text.
The references
on reserve in the library are especially important to the
course since they represent important recent topics that are not covered in
course texts. Students will be responsible for reading the relevant papers.
Grading
The semester grade will be based upon assignments issued in class
(15%), one midterm (25%), three programming projects (30%), and a comprehensive
final examination (30%).
Homework
Homework assignments will be posted on the class web page and will be due by 5pm
on the assigned dates. NO LATE HOMEWORK WILL BE ACCEPTED unless by prior
approval. All homework solutions must be legible, well-reasoned, and accurately
expressed. All work submitted for a grade must be the student's own work. If
you have a question about the grading of an assignment or an exam, you should
speak with the cource instructor within a week after the assignment is returned.
Exams
Midterm, Tuesday, September 25
Final, Friday, December 7, 10:05am - (NOTE: Classes end Wednesday, December 5)
No missed exams will be accepted unless by prior approval. All exams are
closed-book exams. The final covers all the material in the course.
Ethics
The Honor Code
will be strictly enforced. It is a violation to
represent joint work as your own or to let others use your work; always
acknowledge any assistance you received in preparing work that bears your
name. You are expected to work independently unless explicitly
permitted to collaborate on a particular assignment. It is not a violation to
discuss approaches to problems with others; however, it IS a violation to use
wording or expressions in your assignments that have been written by others
without acknowledging the source.
Please be considerate of your fellow students. In particular, please
realize that your classmates will likely need the reserve materials at the
same time you do... don't keep these materials out for long periods of time,
especially before exams and assignment due dates.
Prerequisites
Undergraduate calculus, linear algebra, probability theory, and programming
competence in C or C++. For example, students should be familiar with the idea of
orthogonal bases for discrete or continuous spaces.
Projects
The problem of low level image understanding is hard, and the course would
not be complete without some applied work to complement the lectures.
Images will be provided, and students may work on the platform of their
choice, realizing that their work must be demonstrated when complete.
Text
Gonzalez, R.C. and Woods, R.E., Digital Image Processing, Prentice
Hall, 2008.
References
- Ballard, D.H. and Brown, C.M., Computer Vision, Prentice-Hall,
1982.
- Durrett, John H., Color and the Computer, Academic Press, 1987.
- Forsyth, David and Ponce, Jean, Computer Vision, Prentice Hall, 2003.
- Haralick, R.M. and Shapiro, L.G., Computer and Robot Vision, Volume
1, Addison-Wesley, 1992.
- Haralick, R.M. and Shapiro, L.G., Computer and Robot Vision, Volume
2, Addison-Wesley, 1993.
- Jähne, Bernd, Digital Image Processing, Springer-Verlag, 1991.
- Jain, Anil K., Fundamentals of Digital Image Processing,
Prentice Hall, 1989.
- Jain, Ramesh, Kasturi, R., and Schunck, B.G., Machine Vision,
McGraw-Hill, 1995.
- Marr, David, Vision, Freeman, 1982.
- Netravali, A.N. and Haskell, B.G., Digital Pictures: Representation and
Compression, Plenum, 1989.
- Pavlidis, T., Algorithms for Graphics and Image Processing, Computer
Science Press, 1982.
- Petrou, Maria and Bosdogianni, P., Image Processing, John Wiley,
1999.
- Schalkoff, R.J., Digital Image Processing and Computer Vision, Wiley,
1989.
- Shapiro, L.G. and Stockman, G.C., Computer Vision, Addison Wesley,
2001.
- Sonka, M., Hlavac, V., and Boyle, R., Image Processing, Analysis, and
Machine Vision, PWS Publishing, 1999.
Course Outline
1 Visual Perception, Sampling and Quantization, Digital
Topography, Regions, Boundaries, Connected Components
2 Sensor Architectures
3 Spatial Image Enhancement
4 Linear Systems, Convolution, Correlation, Impulse Response
5 Inner Products, Orthogonal Transformations, Introduction to
the Fourier Transform
6 Frequency Space Enhancement
7 Matched Filtering, Sampling
8 Color, Color Quantization
9,10 Image Morphology
11 Segmentation, Relaxation, Hough Transforms
12 Surface Approximation
13 Representation and Description
14 Image Texture Analysis and Description