ENEL 697 DIGITAL IMAGE PROCESSING (WINTER
2008)
Raj Rangayyan, PhD, PEng, FIEEE,
FEIC, FAIMBE,
FSPIE, FSIIM, FCMBES, “University Professor”
Professor,
Department of Electrical and Computer Engineering, Adjunct Professor of Surgery
and Radiology
Room ICT 440,
Phone 220-6745, email: ranga@ucalgary.ca Website:
Lectures:
Mondays and Wednesdays 4:00 to 5:15 PM. Room: ICT 446.
Laboratories:
on your own schedule.
Classes
start Monday, 14 January, 2008, and end Wednesday, 16 April, 2008.
No
classes during Reading Week: 17 – 24 February, 2008.
Calendar
description: Image
formation and visual perceptual processing. Digital image representation. Two
dimensional Fourier transform analysis. Image enhancement and restoration.
Selected topics from: image reconstruction from projections; image segmentation
and analysis; image coding for data compression and transmission; introduction
to image understanding and computer vision. Case studies from current
applications and research.
Detailed
course content:
Note:
Actual course content subject to variation, depending upon student interest and
selection of topics from the following.
Introduction:
Image acquisition and representation. Visual perceptual processing. Image
quality and information content.
Two-dimensional
systems and transforms. 2D convolution. 2D Fourier transform. Point
spread function and system transfer function. Matrix and vector representation
of images, linear system operations, and transforms.
Removal of
artifacts: Random noise. Structured artifacts. Methods to remove artifacts.
Image
enhancement: Contrast enhancement. Histogram operations. Spatial and
frequency-domain filtering for smoothing and edge enhancement. Algebraic
operations with images. Local, global, and adaptive methods.
Detection of
regions of interest: Edge detection. Segmentation. Detection of objects of
known geometry.
Analysis of
shape: Moments. Fourier descriptors. Shape factors.
Analysis of
texture: Ordered, oriented, and random texture. Statistical and structural
analysis of texture.
Image
reconstruction from projections: Projection geometry. Backprojection,
Fourier, convolution backprojection, and algebraic
reconstruction techniques. Fundamentals of computed tomography.
Image restoration: Degradation models. Inverse filter.
Wiener filter. Deblurring.
Image
coding: Information theory. Source-coding techniques. Decorrelation
techniques. Huffman, run-length, predictive, interpolative, and transform
coding.
Image
analysis and computer vision: Feature representation and pattern
classification.
Case studies
from image processing research.
Recommended
text:
R.M. Rangayyan, “Biomedical Image Analysis”, CRC Press,
Additional
references:
R.C.
Gonzalez and R.E. Woods, "Digital Image Processing", 2nd
Ed., Prentice Hall,
A. Rosenfeld
and A.C. Kak, "Digital Picture Processing",
2nd Ed., vols. 1 and 2, Academic Press, New York, NY, 1982.
M.D. Levine,
"Vision in Man and Machine",
K.R. Castleman, "Digital Image Processing",
Prentice-Hall,
A.K. Jain,
"Fundamentals of Digital Image Processing", Prentice-Hall,
W.K. Pratt,
"Digital Picture Processing", 2nd Ed., Wiley,
M. Sonka, V. Hlavac, and R. Boyle,
“Image Processing, Analysis, and Machine Vision”, 2nd Ed., Brooks/
Cole,
Evaluation
and grading: There will
be two tests (closed-book, closed-notes) during the term. Use of simple,
non-programmable calculators with no text storage facilities will be permitted.
You are
required to complete up to ten laboratory exercises to be assigned during the
course. A report, including illustration (one page each) and discussion of the
results obtained in each experiment (one page each), must be submitted at the
end of the course.
In lieu of a
final exam, you are required to work on a Digital Image Processing Project of
your choice. Projects must involve the development of algorithms for digital
image processing, computer programming for implementation of the algorithm, and
testing of the methods with real images from any application area of your
choice (such as medical imaging, remote sensing, robotics, or geophysical
exploration). The algorithms need not be original, but must be technically
advanced and sophisticated. If the project is a continuation or extension of
previous work, you should state clearly your additional work and findings in
the course project. The project must be completed before the end of the course.
A
full-fledged written project report and a seminar must be presented at the end
of the course. The project report must include a brief introductory review of
the subject area and problem, complete technical details of the methods
(equations, procedures, and algorithms) developed, critical analysis and
discussion of the results obtained, and references. More attention should be
paid to the image processing techniques developed than to the specific
application of interest in the project. The recommended length of the report is
15 double-space-printed pages, excluding illustrations and references.
Grading
details and deadlines:
One-page
project proposal: Monday, 25 February, 2008.
Two tests
(1.5 hours and 20 marks each): TBA.
Lab report
(10 marks): Monday, 14 April, 2008.
Project report
(50 marks): Wednesday, 16 April, 2008.
Seminars: 17
– 18 April, 2008. Exact time to be fixed later.
All
of the items listed above must be completed satisfactorily in order to obtain a
passing grade in the course.
Pre-requisites:
ENEL 327 Signals and Transforms OR: A working knowledge of linear algebra (vectors and matrices),
advanced calculus (complex variables, the Fourier transform), probability and
statistics, computer programming, linear system theory, and digital signal
processing.