EECS107 FUNDAMENTALS OF DIGITAL IMAGE PROCESSING
Catalog Data: EECS107 Fundamentals of Digital Image Processing (Credit Units: 4)
S. Introduces theory and practice of digital image processing. Topics
presented include two-dimensional signal processing theory, image
acquisition, representation, elementary operations, enhancement, filtering,
coding, compressing, restoration, and analysis, as well as image
processing hardware. Prerequisite: EECS152A or consent of instructor.
EECS107 and ICS 181 may not both be taken for credit. Formerly
ECE107. (Design units: 2)
Textbook: Seul, Michael, O’Gorman, Lawrence, and Sammon, Michael J., Practical
Algorithms for Image Analysis: Descriptions, Examples, and Code, ISBN:
References: Class notes (online), research articles, handouts.
Coordinator: Joerg Meyer
Course Objectives: Students should be familiar and comfortable with: Fundamentals of image
acquisition techniques; Digital image representation and formats;
Mathematical principles of image enhancement methods; Detailed
understanding of feature extraction algorithms; Broader understanding of
digital image analysis, digital image compression, and efficient storage;
Sufficient knowledge for further work in all areas of engineering that
require image processing and digital image data analysis.
Course Outcomes: Students will be able to:
Describe different modalities and current techniques in image acquisition
Describe how digital images are represented and stored efficiently
depending on the desired quality, color depth, dynamics (time-varying
Use the mathematical principles of digital image enhancement (contrast,
Describe and apply the concepts of feature detection and contour finding
Analyze the constraints in image processing when dealing with larger data
sets (efficient storage and compression schemes).
Apply the knowledge primarily obtained by studying examples and cases
in the field of biomedical imaging to other engineering disciplines.
Prerequisites By Topic:
Lecture Topics: Introduction, Image Representations
Image Enhancement, Histogram Transformation, Grayscale and Color
Image Enhancement Filters
Edge Detection; Region Detection
Thinning, Point and Line Extraction, Hough Transform
Line Fitting; Spline Fitting
Voronoi Diagram, Tesselation, Triangulation
Discrete Fourier Transform
Frequency Domain Filtering
Wavelet Transform (1); Wavelet Transform (2)
3-D Image Transformations; 3-D Feature Detection
Image Coding and Storage
Wavelet Compression, Progressive Transmission
Class Schedule: Each class meets 3 hours per week for 10 weeks and students are assigned
to a 3 hour lab session per week.
Computer Usage: Computer instruction lab.
Professional Component: Contributes toward the Computer Engineering Topics Courses and Major
Relationship to Program Outcomes: This course relates to Program Outcomes 1, 2, 3, 6 and 8 as
stated at: http://www.eng.uci.edu/dept/objective_computer.
Design Content Description
Approach: The students will learn about state-of-the-art techniques in the lecture, and
experiment with selected methods during the lab sessions. The computer lab session is an
essential part of this course as it is supposed to lower the barrier and the reservations that
some students might have towards using computer-based biomedical imaging technology for
their own work. Presentation of material by the instructor, interactive discussion of possible
approaches, guided discussion of existing and possible future solutions. The lecture covers
the engineering science material necessary for the design project and represents 50% of the
design experience. Hands-on experience using existing algorithms and simple programming
or program extension tasks (scripting, batch processing). No explicit programming skills
required. The laboratory portion covers the engineering design methods and helps the
students to design their own image processing pipeline. The methods are based on the
material covered in the lecture, and after laying out and selecting an approach in the lecture,
the laboratory represents 50% of the design experience.
Laboratory Portion: 50%
Midterm exam: 30%
Lab projects: 40%
Final exam/project: 30%
Estimated ABET Category Content:
Mathematics and Basic Science: ___0 credit units or ___0%
Engineering Science: ___2 credit units or ___50%
Engineering Design: ___2 credit units or ___50%
Prepared by: Joerg Meyer Date: July 2004
CEP Approved: Fall 2004