COURSE TITLE: EECS 332 Digital Image Analysis

CATALOG DESCRIPTION: Introduction to computer and biological vision systems, image formation, edge detection, image segmentation, texture, representation and analysis of two-dimensional geometric structures, and representation and analysis of three-dimensional structures.

REQUIRED TEXT: None

REFERENCE TEXT: R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision , McGraw-Hill, Inc. 1995.

READINGS : Papers from journals, conference proceedings, or book chapters will be assigned.

COURSE COORDINATOR: Ying Wu

COURSE GOALS: The goal of this course is to provide students with a basic understanding of the fundamentals and applications of digital image analysis (or computer vision) techniques including 2-D and 3-D paradigms to solve real world applications.

PREREQUISITES : EECS 230

PREREQUISITES BY TOPIC :

•  Linear algebra

•  Probability

•  Computer programming in C

DETAILED COURSE TOPICS :

1. Introduction to image formation (1 week)
2. Binary image processing (2 weeks)
3. Color and color segmentation (1 week)
4. Region segmentation (1 week)
5. Edge, contour, Hough transform and texture (2 weeks)
6. Motion and tracking (1 week)
7. 3D geometry, calibration, pose and stereo (1 week)
8. Lighting and applications (1 week)

MACHINE PROBLEMS:

1. Implementation of connect component analysis
2. Implementation of morphological operators
3. Implementation of histogram equalization and lighting compensation
4. Implementation of color segmentation
5. Implementation of canny edge detector
6. Implementation of Hough transform.
7. Implementation of camera calibration
8. Implementation of 3D pose determination

FINAL PROJECTS:

Based on the machine problems, the course involves a final project to design a vision-based interface system, i.e., a “virtual gun,” where the cursor moves with your fingertips. The idea is to locate and track a fingertip through a video sequence accurately and robustly. The project consists of three parts: (1) a working demo, (2) a 15-minute presentation, and (3) a 15-page report.

GRADES:

Machine problems – 50%

Final project – 50%

COURSE OBJECTIVES: When a student completes this course, s/he should be able to:

1. Understand the projection geometry in the image formation process.
2. Design and implement computer programs to perform image feature extraction.
3. Design and implement computer programs for image segmentation.
4. Design and implement computer programs for motion analysis and tracking.
5. Understand the basic techniques and issues in 3-D computer vision.
6. Design and build a real vision-based interaction system.

ABET CONTENT CATEGORY: 100% Engineering (Design component).