Lecture: 3 hours
Discussion: 1 hour
Techniques for automated extraction of high-level information from images generated by cameras, three-dimensional surface sensors, and medical devices. Typical applications include detection of objects in various types of images and describing populations of biological specimens as they appear in medical imagery.
Prerequisite: (MAT 067 C- or better or MAT 022A C- or better); (ECS 060 or ECS 032B or ECS 036C)
Credit restrictions/cross listings: None
Summary of course contents
The course will be taught in three modules covering the fundamentals of automated analysis of photographic, three-dimensional surface, and volumetric imaging data.
Three programming projects will be assigned to allow students to implement algorithms for processing each of the 3 image modalities. (1) 2D images: Students will study and implement a selected algorithm for low-level image processing or multiple-view geometry estimation; (2) 3D surface data: Students will implement a selected algorithm for detection of 3D objects in radar image data; (3) Volumetric images: Students will implement an algorithm for modeling or detection of shapes in medical images.
Goals: Students should exit the course conversant in a range of state-of-the-art methods for extracting useful information from images. Students should also be able to write computer programs that extract such information.
Terry S. Yoo (Editor), Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis, AK Peters Publishers, 2004.
Three programming projects will be assigned to allow students to implement algorithms for processing each of the 3 image modalities.
The programming projects for this class are chosen to enhance the lecture material in the course.
Engineering Design Statement:
The individual student taking this class will design and document a set of software modules for automated analysis of a variety of image data that includes the well-established algorithms described in the course outline. Examinations will include questions based on design components of the course.
ABET Category Content:
Engineering Science: 0 unit
Engineering Design: 0 unit
GE3: Science & Engineering
Overlap: Volumetric images are also treated in ECS 177, but the emphasis there is on the visualization rather than automated extraction of high-level data. Two-dimensional image processing is treated in greater mathematical depth in EEC 206 and EEC 208, graduate courses which are currently taught infrequently. A graduate reading course, ECS 289H, covers these subjects in greater depth for a graduate audience.
Instructors: N. Amenta and O. Carmichael
History: Updated 9.7.2018 (CSUGA): Prerequisites updated to include new lower division ECS series courses. Reviewed by O. Carmichael (2012.10.25): Changed abbreviated name to “Image Processing.” Updated summary of course contents to include current lecture material. Added “Goals.” Prior course description from N. Amenta (March 2008).
|1||X||an ability to apply knowledge of mathematics, science, computing, and engineering|
|2||an ability to design and conduct experiments, as well as to analyze and interpret data|
|3||X||an ability to design, implement, and evaluate a system, process, component, or program to meet desired needs, within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability|
|4||an ability to function on multi-disciplinary teams|
|5||an ability to identify, formulate, and solve computer science and engineering problems and define the computing requirements appropriate to their solutions|
|6||an understanding of professional, ethical, legal, security and social issues and responsibilities|
|7||X||an ability to communicate effectively with a range of audiences|
|8||X||the broad education necessary to understand the impact of computer science and engineering solutions in a global and societal context|
|9||X||a recognition of the need for, and an ability to engage in life-long learning|
|10||knowledge of contemporary issues|
|11||X||an ability to use current techniques, skills, and tools necessary for computing and engineering practice|
|12||an ability to apply mathematical foundations, algorithmic principles, and computer science and engineering theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices|
|13||X||an ability to apply design and development principles in the construction of software systems or computer systems of varying complexity|