CIS Special Topics Offerings







1051-253-01 ST: Freshman Imaging Project, Instructor: Joe Pow

Freshman Imaging Project is a year-long sequence of courses built around a single laboratory-based design effort aimed at developing and building a functional imaging system. With the help of faculty and staff from across the imaging science program, students will work together as a unified team to plan and organize the effort, conduct trade studies to assess technology options, and validate that the resulting system meets desired levels of performance. Along the way the students will develop a general understanding of the foundational concepts of imaging science, an in-depth knowledge of at least one aspect of imaging science, a working knowledge of the principles of systems engineering, and proficiency in oral and technical communication. (Permission of instructor)

1051-253-02ST: Earth Systems Dynamics I, Instructor: Jan vanAardt

This course is the first of a two-course sequence, general elective offering that will expose students to earth systems dynamics, i.e., the lithosphere, hydrosphere, atmosphere, and terrestrial components, and their interactions at a global scale. The course also offers introductions to regional and local scale interactions, as well as societal impacts, e.g., science, engineering, policy, and economics. We will focus on the underlying science of these system components, how they fluctuate, interact, and what this means for society as a whole. This will include theoretical background, guest lecturers, class discussion centered on prominent topics, e.g., global warming and the science behind this, and a class project that focuses on global scale interactions and their relevance to scientific, engineering, social, and economic endeavors. Enrollment limited to 20 students Credit 4 (W) (Honors course or permission of instructor; no prerequisites)

1051-553-01    ST: Remote Sensing Systems, Instructor: John Kerekes

This course develops knowledge and understanding of the design and analysis of optical remote sensing systems for Earth remote sensing. Building on general imaging fundamentals learned earlier in their program, students will learn domain specific tools and techniques for analyzing airborne and satellite sensor systems for the optical spectral imaging of Earth. Through a combination of classroom and laboratory experiences, students will learn about the propagation of photons and signals from the Sun through the formation of a digital image. The course will emphasize a linear systems modeling perspective and provide the students the background to understand, model, and predict remote sensing imaging system performance. (1051-370, 1051-452, 1051-453, or permission of instructor)  Class 4, Credit 4 (W)

Text: John R. Schott, Remote Sensing: The Image Chain Approach, 2nd Edition Academic Press

1051-553-02 ST: Imaging Lab II, Instructor: Jinwei Gu

This course is the second part of a three-course lab sequence designed to develop laboratory skills in imaging system characterization and design. These skills include measuring and modeling characteristics of complex imaging systems, designing and evaluating practical imaging systems, programming data analysis routines, and writing research reports in a technical format suitable for publication. Specifically, this course will have laboratories covering the measurement of spatial characteristics of complex imaging systems. Students will learn practical skills for measuring resolution and modulation transfer function (MTF), evaluating the non-uniformity of light source and display, and characterization of a projector-camera system.  (Imaging Systems Lab I)

1051-553-03       ST: Probability and Statistics for Image Science, Instructor: Rich Hailstone

This course is an introduction to probability and statistics. The first half of the course will cover probability distributions for discrete and continuous random variables, expectation, variance, and joint distributions. The second half of the course will cover point estimation, statistical intervals, hypothesis testing, and inference. (1016-283 Project-based Calculus III or equivalent required) Class 4, Credit 4 (W)

2065-552-70       Digital Color Management, Instructor: Ed Giorgianni T/R 6:00 to 7:50 PM

This course offers a comprehensive study of the methods and techniques used to manage and interchange color in modern digital color-imaging systems. The principles of colorimetry and densitometry will be reviewed and applied specifically to color imaging applications. The fundamental colorimetric properties of color imaging media, devices and systems will be explored and compared. Digital color encoding principles will be examined, and the features and limitations of various digital color encoding methods will be described. The three basic paradigms underlying all color managed systems will be discussed, and a new unified paradigm that encompasses all three basic paradigms will be introduced. The color encoding requirements and associated colorimetric transformations required to support that paradigm will be discussed. Various simple and complex systems based on the unified paradigm, including a Digital Cinema system currently being developed by AMPAS and SMPTE, will be described.  Prerequisite: 1051-402 (Color Science) Class 4, Credit 4 (W)



1051-753-01       Computational Methods for Imaging Science, Instructor: Harvey Rhody

Computational science is a recognized general discipline for constructing mathematical models and numerical techniques to simulate and analyze scientific, engineering and social problems. Computational methods for imaging science uses the same techniques but focuses on problems that arise in the field of imaging science. The purpose is to gain insight and understanding to enable the development of imaging systems to solve problems in a broad range of applications that use imaging as a fundamental sensing and visualization component. Graduate standing. Class 4, Credit 4

1051-753-02             Introduction to Multi-view Imaging, Instructor: Harvey Rhody

Multi-view imaging has roots in computer vision, photogrammetry and sensor fusion and is important for applications such as scene modeling, scene understanding, robot navigation, and use of information from any collection of sensors that include geometric information in their products. Application domains include remote sensing, medical imaging, visually assisted navigation, and virtual scene reconstruction. Computational techniques are constructed with a mathematical framework that is based upon the perspective geometry of multiple views. This course covers this mathematical background and related computational techniques. Each topic is accompanied by exercises to enlighten and develop both the theory and implementation of algorithms for basic techniques such as image registration, camera calibration, recovery of scene geometry, image fusion, use of LIDAR data with images and construction of synthesized scene views. Expected background is knowledge of algorithms for processing individual images, programming skills in Matlab or IDL, and knowledge of linear vector space mathematics. Prerequisite: Graduate standing and 1051-782 Digital Image Processing or equivalent, or permission of instructor. Class 4, Credit 4

Text: Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, 2nd Ed., Cambridge, 2003

Last Modified: 5:05pm 31 Oct 11