Courses

• 1051.204 Imaging in the Physical Sciences (Fall Quarter)
  
  Corequisites: none
  Credit Hours: 4
  Required Text: None
  Sumplementary Text: Imaging in the Physical Sciences, Edited by Maria Helguera and Joel Kastner, Kendall/Hunt, 2006
  Supplementary Text: Seeing the Light: Optics in Nature, Photography, Color, Vision and Holography, by David Falk, Dieter Brill, and David Stork, John Wiley and Sons, 1986.
  Course Objectives: This course presents a survey of the field of imaging science and its applications by examining representative imaging systems from the imaging chain perspective. Fundamental properties and characteristics of light, optics, and sensors, as well as fundamental principles of image processing, are presented and explored through lab experiments and through analysis of familiar imaging systems (e.g., traditional film and digital cameras, telescopes, medical X-ray systems, consumer video systems, copy machines, laser and ink-jet printers, and fax machines). Students explore how imaging techniques are applied to representative scientific problems from fields such as medical science, remote sensing, and astronomy.

Right-Click here to download the Biplane image
Right-Click here to download the RockA image
Right-Click here to download the RockB image
Right-Click here to download the Bars image


• 1051.706, 707, 708 Introduction to Imaging Science (Fall, Winter, Spring Quarters)
  
  Prerequisites: Graduate Standing
  Credit Hours: 1 each quarter (counts toward research credit requirement)
  Course Description: This three-quarter course sequence is focused on familiarizing students with research activities in the Chester F. Carlson Center for Imaging Science, research practices in the university, our research environment and policies and procedures impacting graduate students. The course is coupled with the research seminar sponsored by the Center for Imaging Science (weekly presentations). The students are expected to attend and participate in the seminar as part of the course. The course will also address issues and practices associated with technical presentation and technical writing. Credits earned in this course apply to research requirements.


• 1051.784 Pattern Recognition (next taught by Dr. Kerekes in Spring 2009)
  
  Prerequisites: Graduate standing, Linear Image Math and Programming
  Credit Hours: 4
  Text: Pattern Recognition, 3rd edition, 2006, by Theodoridis and Koutroumbas
  Course Objectives: This course develops a fundamental understanding of adaptive pattern recognition and a basic working knowledge of techniques that can be used in a broad range of applications. Inherent in adaptive patter recognition is the ability of the system to learn by supervised or unsupervised training, or by competition within a changing course environment. The effectiveness of a system depends upon its structure, adaptive properties and specifics of the application. Particular structures that are developed and analyzed include statistical pattern recognition, clustering, multilayered perceptrons (with a variety of weight training algorithms), and evolutionary learning systems. The goal is to gain both a fundamental and working knowledge of each kind of system and the ability to make a good selection when faced with real applications.
  Click here for the course syllabus from 2005
  
  MISI Image Data File
  MISI Image ENVI Header
  MISI Image JPEG
  
  Final Project Training Data
  Final Project Training Classes
  Final Project Testing Data
  
  
• 1051.753 Special Topics: Synthetic Aperture Radar Imaging (Next offered in Winter 2008/9)
  
  Prerequisites: Graduate standing, Linear Image Math and Programming
  Credit Hours: 4
  Text: Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach by Jakowatz, 1996 (new edition expected in 2008)
  Course Objectives: This course covers the history, fundamental principles, and system requirements for high-resolution spotlight synthetic aperture radar (SAR) imaging. Topics included are synthetic aperture radar concepts, surface and volume scattering, linear models of SAR signal history, image formation and processing algorithms, image quality requirements and SAR system performance, phase errors, and autofocus algorithms. The emphasis in the course is placed on understanding the fundamental principles of spotlight SAR imaging and upon the factors that drive the image quality of SAR products. Along the way, a variety of remote sensing and linear systems theory will be employed to provide specific insight into the following system performance metrics: image size, area rate, resolution, impulse response, noise equivalent backscatter, multiplicative and additive noise, residual quadratic phase error, dynamic range, depth of focus, geometric distortion, oversampling, and signal-to-noise ratio
  
  Homework 2 Test Data
  
  

Return to Dr. Kerekes' Home Page

Last updated: 19 November 2007