Online Learning at CIS

The Center for Imaging Science offers the master of science degree in imaging science to students as an online learning program. This program is identical to the M.S. degree offered on-campus with a few alterations in scheduling and course offerings which are described below.

This program is designed so that students can complete the degree in three to four years by taking one or two courses each quarter. See the schedule below for course availability.

For the MS program, students choose a specialization track. The Online Learning Program offers three tracks:

More information about the program is available in the links above and to the left and in the CIS Graduate Handbook. More information on the program is also available in the RIT graduate bulletin.

For information please contact Maria Helguera, helguera@cis.rit.edu, (585)475-7053. Please see our FAQ for answers to common questions.

For more information about Online Learning at RIT and enrolling in Online Learning courses, please visit their web page.

Information for New Students and Potential Applicants

Although our core course sequence begins in the Fall, interested students may still enroll for elective or specialty track courses or begin the application process at any time. They could then begin taking core courses the following fall. The permission of the instructor is required for entry into any class if a student is not matriculated. Please contact us if you have any questions.

Course Information

As mentioned above, the Online Learning MS requirements are identical to those of the local MS degree with the project/paper option. Students complete 45 credit hours of course work which are divided as follows:

Core Courses

The core courses consist of the following:

Specialty Tracks

The online learning program offers three areas of specialization. Unique tracks may be created with the consent of an advisor. Please check the availability of courses in the schedule below.

Electives

Elective courses may be chosen from other specialty tracks or from the list below:

There is the possibility of taking electives from departments other that CIS, such as the Center for Quality & Applied Statistics (CQAS) (see their Online Learning description and course listing for more information. Electives must be approved by the coordinator for credit.

Project/Paper

Students enroll in course 1051-840 MS Project Paper supervised by a particular faculty for a 1 credit project/paper. This can be done any quarter usually after a student has completed most of his specialty track courses.

Current Schedule for Academic Years 2004-2005 and 2005-2006

Please note that this schedule is subject to change:

Course Descriptions
Syllabi and sample course pages are linked where available.

1051-711 Basic Principles of Imaging Science I Instructor: Professor Richard Hailstone

This course is the first of a two-quarter sequence that provides the student with a basic understanding of the scientific principles associated with electromagnetic radiation propagation, image capture and formation, and image processing used to reproduce or display images. The first part of this course focuses on the image capture stage of the image chain. The fundamentals of the interaction between light and matter are covered. These concepts are then used to understand the operation and limitation of detectors, including charge-coupled devices and conventional film. The latter part of the course focuses on the image display stage of the image chain. Both mean level and spatial properties are discussed. The final part of the course ties together the basic principles covered and uses these to understand system design optimization. (Prerequisites: 1051-716 or concurrently) Credit 4 (F) Course Overview Course Syllabus Course Table of Contents

1051-712 Basic Principles of Imaging Science II Instructor: Dr. Navalgund Rao

This course continues the development of basic understanding of scientific principles associated with image capture, formation, and image processing used to reproduce or display images. An end-to-end treatment of an imaging system shall be employed to illustrate the interrelationships among the concepts introduced throughout the course. System analyses include the use of modeling concepts and image quality metrics to demonstrate how the concepts developed in Linear Image Mathematics can be used in concert with concepts in this course to describe and assess a simple imaging system. (Prerequisites: 717 or concurrently) Credit 4 (W)

1051-713 Noise & Random Processes Instructor: Professor Richard Hailstone

The purpose of this course is to develop an understanding and ability in modeling noise and random processes within the context of imaging systems. The focus will be on stationary random processes in both one dimension (time) and two dimensions (spatial). Power spectrum estimation will be developed and applied to signal characterization in the frequency domain. The effect of linear filtering will be modeled and applied to signal detection and maximization of SNR. The matched filter and the Wiener filter will be developed. Signal detection and amplification will be modeled, using noise figure and SNR as measures of system quality. At completion of the course, the student should have the ability to model signals and noise within imaging systems. (Prerequisites: 1051-716, 717, 711, 712) Credit 4 (S)

1051-714 Information Theory for Imaging Systems Instructor: Dr. Harvey Rhody

This course develops a basic understanding of the efficient representation of information for storage and transmission. Classical concepts of information theory are developed and applied to image compression, storage and transmission. The intent is to develop a foundation for the efficient handling of image-based information in imaging systems. (Prerequisites: 1051-716, 717, 711, 712) Credit 4 (S)

1051-716 Linear Image Math I Instructors: Dr. Navalgund Rao

This course develops the mathematical methods required to describe continuous and discrete linear systems, with special emphasis on tasks required in the analysis or synthesis of imaging systems: vector spaces, linear algebra and complex variables are reviewed. 1D and 2D real and complex valued special functions are introduced. The classification of systems as linear/nonlinear and shift variant/invariant is discussed. Credit 4 (F) Course Contents

1051-717 Linear Image Math II Instructors: TBD

This course continues the development of mathematical methods required to describe continuous and discrete linear systems. The convolution integral is presented, followed by a discussion of Fourier methods as applied to the analysis of linear systems. Emphasis is placed on the physical meaning and interpretation of these transform methods. Image sampling is introduced and discrete convolution and Fourier transform is considered. The course concludes with discussions of various applications of the mathematical models. (Prerequisites: 1051-716.) Credit 4 (W)

1051-726 Computing for Imaging Science Instructor: Dr. Harvey Rhody

A course to prepare graduate students in science and engineering to use computers as required by their disciplines. It covers advanced programming techniques, the design, implementation, and validation of large computer programs. At the end of the course the student will be proficient in IDL and Unix environment. Programming projects will be required. Credit 4 (W)

1051-736 Geometrical Optics Instructor: Mr. Robert MacIntyre

This course leads to a thorough understanding of the geometrical properties of optical imaging systems. A method is developed of performing a first-order design of an optical system, applicable to uniform and gaussian beams. The following topics are included: paraxial optics of axisymmetric systems, Gaussian optics (cardinal points, pupils and stops, optical invariant), propagation of energy through lens systems, basic optical instruments and components, gradient index optics, finite ray tracing, introduction to aberrations, geometrical optics of gaussian beams. Credit 4 (S)

1051-749 Color Reproduction Instructor: Dr. Mark Fairchild

This online learning course presents the concepts required for an understanding of the relationships between mean-level input and output in various color imaging systems. Analog, digital, and hybrid color imaging systems will be covered. Special emphasis will be given to mean-level reproduction in photography, printing, and television. Credit 4 (F) Course Outline & Examples

1051-761 Remote Sensing & Image Analysis: Radiometric Remote Sensing Instructor: Dr. John Schott

An introduction to radiometric concepts as they relate to remote sensing. The emphasis is on aerial and satellite imaging systems operating from 0.4-20 µm. After a brief review of the field, the basic radiometry concepts needed for remote sensing are introduced and a governing equation for radiance reaching the sensor is carefully derived. Remote Sensing imaging systems are introduced with an emphasis on design concepts and radiometric calibration. Credit 4 (F)

1051-762 Remote Sensing & Image Analysis II: Image Data Analysis Instructor: Dr. John Schott

The problem of inverting recorded data to surface reflectance or temperature values is treated using a variety of techniques, including the use of ground truth, "in scene" methods, and radiation propagation models. Multispectral digital image processing methods are introduced and their utility in various remote sensing applications considered. The potential for including multiple sources of data in image analysis is treated through consideration of multispectral image data fusion and the use of geographic information systems. (1051-761) Credit 4 (W)

1051-753 Remote Sensing: Imaging Spectroscopy Instructor: Dr. John Schott

Spectroscopic imaging sensors are introduced at the conceptual level with an emphasis on two or three specific sensors to provide detail on design trades. Instrument and atmospheric correction issues are reviewed with an emphasis on spectroscopic issues. Sensor noise and noise processing with an emphasis on spectrally correlated noise are presented in a form suitable for incorporation in data processing algorithms. The bulk of the course focuses on algorithms aimed at parameter retrieval, anomaly detection, and member selection, abundance mapping and target detection. (1051-761,762) Credit 4 (S)

1051-753 Ultrasound Imaging Instructor: Dr. Maria Helguera

An overview of the physics and signal processing principles of ultrasound as applied to the different medical imaging modalities such as B-mode, M-mode, Doppler, and 3D imaging will be introduced. (1051 717). Credit 4 (S)

1051-765 Remote Sensing Systems Instructor: Dr. John Schott

This course is designed to draw on the student's knowledge of linear system theory, digital image processing, and noise concepts and apply it to an end-to-end system in an area associated with remote sensing. Generalized concepts from these fields will be focused to show how they can be applied to solve remote sensing image analysis and system design and evaluation problems. The course also includes an extensive treatment of synthetic image generation and its use in system design studies and algorithm development and testing. Credit 4 (TBD)

1051-774 Vision & Psychophysics Instructor: Dr. Ethan Montag

This course provides an overview of the human visual system and psychophysical techniques used to investigate it. The optical, sensory, and neural aspects of vision and image quality are treated. Topics include color vision, adaptation, sensor response functions, neural networks, and an introduction to electro-optical and computational analogs. Credit 4 (F) Sample pages and syllabus

1051-775 Applied Colorimetry Instructors: Dr. Roy Berns

This course covers the principles of color science including theory and application. Topics include CIE colorimetry, the use of linear algebra for color transformations, the Munsell color order system, metamerism, color inconstancy, history and theory of color tolerance equations and spaces, and an overview of color management. Credit 4 (W)

1051-782 Introduction to Digital Image Processing Instructor: Dr. Maria Helguera

After a brief review of 2-dimensional signal processing, the course discusses the processing of images on a computer using the programming language IDL. It includes methods of contrast manipulation, image smoothing, and image sharpening using a variety of linear and non-linear methods. Also discussed are methods of edge and line enhancement and detection, followed by techniques of image segmentation.(1051-717 or permission of instructor) Credit 4 (F) Course Outline

1051-784 Digital Image Processing: Spatial Pattern Recognition Instructor: TBD

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 pattern recognition is the ability of then system to learn by supervised or unsupervised training, or by competition within a changing environment. The effectiveness of the system depends upon it structure, adaptive properties and specifics of the application. Particular structures that are developed and analyzed include statistical PR, clustering systems, fuzzy clustering systems, multilayered perceptrons (with a variety of weight training algorithms), and associative memory systems. The goal is to gain both a fundamental and working knowledge of each kind of system and the ability to make a good system selection when faced with a real application design. Credit 4 (S)

1051-816 Color Systems Instructor: Dr. Mitchell Rosen

This course covers the key techniques utilized in device-independent color imaging systems. Topics include: device calibration and characterization (input, output, display); device profiles; multidimensional look-up table construction, inversion and interpolation; gamut mapping; and, color-management systems. Credit 4 (S)

1051-840 MS Project Paper Instructor: By arrangement

The analysis and solution of Imaging Science Systems problems for students enrolled in Online Learning or Systems Capstone option. Credit 1

1051-753 Imaging with Wavelet Transforms (Special Topics) Instructor: Dr. Raghuveer Rao

Credit 4 (W) Course Outline

0307-834 Multivariate Statistics for Imaging Science Instructor: Dr. Peter Bajorski (offered through the Center for Quality & Applied Statistics)

Multivariate data are characterized by multiple responses. This course concentrates on the mathematical and statistical theory that underlies the analysis of multivariate data. Applications of these methods will be covered with emphasis on uses in Imaging Science. Credit 4 (W alternate years) Course Description