Imaging Science Ph.D.

Imaging Science, Ph.D. degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
IMGS-606
Graduate Seminar I
This course is focused on familiarizing students with research activities in the Carlson Center, research practices in the university, research environment and policies and procedures impacting graduate students. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course also addresses issues and practices associated with technical presentation and technical writing. Credits earned in this course apply to research requirements. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Seminar 1 (Fall).
1
IMGS-607
Graduate Seminar II
This course is a continuation of the topics addressed in the preceding course Imaging Science Graduate Seminar I. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course addresses issues and practices associated with technical presentations. Credits earned in this course apply to research requirements. (Prerequisites: IMGS-606 or equivalent course.) Seminar 1 (Spring).
1
IMGS-609
Graduate Laboratory I
This laboratory course is intended to familiarize graduate students with many concepts, tools, and techniques necessary for completion of the Imaging Science graduate curriculum. Students will work in a variety of areas including scientific programming, numerical analysis, imaging system analysis, and characterization. (Pre-requisite: Graduate standing in Imaging Science or permission of the instructor.) (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4 (Fall).
2
IMGS-613
Probability, Noise, and Systems Modeling
This course develops models of 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: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring).
3
IMGS-616
Fourier Methods for Imaging
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. The classification of systems as linear/nonlinear and shift variant/invariant, development and use of the convolution integral, Fourier methods as applied to the analysis of linear systems. The physical meaning and interpretation of transform methods are emphasized. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall).
3
IMGS-619
Radiometry
This course is focused on the fundamentals of radiation propagation as it relates to making quantitative measurements with imaging systems. The course includes an introduction to common radiometric terms and derivation of governing equations with an emphasis on radiation propagation in both non-intervening and turbid media. The course also includes an introduction to detector figures of merit and noise concepts. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall).
3
IMGS-620
The Human Visual System
This course describes the underlying structure of the human visual system, the performance of those structures and the system as a whole, and introduces psychophysical techniques used to measure them. The visual system's optical and neural systems responsible for collecting and detecting spatial, temporal, and spectral signals from the environment are described. The sources and extent of limitations in the subsystems are described and discussed in terms of the enabling limitations that allow practical imaging systems. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall).
3
IMGS-633
Optics for Imaging
This course provides the requisite knowledge in optics needed by a student in the graduate program in Imaging Science. The topics covered include the ray and wave models of light, diffraction, imaging system resolution. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring).
3
IMGS-682
Image Processing and Computer Vision
This course will cover a wide range of current topics in modern still digital image processing. Topics will include grey scale and color image formation, color space representation of images, image geometry, image registration and resampling, image contrast manipulations, image fusion and data combining, point spatial and neighborhood operations, image watermarking and steganography, image compression, spectral data compression, image segmentation and classification, and basic morphological operators. Projects will involve advanced computational implementations of selected topics from the current literature in a high level language such as Matlab or IDL and will be summarized by the students in written technical papers. (Prerequisites: IMGS-616 or equivalent course.) Lecture 3 (Spring).
3
 
IMGS Elective (optional)*
3
Second Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
1
 
IMGS Electives
12
Third Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
10
Fourth Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
10
Fifth Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
2 or 5*
Total Semester Credit Hours
60

* Students opting to take the optional Elective in the first year would take 2 units of IMGS-PHD in the final year. Students opting not to take the optional elective would take 5 units of IMGS-PHD in the final year.

Electives
Course Sem. Cr. Hrs.
ASTP-613
Astronomical Observational Techniques and Instrumentation
This course will survey multi-wavelength astronomical observing techniques and instrumentation. The design characteristics and function of telescopes, detectors, and instrumentation in use at the major ground based and space based observatories will be discussed as will common observing techniques such as imaging, photometry and spectroscopy. The principles of cosmic ray, neutrino, and gravitational wave astronomy will also be briefly reviewed. (Prerequisites: This course is restricted to students in the ASTP-MS and ASTP-PHD programs.) Lecture 3 (Fall).
3
CLRS-601
Principles of Color Science
3
CLRS-602
Color Physics and Applications
This course explores the relationship between a material’s color and its constituent raw materials such as colorants, binding media, substrates, and overcoats. These can be determined using a variety of physical models based on absorption, scattering, luminescence, and interference phenomena. These models enable the production of paints, plastics, colored paper, printing, and others to have specific colors. Accompanying laboratories will implement and optimize these models using filters, artist opaque and translucent paints and varnishes including metallic and pearlescent colorants, and inkjet printing. Statistical techniques include principal component analysis and linear and nonlinear optimization. (Prerequisites: CLRS-601 or equivalent course.) Lecture 3 (Spring).
3
CLRS-720
Computational Vision Science
Computational Vision Science This course provides an introduction to modern computer-based methods for the measurement and modeling of human vision. Lectures will introduce the experimental techniques of visual psychophysics including threshold measurement, psychometric functions, signal detection theory, and indirect, direct, and multidimensional scaling. Lectures will also introduce the MATLAB technical computing environment and will teach how to use MATLAB to run computer-based psychophysical experiments and to analyze experimental data and visualize results. Laboratory exercises will provide practical experience in using computer-based tools to conduct psychophysical experiments and to develop computational models of the results. Prior experience in vision science and/or scientific computing will be helpful but is not required. (Prerequisites: Graduate standing in CLRS-MS, IMGS-MS, CLRS-PHD or IMGS-PHD.) Lecture 3 (Fall).
3
CLRS-820
Modeling Visual Perception
This course presents the transition from the measurement of color matches and differences to the description and measurement of color appearance in complex visual stimuli. This seminar course is based mainly on review and student-led discussion of primary references. Topics include: appearance terminology, appearance phenomena, viewing conditions, chromatic adaptation, color appearance modeling, image appearance, image quality, and material appearance. (Prerequisites: CRLS-601 and CLRS-720 or equivalent courses.) Lecture 3 (Spring).
3
CSCI-603
Computational Problem Solving
This course focuses on the application of computational thinking using a problem-centered approach. Specific topics include: expression of algorithms in pseudo-code and a programming language; elementary data structures such as lists, trees and graphs; problem solving using recursion; and debugging and testing. Assignments (both in class and homework) requiring a pseudo-code solution and implementation in a programming language are an integral part of the course. Note: This course serves as a bridge course for graduate students and cannot be taken by undergraduate students without permission from the CS Undergraduate Program Coordinator. (This course is restricted to students in COMPSCI-MS.) Lecture 3 (Fall, Spring).
3
CSCI-630
Foundations of Artificial Intelligence
An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments and oral/written summaries of research papers are required. (Prerequisites:((CSCI-603 or CSCI-605) &CSCI-661) with grades of B or better or ((CSCI-243 or SWEN-262)&(CSCI-262 or CSCI-263)).If you have earned credit for CSCI-331 or you are currently enrolled in CSCI-331 you won't be permitted to enroll in CSCI-630.) Lecture 3 (Fall, Spring).
3
CSCI-631
Foundations of Computer Vision
An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit. (Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.) Lecture 3 (Fall, Spring).
3
CSCI-737
Pattern Recognition
An introduction to pattern classification and structural pattern recognition. Topics include Bayesian decision theory, evaluation, clustering, feature selection, classification methods (including linear classifiers, nearest-neighbor rules, support vector machines, and neural networks), classifier combination, and recognizing structures (e.g. using HMMs and SCFGs). Students will present current research papers and complete programming projects such as optical character recognizers. Offered every other year. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lecture 3 (Fall).
3
EEEE-780
Digital Video Processing
In this graduate level course the following topics will be covered: Representation of digital video - introduction and fundamentals; Time-varying image formation models including motion models and geometric image formation; Spatio-temporal sampling including sampling of analog and digital video; two dimensional rectangular and periodic Sampling; sampling of 3-D structures, and reconstruction from samples; Sampling structure conversion including sampling rate change and sampling lattice conversion; Two-dimensional motion estimation including optical flow based methods, block-based methods, Pel-recursive methods, Bayesian methods based on Gibbs Random Fields; Three-dimensional motion estimation and segmentation including methods using point correspondences, optical flow & direct methods, motion segmentation, and stereo and motion tracking. (Prerequisites: EEEE-779 or equivalent course.) Lecture 3 (Spring).
3
ENVS-650
Hydrologic Applications of Geographic Information Systems
Aerial photography, satellite imagery, Global Positioning Systems (GPS), and Geographic Information Systems (GIS) are extremely useful tools in hydrologic modeling and environmental applications such as rainfall runoff modeling, pollution loading, landscape change analyses, and terrain modeling. This course will: 1) introduce students to spatial analysis theories, techniques and issues associated with hydrologic and environmental applications; 2) provide hands-on training in the use of these spatial tools and models while addressing a real problem; 3) provide experience linking GIS and model results to field assessments and monitoring activities; 4) enable students to solve a variety of spatial and temporal hydrologic and environmental problems; and 5) provide tools useful for addressing environmental problems related to the graduate thesis or project. (Prerequisites: ENVS-250 or equivalent course or graduate standing in the ENVS-MS program.) Lec/Lab 6 (Spring).
4
IMGS-606
Graduate Seminar I
This course is focused on familiarizing students with research activities in the Carlson Center, research practices in the university, research environment and policies and procedures impacting graduate students. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course also addresses issues and practices associated with technical presentation and technical writing. Credits earned in this course apply to research requirements. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Seminar 1 (Fall).
1
IMGS-607
Graduate Seminar II
This course is a continuation of the topics addressed in the preceding course Imaging Science Graduate Seminar I. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course addresses issues and practices associated with technical presentations. Credits earned in this course apply to research requirements. (Prerequisites: IMGS-606 or equivalent course.) Seminar 1 (Spring).
1
IMGS-609
Graduate Laboratory I
This laboratory course is intended to familiarize graduate students with many concepts, tools, and techniques necessary for completion of the Imaging Science graduate curriculum. Students will work in a variety of areas including scientific programming, numerical analysis, imaging system analysis, and characterization. (Pre-requisite: Graduate standing in Imaging Science or permission of the instructor.) (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4 (Fall).
2
IMGS-613
Probability, Noise, and System Modeling
This course develops models of 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: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring).
3
IMGS-616
Fourier Methods for Imaging
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. The classification of systems as linear/nonlinear and shift variant/invariant, development and use of the convolution integral, Fourier methods as applied to the analysis of linear systems. The physical meaning and interpretation of transform methods are emphasized. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall).
3
IMGS-619
Radiometry
This course is focused on the fundamentals of radiation propagation as it relates to making quantitative measurements with imaging systems. The course includes an introduction to common radiometric terms and derivation of governing equations with an emphasis on radiation propagation in both non-intervening and turbid media. The course also includes an introduction to detector figures of merit and noise concepts. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall).
3
IMGS-620
The Human Visual System
This course describes the underlying structure of the human visual system, the performance of those structures and the system as a whole, and introduces psychophysical techniques used to measure them. The visual system's optical and neural systems responsible for collecting and detecting spatial, temporal, and spectral signals from the environment are described. The sources and extent of limitations in the subsystems are described and discussed in terms of the enabling limitations that allow practical imaging systems. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall).
3
IMGS-622
Vision Sciences Seminar
This seminar course provides a forum in which students, faculty, and researchers with an interest in the Vision Sciences (visual neuroscience, perception psychology, computational vision, computer graphics) can interact through reading, presentation, and discussion of classic texts and contemporary research papers in the field. Students will read and summarize weekly readings in writing and will periodically prepare presentations and lead discussions. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 1 (Fall, Spring).
1
IMGS-624
Interactive Virtual Env
This course provides experience in the development of real-time interactive three-dimensional environments, and in the use of peripherals, including virtual reality helmets, motion tracking, and eye tracking in virtual reality. Students will develop expertise with a contemporary Game Engine, along with an understanding of the computations that facilitate 3D rendering for interactive environments. Projects will cover topics such as lighting and appearance modelling, mathematics for vertex manipulation, 3D to 2D projection, ray tracing, the integration of peripherals via software development kits, and the spatial and temporal calibration of an eye tracker embedded within a head-worn display. Students will complete homework tutorials on game/application development in a contemporary computer gaming engine. This course involves a substantial programing component, and prior programming experience is required. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4, Lecture 1 (Fall).
3
IMGS-628
Design and Fabrication of Solid State Cameras
The purpose of this course is to provide the student with hands-on experience in building a CCD camera. The course provides the basics of CCD operation including an overview, CCD clocking, analog output circuitry, cooling, and evaluation criteria. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lab 6, Lecture 1 (Fall).
3
IMGS-632
Advanced Environmental Applications of Remote Sensing
This course will focus on a broader selection of analytical techniques with an application-centric presentation. These techniques include narrow-band indices, filtering in the spatial and frequency domains, principal component analysis, textural analysis, hybrid and object-oriented classifiers, change detection methods, and structural analysis. All of these techniques are applied to assessment of natural resources. Sensing modalities include imaging spectroscopy (hyperspectral), multispectral, and light detection and ranging (lidar) sensors. Applications such as vegetation stress assessment, foliar biochemistry, advanced image classification for land use purposes, detecting change between image scenes, and assessing topography and structure in forestry and grassland ecosystems (volume, biomass, biodiversity) and built environments will be examined. Real-world remote sensing and field data from international, US, and local sources are used throughout this course. Students will be expected to perform a more comprehensive final project and homework assignments, including literature review and discussion and interpretation of results. (This course requires permission of the Instructor to enroll.) Lab 3, Lecture 2 (Spring).
3
IMGS-633
Optics for Imaging
This course provides the requisite knowledge in optics needed by a student in the graduate program in Imaging Science. The topics covered include the ray and wave models of light, diffraction, imaging system resolution. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring).
3
IMGS-635
Optical System Design and Analysis
The primary objectives of this course are to teach critical optics and system concepts, and skills to specify, design, simulate, and evaluate optical components and systems. A modern optical design program and various types of optical systems will be used to illustrate how to solve real-world optical engineering problems. The course is not a traditional lens design course, which usually focuses on designing and optimizing individual lens elements. Instead the course will emphasize analyzing systems, which are often made with off-the-shelf optical components. (Prerequisites: IMGS-321 or IMGS-633 or (EEEE-505 and EEEE-705) or (IMGS-322 or PHYS-365) or equivalent course.) Lecture 1 (Spring).
3
IMGS-639
Principles of Solid State Imaging Arrays
This course covers the basics of solid state physics, electrical engineering, linear systems and imaging needed to understand modern focal plane array design and use. The course emphasizes knowledge of the working of CMOS and infrared arrays. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
3
IMGS-640
Remote Sensing Systems and Image Analysis
This course introduces the students to the governing equations for radiance reaching aerial or satellite based imaging systems. It then covers the temporal, geometric, spectral, and noise properties of these imaging systems with an emphasis on their use as quantitative scientific instruments. This is followed by a treatment of methods to invert the remotely sensed image data to measurements of the Earth’s surface (e.g. reflectance and temperature) through various means of inverting the governing radiometric equation. The emphasis is on practical implementation of multidimensional image analysis and examining the processes governing spatial, spectral and radiometric image fidelity. (Prerequisite: IMGS-251 or equivalent course.) Lecture 3 (Fall).
3
IMGS-642
Testing of Focal Plane Arrays
This course is an introduction to the techniques used for the testing of solid state imaging detectors such as CCDs, CMOS and Infrared Arrays. Focal plane array users in industry, government and university need to ensure that key operating parameters for such devices either fall within an operating range or that the limitation to the performance is understood. This is a hands-on course where the students will measure the performance parameters of a particular camera in detail. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lab 6, Lecture 1 (Spring).
3
IMGS-682
Image Processing and Computer Vision
This course will cover a wide range of current topics in modern still digital image processing. Topics will include grey scale and color image formation, color space representation of images, image geometry, image registration and resampling, image contrast manipulations, image fusion and data combining, point spatial and neighborhood operations, image watermarking and steganography, image compression, spectral data compression, image segmentation and classification, and basic morphological operators. Projects will involve advanced computational implementations of selected topics from the current literature in a high level language such as Matlab or IDL and will be summarized by the students in written technical papers. (Prerequisites: IMGS-616 or equivalent course.) Lecture 3 (Spring).
3
IMGS-684
Deep Learning for Vision
This course will review neural networks and related theory in machine learning that is needed to understand how deep learning algorithms work. The course will include the latest algorithms that use deep learning to solve problems in computer vision and machine perception, and students will read recent papers on these systems. Students will implement and evaluate one or more of these systems and apply them to problems that match their interests. Students are expected to have taken multiple computer programming courses and to be comfortable with linear algebra and calculus. No prior background in machine learning or pattern recognition is required. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
3
IMGS-699
Imaging Science Graduate Co-op
This course is a cooperative education experience for graduate imaging science students. CO OP (Fall, Spring, Summer).
0
IMGS-711
Computational Methods for Imaging Science
This course addresses computational topics that are important in a variety of applications in imaging science. Examples of topics that may be included are: vector space operations, including matrix factorizations and solutions of systems of equations (used in hyperspectral target detection and image compression, among many other applications); linear and nonlinear optimization (used for the design of detectors, camera calibration, bundle adjustment, etc.); iterative methods and dynamic systems (Kalman filtering, tracking, optical flow, etc.); random number generation and use (Monte Carlo methods, system performance evaluation, etc.); and energy minimization techniques applied to image processing (used for image enhancement, segmentation, etc.) (Prerequisites: IMGS-616 or IMGS-682 or equivalent course.) Lecture 3 (Fall).
3
IMGS-712
Multi-view Imaging
Images are 2D projections gathered from scenes by perspective projection. By making use of multiple images it is possible to construct 3D models of the scene geometry and of objects in the scene. The ability to derive representations of 3D scenes from 2D observations is a fundamental requirement for applications in robotics, intelligence, medicine and computer graphics. This course develops the mathematical and computational approaches to modeling of 3D scenes from multiple 2D views. After completion of this course students are prepared to use the techniques in independent research. (Prerequisites: IMGS-616 or IMGS-682 or equivalent course.) Lecture 3 (Spring).
3
IMGS-715
Computational Photography
Computational photography is an emerging field that aims to overcome the limitations of conventional digital imaging and display devices by using computational techniques and novel programmable sensors and optical devices. In this course, we will study start-of-the-art techniques for capturing, modeling, and displaying complex appearance phenomena. We will cover topics such as computational sensor with assorted pixel designs, mobile camera control, light field capture and rendering, computational flash photography, computational illumination for appearance modeling and 3D reconstruction, light transport analysis, and light sensitive display and printing techniques. We will integrate the latest smart imaging devices into the course for homework and term projects. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering.) Lecture 3 (Fall).
3
IMGS-719
Radiative Transfer I
This course is the first course in a two-semester course sequence that covers the theory of radiative transfer in disordered media. The course begins with a brief review of basic electromagnetism and models for scattering and absorption by single particles and progresses to the theory of radiative transfer in semi-infinite media. Various approximations that allow closed-form solutions are presented, and related phenomenology, such as the shadow-hiding opposition effect and coherent backscatter opposition effects, are described in terms of these models. (Prerequisites: IMGS-619 and IMGS-633 or ASTP-615 or equivalent courses.) Lecture 3 (Spring).
3
IMGS-720
Radiative Transfer II
This course covers advanced topics related to the theory of radiative transfer in disordered media. The course begins with a review of topics presented in the first semester course, including the radiative transfer solutions due to Hapke’s solution for a semi-infinite medium and the opposition effect. Students will complete a project focused on one or more advanced topics related to radiative transfer in disordered media, such as effects of surface roughness, scattering in layered media, oriented scattering layers, more advanced treatments of multiple scattering or polarization, or radiative transfer in the water column. (Prerequisites: IMGS-719 or equivalent course.) Lecture 3 (Spring).
3
IMGS-723
Remote Sensing: Spectral Image Analysis
This course is focused on analysis of high-dimensional remotely sensed data sets. It begins with a review of the properties of matter that control the spectral nature of reflected and emitted energy. It then introduces three mathematical ways to characterize spectral data and methods to perform initial analysis of spectral data to characterize and preprocess the data. These include noise characterization and mitigation, radiometric calibration, atmospheric compensation, dimensionality characterization, and reduction. Much of the course focuses on spectral image analysis algorithms employing the three conceptual approaches to characterizing the data. These analytical tools are aimed at segmentation, subpixel or pixel unmixing approaches and target detection including treatment of signal processing theory and application. There is also a significant emphasis on incorporation of physics based algorithms into spectral image analysis. The course concludes with an end-to-end treatment of image fidelity incorporating atmospheres, sensors, and image processing effects. (Prerequisites: IMGS-619 and IMGS-722 or equivalent courses.) Lecture 3 (Fall).
3
IMGS-724
Introduction to Electron Microscopy
The course will introduce the basic concepts and practice of electron microscopy, including transmission electron microscopy (TEM), scanning electron microscopy (SEM) and x-ray microanalysis. During the second half of the course students will do an 8-10 hour hands-on project in SEM or TEM or both, including a project paper and a poster presentation. Laboratory demonstrations will be held in the NanoImaging Lab to reinforce the lecture material. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering.) Lecture 3 (Spring).
3
IMGS-729
Photogrammetry for Airborne and Space Systems
3-4
IMGS-730
Magnetic Resonance Imaging
This course is designed to teach the principles of the imaging technique called magnetic resonance imaging (MRI). The course covers spin physics, Fourier transforms, basic imaging principles, Fourier imaging, imaging hardware, imaging techniques, image processing, image artifacts, safety, and advanced imaging techniques. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Spring).
3
IMGS-737
Physical Optics
This course covers the wave properties of light, its interaction with matter, and the application of these principles to imaging systems. Topics include polarization of light, birefringence, interference and interferometers, spatial and temporal coherence, and scalar diffraction theory. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lab 3, Lecture 2 (Spring).
3
IMGS-740
Imaging Science MS Systems Project Paper
The analysis and solution of imaging science systems problems for students enrolled in the MS Project capstone paper option. Research 3 (Fall, Spring, Summer).
3
IMGS-754
Pattern Recognition
This course develops a fundamental understanding of adaptive pattern recognition and a basic working knowledge of techniques for use in a broad range of applications. Inherent in adaptive pattern recognition is the ability of the system to learn by supervised or unsupervised training, or by competition within a changing environment. The effectiveness of the system depends upon its structure, adaptive properties, and specifics of the application. Particular structures developed and analyzed include Bayes decision theory, parametric and non-parametric techniques, multilayer perceptrons, and unsupervised clustering methods. The goal is to gain both a fundamental and working knowledge of each kind of technique and the ability to select the most appropriate one when faced with a real application design. (Prerequisites: IMGS-613 or equivalent course.) Lecture 3 (Spring).
3
IMGS-756
Advanced Digital Image Processing
This course investigates algorithms and techniques for a variety of imaging applications. The techniques build on the background from IMGS-682. The course is taught using a lecture and group project format, in which the lectures focus on advanced techniques and provide applications of their use in selected applications. The group projects enable students to work on substantial designs that require the understanding of the task domain, exploration of solution methods by analysis and prototyping, and implementation of a selected approach. Each team presents a preliminary plan, an approach with feasibility analysis, and a final demonstration. (Prerequisites: IMGS-682 or equivalent course.) Lecture 3 (Fall).
3
IMGS-765
Performance Modeling and Characterization of Remote Sensing System
This course introduces the techniques utilized for system performance predictions of new imaging platforms during their design phase. Emphasis will be placed on systems engineering concepts and their impact on final product quality through first principles modeling. In addition, the student will learn techniques to characterize system performance during actual operation to monitor compliance to performance specifications and monitor system health. Although the focus of the course will be on electro-optical collection systems, some modality specific concepts will be introduced for LIDAR, broadband infrared, polarimetric, and hyperspectral systems. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring).
3
IMGS-766
Geometric Optics and Lens Design
This course leads to a thorough understanding of the geometrical properties of optical imaging systems and detailed procedures for designing any major lens system. Automatic lens design, merit functions, and optimization are applied to real design problems. The course will utilize a modern optical design program and examples carried out on a number of types of lenses to illustrate how the process of design is carried out. (Prerequisites: IMGS-633 or equivalent course.) Lab 2, Lecture 2 (Fall).
3
IMGS-789
Graduate Special Topics: Robot Vision
This is a graduate-level course on a topic that is not part of the formal curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lec/Lab (Fall, Spring, Summer).
1-3
IMGS-790
Research & Thesis
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
1-6
IMGS-797
Principles of Computed Tomographic Imaging
3
IMGS-799
Imaging Science Independent Study
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their graduate studies. (Enrollment in this course requires permission from the department offering the course.) Ind Study (Fall, Spring, Summer).
1-4
IMGS-830
Advanced Topics in Remote Sensing
This course is an in-depth examination of emerging techniques and technologies in the field of remote sensing at an advanced level. Examples of topics, which will differ each semester, are typically formed around a specific remote sensing modality such as lidar, polarimetry, radar, and hyperspectral remote sensing. (Prerequisites: IMGS-723 or equivalent course.) Lecture 3 (Spring).
3
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
1-6
MATH-605
Stochastic Processes
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows. (Prerequisites: ((MATH-241 or MATH-241H) and MATH-251) or equivalent courses or graduate standing in ACMTH-MS or MATHML-PHD or APPSTAT-MS programs.) Lecture 3 (Spring).
3
MATH-645
Graph Theory
This course introduces the fundamental concepts of graph theory. Topics to be studied include graph isomorphism, trees, network flows, connectivity in graphs, matchings, graph colorings, and planar graphs. Applications such as traffic routing and scheduling problems will be considered. (This course is restricted to students with graduate standing in the College of Science or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
3
MATH-711
Advanced Methods in Scientific Computing
3
MCSE-712
Nonlinear Optics
This course introduces nonlinear concepts applied to the field of optics. Students learn how materials respond to high intensity electric fields and how the materials response: enables the generation of other frequencies, can focus light to the point of breakdown or create waves that do not disperse in time or space solitons, and how atoms can be cooled to absolute zero using a(laser. Students will be exposed to many applications of nonlinear concepts and to some current research subjects, especially at the nanoscale. Students will also observe several nonlinear-optical experiments in a state-of-the-art photonics laboratory. (Prerequisites: EEEE-374 or equivalent course or graduate student standing in the MCSE-PHD program.) Lecture 3 (Spring).
3
MCSE-713
Lasers
This course introduces students to the design, operation and (applications of lasers (Light Amplification by Stimulated Emission of (Radiation). Topics: Ray tracing, Gaussian beams, Optical cavities, (Atomic radiation, Laser oscillation and amplification, Mode locking and Q switching, and Applications of lasers. (Prerequisites: EEEE-374 or equivalent course or graduate student standing in the MCSE-PHD program.) Lecture 3 (Fall).
3
MCSE-731
Integrated Optical Devices & Systems
This course discusses basic goals, principles and techniques of integrated optical devices and systems, and explains how the various optoelectronic devices of an integrated optical system operate and how they are integrated into a system. Emphasis in this course will be on planar passive optical devices. Topics include optical waveguides, optical couplers, micro-optical resonators, surface plasmons, photonic crystals, modulators, design tools and fabrication techniques, and the applications of optical integrated circuits. Some of the current state-of-the-art devices and systems will be investigated by reference to journal articles. Lecture 3 (Fall).
3
STAT-641
Applied Linear Models - Regression
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
3
STAT-758
Multivariate Statistics for Imaging Science
This course introduces multivariate statistical techniques and shows how they are applied in the field of Imaging Science. The emphasis is on practical applications, and all topics will include case studies from imaging science. Topics include experimental design and analysis, the multivariate Gaussian distribution, principal components analysis, singular value decomposition, orthogonal subspace projection, cluster analysis, canonical correlation and canonical correlation regression, regression, multivariate noise whitening. This course is not intended for CQAS students unless they have particular interest in imaging science. CQAS students should be taking the course STAT-756-Multivariate Analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS, SMPPI-ACT, IMGS-MS, IMGS-PHD, CLRS-MS or CLRS-PHD.) Lecture 3 (Summer).
3