Imaging science is a highly interdisciplinary field of study that incorporates elements from mathematics, engineering, computer science, and physics to understand, design, and utilize imagery and imaging systems to study scientific phenomena. The imaging science minor is designed to allow students from various departments across RIT to study how to use imaging to enhance their primary field of study or discover how to incorporate imaging science into their major discipline to solve complex, interdisciplinary problems in imaging, imagery exploitation, and the design and evaluation of imaging systems.
Notes about this minor:
This minor is closed to students majoring in imaging science.
Posting of the minor on the student's academic transcript requires a minimum GPA of 2.0 in the minor.
This course provides an introductory overview of the basic engineering and scientific principles associated with imaging systems. Topics covered include imaging physics, photographic science, human vision and perception, image capture and display technologies (both analog and digital), and digital image processing. This course is taught using both mathematical and phenomenological presentation and prepares students to proceed with more in-depth investigation of these fields in subsequent imaging science and motion picture science courses. Accompanying laboratory exercises provide hands-on experience with the presented concepts.
Vision & Psychophysics
This course presents an overview of the organization and function of the human visual system and some of the psychophysical techniques used to study visual perception.
This course introduces the concepts of quantitative measurement of electromagnetic energy. The basic radiometric and photometric terms are introduced using calculus-based definitions. Governing equations for source propagation and sensor output are derived. Simple source concepts are reviewed and detector figures of merit are introduced and used in problem solving. The radiometric concepts are then applied to simple imaging systems so that a student could make quantitative measurements with imaging instruments.
Linear and Fourier Methods for Imaging
This course develops the concepts of complex numbers and linear algebra for describing imaging systems in the frequency domain via the discrete and continuous Fourier transforms.
This course introduces the analysis and design of optical imaging systems based on the ray model of light. Topics include reflection, refraction, imaging with lenses, stops and pupils, prisms, magnification and optical system design using computer software.
Light waves having both amplitude and phase will be described to provide a foundation for understanding key optical phenomena such as interference, diffraction, and propagation. Starting from Maxwell's equations the course advances to the topic of Fourier optics.
Interactions Between Light and Matter
This course introduces the principles of how light interacts with matter. The principles of atomic physics as applied to simple atoms are reviewed and extended to multi-electron atoms to interpret their spectra. Molecular structure and spectra are covered in depth, including the principles of lasers. The concepts of statistical physics concepts are introduced and applied to the structure of crystalline solids, their band structure and optical properties. These concepts are then used to understand electronic imaging devices, such as detectors.
Fundamentals of Color Science
This course will introduce students to the field of Color Science. Students will learn about the physical sources of color, the visual mechanisms that provide our experience of color, and the descriptive systems that have been developed for relating the physical and visual properties. Through hands-on projects, students will learn practical methods for measuring, modeling, and controlling color in digital imaging systems.
Image Processing and Computer Vision I
This course is an introduction to the basic concepts of digital image processing. The student will be exposed to image capture and image formation methodologies, sampling and quantization concepts, statistical descriptors and enhancement techniques based upon the image histogram, point processing, neighborhood processing, and global processing techniques based upon kernel operations and discrete convolution as well as the frequency domain equivalents, treatment of noise, geometrical operations for scale and rotation, and grey-level resampling techniques. Emphasis is placed on applications and efficient algorithmic implementation using the student's programming language of choice.
Image Processing & Computer Vision II
This course is considers the more advanced concepts of digital image processing. The topics include image reconstruction, noise sources and techniques for noise removal, information theory, image compression, video compression, wavelet transformations, frequency-domain based applications, morphological operations, and modern digital image watermarking and steganography algorithms. Emphasis is placed on applications and efficient algorithmic implementation using the student’s computer programming language of choice, technical presentation, and technical writing.
This course provides an overview of the underlying physical concepts, designs, and characteristics of detectors used to sense electromagnetic radiation having wavelengths ranging from as short as X-rays to as long as millimeter radiation. The basic physical concepts common to many standard detector arrays will be reviewed. Some specific examples of detectors to be discussed include photomultipliers, micro channel plates, hybridized infrared arrays, positive-intrinsic-negative (PIN) detectors, and superconductor-insulator-superconductor (SIS) mixers. The use of detectors in fields such as astronomy, high energy physics, medical imaging and digital imaging will be discussed.
Multivariate Statistical Image Processing
This course discusses the digital image processing concepts and algorithms used for the analysis of hyperspectral, multispectral, and multi-channel data in multiple imaging application areas. Concepts are covered at the theoretical and implementation level using current, popular commercial software packages and high-level programming languages to work examples, homework problems and programming assignments. The requisite multivariate statistics will be presented as part of this course as an extension of the univariate statistics that the students have previously been exposed to in the introductory statistics classes. Topics include methods for supervised data classification, clustering algorithms and unsupervised classification, multispectral data transformations, data-redundancy reduction techniques, derivation of non-spectral images features to aid in the classification process, and data fusion for resolution enhancement.
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.
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 complementary metal-oxide-semiconductor (CMOS) and infrared arrays.
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 (charge coupled device), CMOS, (complementary metal oxide semiconductor), and infrared arrays. Focal plane array users in industry, government and academia 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.
Linear Systems and Differential Equations
This is an introductory course in linear algebra and ordinary differential equations in which a scientific computing package is used to clarify mathematical concepts, visualize problems, and work with large systems. The course covers matrix algebra, the basic notions and techniques of ordinary differential equations with constant coefficients, and the physical situation in which they arise.
This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course.
Probability and Statistics I
This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications.
Modern Physics I
This course provides an introductory survey of elementary quantum physics, as well as basic relativistic dynamics. Topics include the photon, wave-particle duality, deBroglie waves, the Bohr model of the atom, the Schrodinger equation and wave mechanics, quantum description of the hydrogen atom, electron spin, and multi-electron atoms.
Vibrations and Waves
This course is an introduction to the physics of vibrations and waves, beginning with the simple harmonic oscillator, the foundation to understanding oscillatory and vibratory systems. The course will include driven and damped single oscillators, coupled discrete oscillators, and continuous vibrating systems. Connections will be made with many areas of physics that involve oscillation, including mechanics, electromagnetism, and quantum mechanics.
Mathematical Methods in Physics
This course serves as an introduction to the mathematical tools needed to solve intermediate and upper-level physics problems. Topics include matrix algebra, vector calculus, Fourier analysis, partial differential equations in rectangular coordinates, and an introduction to series solutions of ordinary differential equations.
In this course light waves having both amplitude and phase will be described to provide a foundation for understanding key optical phenomena such as interference, diffraction, and propagation. Starting from Maxwell's equations the course advances to the topic of Fourier optics.
* At least one course must be completed at the 300-level or above.
† At least three courses (9 credits) must be taken in Imaging Science (IMGS, including SOFA-103)