This course introduces non-science majors to the primary imaging technologies currently used in the field of cultural heritage, in support of the preservation and conservation of artifacts in museums, archives, libraries, and other institutions. Understanding historical manuscripts and artifacts of historical and cultural heritage significance is an important part of studying both past and present cultures. The use of modern imaging technologies to discover, understand, and preserve these artifacts is becoming an important and rapidly growing field of exploration, and combines aspects of history, languages, material science, and specialty imaging. This course will introduce students to the basic concepts behind the primary imaging technologies used in the field of cultural heritage research, with a focus on passive spectral imaging. The course also provides a more detailed description of various imaging modalities, e.g., spectral, x-ray, 3D, etc., and basic image processing concepts used to extract information from spectral imagery. Real data from cultural heritage image collections will be used as examples for in-class demonstrations, whenever possible. Students also will work on projects related to image collection and processing, as applied to data from works of cultural value.

The purpose of this course is to develop an understanding and ability to model signal and noise within the context of imaging systems. A review of the modulation transfer function is followed by a brief review of probability theory. The concept of image noise is then introduced. Next, random processes are considered in both the spatial and frequency domains, with emphasis on the autocorrelation function and power density spectrum. Finally, the principles of random processes are applied to signal and noise transfer in multistage imaging systems. At the completion of the course the student will be able to model signal and noise transfer within a multistage imaging system.

This course develops models of noise and random processes within the context of imaging systems. The focus will be on stationary random processes in the spatial and spatial frequency domain. The concept of image noise is introduced in both the analog and digital domain. Random processes are studied in both the spatial and spatial frequency domain stressing the autocorrelation function and the power density spectrum. The application of random processes to the understanding of signal noise in imaging systems in both the continuous and the digital domains is presented. Tools for modeling signal and noise transfer are emphasized. At the completion of the course the student should have the ability to model signal and noise transfer within a multistage imaging system.

This course is a cooperative education experience for graduate imaging science students.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

Continuation of Thesis

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.

Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

Continuation of Thesis

Vertically Integrated Projects (VIP) engage undergraduate students in long-term, large-scale, multidisciplinary project teams that are led by faculty. VIP courses are project-based, team-based courses directly supporting faculty research and scholarship. VIPs under this course number have a particular focus on interdisciplinary humanities and social sciences expertise, with membership in teams across RIT colleges.

Vertically Integrated Projects (VIP) engage undergraduate students in long-term, large-scale, multidisciplinary project teams that are led by faculty. VIP courses are project-based, team-based courses directly supporting faculty research and scholarship. VIPs under this course number have a particular focus on interdisciplinary humanities and social sciences expertise, with membership in teams across RIT colleges.