David Messinger
Professor
Chester F. Carlson Center for Imaging Science
College of Science
Xerox Chair
585-475-4538
Office Location
David Messinger
Professor
Chester F. Carlson Center for Imaging Science
College of Science
Xerox Chair
Education
BS, Clarkson University; Ph.D., Rensselaer Polytechnic Institute
Bio
Dr. Messinger received a Bachelors degree in Physics from Clarkson University and a Ph.D. in Physics from Rensselaer Polytechnic Institute. He has worked as an Analyst for XonTech Inc., on the National Missile Defense Program for Northrop Grumman, and was an Intelligence Community Postdoctoral Research Fellow. He is currently a Professor and the Xerox Chair in Imaging Science at the Rochester Institute of Technology. From 2014 - 2022 he was Director of the Chester F. Carlson Center for Imaging Science, an academic unit in the College of Science offering BS, MS, and Ph.D. degrees. From 2007-2014 he was the Director of the Digital Imaging and Remote Sensing Laboratory in the Center. He has been Principal Investigator on approximately $8M in externally sponsored research funding, has published over 180 scholarly articles, and has served as primary advisor for over 35 MS and Ph.D. students. He is a Fellow of SPIE. His personal research focuses on projects related to spectral image analysis using physics-based approaches and advanced mathematical techniques. Applications of this research have ranged from airborne and space-based imaging for national security, archeology, and disaster response, to cultural heritage imaging of historical artifacts such as manuscripts and maps.
585-475-4538
Areas of Expertise
Digital Imaging
Remote Sensing
Cultural Heritage Imaging
Spectral Imaging
Image Processing
Image System Analysis
Multispectral Imaging
Hyperspectral Imaging
Spectroscopy
Imaging Science
Select Scholarship
Journal Paper
Maali-Amiri, Morteza, David Messinger, and Todd Hanneken. "Colorimetric characterization of multispectral imaging systems for visualization of historical artifacts." Journal of Cultural Heritage 68. (2024): 136-148. Web.
Bergstrom, Austin and David Messinger. "Image Quality and Object Detection Performance." Journal of Electronic Imaging 33. 5 (2024): 1-22. Web.
Maali-Amiri, Morteza and David Messinger. "Virtual Cleaning of Works of Art Using A Deep Generative Network: Spectral Reflectance Estimation." Heritage Science 11. 16 (2023): N/A. Print.
Bergstrom, Austin and David Messinger. "Image quality and computer vision performance: assessing the effects of image distortions and modeling performance relationships using the the General Image Quality Equation." Journal of Electronic Imaging 32. 2 (2023): N/A. Print.
Ducay, Rey and David Messinger. "Image Fusion of Hyperspectral and Multispectral Imagery Using NNDiffuse." Journal of Applied Remote Sensing 17. 2 (2023): N/A. Web.
Zawacki, Alex, et al. "Fragments under the Lens: A Case Study of Multispectral versus Hyperspectral Imaging for Manuscript Recovery." Digital Philology: A Journal of Medieval Cultures 12. 1 (2023): 123-143. Print.
Huang, Sihan and David Messinger. "An Unsupervised Laplacian Pyramid Network for Radiometrically-Accurate Data Fusion of Hyperspectral and Multispectral Imagery." Transactions on Geoscience and Remote Sensing 14. 8 (2022): 1. Web.
Huang, Sihan and David Messinger. "An Unsupervised Cascade Fusion Network for Radiometrically- Accurate Vis-NIR-SWIR Hyperspectral Sharpening." Remote Sensing 14. (2022): 4390. Web.
Zawacki, Alexander, et al. "Fragments under the Lens: A Case Study of Multispectral versus Hyperspectral Imaging for Manuscript Recovery." Digital Philology 12. 1 (2023): 123-143. Web.
Bai, Di, David Messinger, and David Howell. "Deep Learning Spatial-Spectral Processing of Hyperspectral Images for Pigment Mapping of Cultural Heritage Artifacts." Workshop on Pattern Recognition for Cultural Heritage (PatReCH 2020). (2021): 200-214. Print.
Peery, Tyler, et al. "Image Quality in Cultural Heritage." Manuscript Cultures 15. (2021): 91 - 104. Print.
Kleynhans, Tania, et al. "Low-Cost Multispectral System Design for Pigment Analysis in Works of Art." Sensors 21. (2021): 1-15. Web.
Maali-Amiri, Morteza and David Messinger. "Virtual Cleaning of Works of Art Using Deep Convolutional Neural Networks." Heritage Science 9. 94 (2021): 1-19. Web.
Starynska, Anna, David Messinger, and Yu Kong. "Revealing a history: palimpsest text separation with generative networks." International Journal on Document Analysis and Recognition (IJDAR) 24. (2021): 181-195. Web.
Kleynhans, Tania, David Messinger, and John Delaney. "Towards Automatic Classification of Diffuse Reflectance Image Cubes from Paintings Collected with Hyperspectral Cameras." MicroChemical Journal 157. (2020): 1-9. Web.
Peery, Tyler and David Messinger. "Spatial Resolution as a Trade Space for Low-Light Imaging of Sensitive Cultural Heritage Documents." Journal of Cultural Heritage Research. (2020): 1-10. Print.
Kleynhans, Tania, et al. "An Alternative Approach to Mapping Pigments in Paintings with Hyperspectral Reflectance Image Cubes Using Artificial Intelligence." Heritage Science 8. 84 (2020): 1-16. Print.
Bai, Di, David Messinger, and David Howell. "A Hyperspectral Image Spectral Unmixing and Classification Approach to Pigment Mapping in Historical Artifacts." Journal of the American Institute of Conservation 58. 1-2 (2019): 68-89. Print.
Kucer, Michal, Alex Loui, and David Messinger. "Leveraging Expert Feature Knowledge for Predicting Image Aesthetics." IEEE Transactions on Image Processing 27. 10 (2018): 5100 - 5112. Print.
Dorado-Munoz, Leidy, David Messinger, and Damien Bove. "Integrating Spatial and Spectral Information for Enhancing Spatial Features in the Gough Map of Great Britain." Journal of Cultural Heritage Research 34. (2018): 159-165. Print.
Fan, Lei and David Messinger. "Joint Spatial-Spectral Hyperspectral Image Clustering Using Block-Diagonal Amplified Affinity Matrix." Optical Engineering 57. 3 (2018): UNK. Web.
Yang, Jie, David Messinger, and Roger Dube. "Bloodstain Detection and Discrimination Impacted by Spectral Shift Using an Interference Filter based VNIR Multispectral Crime Scene Imaging System." Optical Engineering 57. 3 (2018): UNK. Web.
Messinger, David W., et al. "Spatial Segmentation of Multi/hyperspectral Imagery by Fusion of Spectral-gradient-textural Attributes." SPIE Journal of Applied Remote Sensing 9. 1 (2015): 1--37. Print.
Messinger, David W., et al. "Nearest Neighbor Diffusion Based Pan-sharpening Algorithm for Spectral Images." Optical Engineering 53. 1 (2014) Print.
Sun, Weihua and David W. Messinger. "Knowledge-Based Automated Road Network Extraction System Using Multispectral Images." Optical Engineering 52. 4 (2013) Print.
Albano, James A., David W. Messinger, and Stanley R. Rotman. "Commute Time Distance Transformation Applied to Spectral Imagery and its Utilization in Material Clustering." Optical Engineering 51. 7 (2012) Print.
Schlamm, Ariel, David W. Messinger, and Bill Basener. "Interest Segmentation of Large Area Spectral Imagery for Analyst Assistance." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5. 2 (2012) Print.
Messinger, David W., et al. "Metrics of Spectral Image Complexity with Application to Large Area Search." Optical Engineering 51. 3 (2012) Print.
Aardt, Jan A. van, et al. "Geospatial Disaster Response During the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination." Photogrammetric Engineering and Remote Sensing 77. 9 (2011): 943-952. Print.
Published Conference Proceedings
Ducay, Rey and David Messinger. "Radiometric Assessment of Multispectral Pansharpening Methods as Applied to Hyperspectral Imagery." Proceedings of the Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI. Ed. Miguel Velez-Reyes and David Messinger. Bellingham, WA: SPIE, 2020. Web.
Kleynhans, Tania, David Messinger, and John Delaney. "Automatic Material Classification of Paintings in Illuminated Manuscripts from VNIR Reflectance Hyperspectral Data Cubes." Proceedings of the Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI. Ed. Miguel Velez-Reyes and David Messinger. Bellingham, WA: SPIE, 2020. Web.
Huang, Sihan and David Messinger. "Hyperspectral Analysis of Cultural Heritage Artifacts: Using Modified Adaptive Coherence Estimator to Separate Spectra with Subtle Spectral Differences." Proceedings of the Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI. Ed. Miguel Velez-Reyes and David Messinger. Bellingham, WA: SPIE, 2020. Web.
Taufique, Abu Md and David Messinger. "Hyperspectral Pigment Analysis of Cultural Heritage Artifacts Using the Opaque Form of Kubelka-Munk Theory." Proceedings of the Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV. Ed. Miguel Velez-Reyes and David Messinger. Baltimore, MD: SPIE, 2019. Print.
Peery, Tyler and David Messinger. "Panchromatic Sharpening Enabling Low-intensity Imaging of Cultural Heritage Documents." Proceedings of the Image Sensing Technologies: Materials, Devices, Systems, and Applications VI. Ed. Nibir K. Dhar, Achyut K. Dutta, and Sachidananda R. Babu. Baltimore, MD: SPIE, 2019. Print.
Kucer, Michal and David Messinger. "Aesthetic Inference for Smart Mobile Devices." Proceedings of the IEEE Western Conference on Applications in Computer Vision (WACV) 2018. Ed. UNK. Lake Tahoe, NV: IEEE, 2018. Web.
Peery, Tyler and David Messinger. "Processes for Conducting HSI Pan-Sharpening with 3D Digital Flattening." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV. Ed. UNK. Orlando, FL: SPIE, Web.
Peery, Tyler and David Messinger. "MSI vs. HSI in Cultural Heritage Imaging." Proceedings of the Imaging Spectrometry XXII: Applications, Sensors, and Processing. Ed. UNK. San Diego, CA: SPIE, Web.
Bai, Di. "Pigment Diversity Estimation for Hyperspectral Images of the Selden Map of China." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV. Ed. UNK. Orlando, FL: SPIE, 2018. Web.
Messinger, David W. "An Adaptive Locally Linear Embedding Manifold Learning Approach for Hyperspectral Target Detection." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI. Baltimore, Maryland: SPIE, 2015. Print.
Messinger, David W. "Streaming Analysis of Track Data from Video." Proceedings of the Geospatial Informatics, Fusion, and Motion Video Analytics V. Baltimore, Maryland: SPIE, 2015. Print.
Messinger, David W. "Schrodinger Eigenmaps for Spectral Target Detection." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI. Baltimore, Maryland: SPIE, 2015. Print.
Messinger, David W. "Effects of Cubesat Design Parameters on Image Quality and Feature Extraction for 3D Reconstruction." Proceedings of the International Geoscience and Remote Sensing Symposium. Quebec, Quebec: IEEE, 2014. Print.
Ziemann, Amanda, David W. Messinger, and James A. Albano. "Target Detection Performed on Manifold Approximations Recovered from Hyperspectral Data." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Fan, Lei, Brittany Ambeau, and David W. Messinger. "A Semi-supervised Classification Algorithm Using The TAD-derived Background as Training Data." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Dorado-Munoz, Leidy, David W. Messinger, and Amanda Ziemann. "Target Detection Using The Background Model from the Topological Anomaly Detection Algorithm." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Busuioceanu, Maria, et al. "Evaluation of the CASSI-DD Hyperspectral Compressive Sensing Imaging System." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Albano, James A., David W. Messinger, and Emmett J. Ientilucci. "Spectral Target Detection Using a Physical Model and A Manifold Learning Technique." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Hagstrom, Shea and David W. Messinger. "Fusing LIDAR-based Voxel Geometry with Multi-angle Visible Imagery." Proceedings of the Laser Radar Technology and Applications XVIII. Baltimore, Maryland: n.p., 2013. Print.
Sun, Weihua and David W. Messinger. "Pan-sharpening of Spectral Image with Anisotropic Diffusion for Fine Feature Extraction Using GPU." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Giannandrea, AnneMarie, et al. "The SHARE 2012 Data Campaign." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Canham, Kelly, et al. "SHARE 2012: Large Edge Targets for Hyperspectral Imaging Applications." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Albano, James A., Amanda Ziemann, and David W. Messinger. "Assessing the Impact of the Edge-Weighting Function in a Graph-Based Approach to Anomaly Detection." Proceedings of the IEEE WHISPERS 2013. Gainesville, Florida: n.p., 2013. Print.
Busuioceanu, Maria, et al. "Analysis and Utility of Atmospheric Compensation of Simulated Compressive Sensing (CS) Measurements." Proceedings of the IEEE WHISPERS 2013. n.p., 2013. Print.
Ashok, Luca and David W. Messinger. "A Spectral Image Clustering Algorithm Based on Ant Colony Optimization." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Ziemann, Amanda, et al. "Assessing the Impact of Background Spectral Graph Construction Techniques on the Topological Anomaly Detection Algorithm." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Albano, James A. and David W. Messinger. "Euclidean Commute Time Distance Embedding and its Application to Spectral Anomaly Detection." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Sun, Weihua and David W. Messinger. "An Automated Approach for Constructing Road Network Graph from Multispectral Images." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Sharon, Gil, et al. "Detection of Anomalous Activity in Hyperspectral Imaging: Metrics for Evaluating Algorithms." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Canham, Kelly, et al. "Spectral Library Generation for Hyperspectral Archaeological Validation." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Hagstrom, Shea and David W. Messinger. "Line-of-Sight Measurement in Large Urban Areas using Voxelized Lidar." Proceedings of the Laser Radar Technology and Applications XVII. Baltimore, Maryland: n.p., 2012. Print.
Sun, Jiangquin and David W. Messinger. "Parking Lot Process Model Incorporated into DIRSIG Scene Simulation." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Syed, Abdul H., Eli Saber, and David W. Messinger. "Encoding of Topological Information in Multi-Scale Remotely Sensed Data: Applications to Segmentation and Object-Based Image Analysis." Proceedings of the IEEE Conference on Geographic Object Based Image Analysis (GEOBIA). Rio de Janeiro, Brazil: n.p., 2012. Print.
Herweg, Jared, et al. "SpecTIR Hyperspectral Airborne Rochester Experiment Data Collection Campaign." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Sah, Shagan, et al. "A Multi-Temporal Analysis Approach for Land Cover Mapping in Support of Nuclear Incident Response." Proceedings of the SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, MD: n.p., 2012. Print.
Basener, William, et al. "A Detection Identification Process with Geometric Target Detection and Subpixel Spectral Visualization." Proceedings of the WHISPERS 2011. Lisbon, Portugal: n.p., 2011. Print.
Bartlett, Brent D., et al. "Anomaly Detection of Man-made Objects Using Spectro-polarimetric Imagery." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. Orlando, FL: n.p., 2011. Print.
Full Patent
Kucer, Michal, et al. "An Iterative Method for Salient Foreground Detection and Multi-Object Segmentation." U.S. Patent 10,706,549. 7 Jul. 2020.
Published Article
Bartlett, B.D., M.G. Gartley, D.W. Messinger, C. Salvaggio, J.R. Schott. “Spectro-polarimetric bidirectionalreflectance distribution function determination of in-scene materials and its use in target detection applications.” Journal of Applied Remote Sensing, 4.043552 (2010): 1-21. Print. £
Canham, K., A. Schlamm, A. Ziemann, B. Basener, D.W. Messinger, “Spatially adaptive hyperspectralendmember selection and spectral unmixing.” IEEE Transactions on Geoscience and Remote Sensing, 2010. n.p. Print. " £
Messinger, D.W., A. Ziemann, B. Basener, A. Schlamm. “A complexity metric for spectral imagerybased on spatially local convex hull volumeestimation.” IEEE Transactions onGeoscience and Remote Sensing,November 2010. n.p. Print. " £
Schlamm, A., D.W. Messinger, A. Ziemann, B. Basener.“Change detection in multi- andhyperspectral image tiles based on quantitative measures of point density.”Journal of Applied Remote Sensing.(2010): n.p. Print. " £
Schlamm, A., D.W.Messinger. “A Euclidean Distance Transformation for Improved Anomaly Detection in Spectral Imagery.” Proceedingsof the 2010 Western NY Image ProcessingWorkshop, IEEE, Nov. 2010. n.p. Print. " £
Mercovich, R., A. Harkin, D.W. Messinger. “Utilizing the graph modularity to blind cluster multispectral satellite imagery.” Proceedings of the 2010 Western NY Image Processing Workshop, IEEE, Nov. 2010. n.p. Print. " £
Messinger, D.W., A. Ziemann, B. Basener, A. Schlamm. “Spectral Image Complexity Estimated Through Local Convex Hull Volume.” WHISPERS 2010, (2010): n.p. Print. " £
Schlamm, A., D.W.Messinger, B. Basener. “Interest segmentation of hyperspectral imagery.” WHISPERS 2010, (2010): n.p. Print. " £
Messinger, D.W., J.A. van Aardt, D. McKeown, M.V. Casterline,J.W. Faulring, N.G. Raqueno, B. Basener, M. Velez-Reyes. “High Resolution and LIDAR Imaging Support to the Haiti Earthquake Relief Effort.” Imaging Spectrometry XV, Optics & Photonics, 7812 (August 2010): n.p. Print. "
Weiner, A., D.W. Messinger. “An End-to-End Airborne FTS Simulation for Evaluating the Performance Trade Space in Fugitive Gas Identification.” Imaging Spectrometry XV,Optics & Photonics, 7812 (August 2010): n.p. Print. "
Schlamm, A., R. Resmini, D.W. Messinger, B. Basener. “A comparison study of dimension estimationalgorithms.” SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 7695 (2010): n.p. Print. "
Schlamm, A., D.W.Messinger, B. Basener. “A novel method for change detection in spectral imagery.” SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 7695 (2010): n.p. Print. "
Ziemann, A., D.W.Messinger, B. Basener, A. Schlamm.“Iterative convex hull volume estimation inhyperspectral imagery for change detection.” SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, andUltraspectral Imagery XVI, 7695 (2010): n.p. Print. "
Sarrazin, D., J.A. van Aardt, D.W. Messinger, G.P. Asner. “Fusing waveform LIDAR and hyperspectral data for species-level structural assessment in savanna ecosystems.” SPIE, Laser Radar Technology and Applications XV, 7684 (2010): n.p. Print. "
Hagstrom, S., D.W.Messinger, S.D. Brown. “Feature extraction using voxel aggregation of focuseddiscrete LIDAR data.” SPIE, Laser Radar Technology and Applications XV, 7684 (2010): n.p. Print. "
Currently Teaching
IMGS-115
Cultural Heritage Imaging
3 Credits
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.
IMGS-442
Imaging Systems Analysis and Modeling
4 Credits
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.
IMGS-613
Noise and System Modeling
2 Credits
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.
IMGS-699
Imaging Science Graduate Co-op
0 Credits
This course is a cooperative education experience for graduate imaging science students.
IMGS-790
Research & Thesis
1 - 6 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-791
Continuation of Thesis
0 Credits
Continuation of Thesis
IMGS-799
Imaging Science Independent Study
1 - 4 Credits
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.
IMGS-890
Research & Thesis
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-891
Continuation of Thesis
0 Credits
Continuation of Thesis
ITDL-210
Vertically Integrated Projects
1 - 3 Credits
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.
ITDL-510
Vertically Integrated Projects
1 - 3 Credits
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.