Carl Salvaggio Headshot

Carl Salvaggio

Professor

Chester F. Carlson Center for Imaging Science
College of Science
Director of the Digital Imaging and Remote Sensing Laboratory

585-475-6380
Office Location
Office Mailing Address
Chester F. Carlson Center for Imaging Science Building 76 Room 3136

Carl Salvaggio

Professor

Chester F. Carlson Center for Imaging Science
College of Science
Director of the Digital Imaging and Remote Sensing Laboratory

Bio

Dr. Salvaggio is a Full Professor and Director of the Digital Imaging and Remote Sensing Laboratory teaching and conducting research in, as the name might imply, image processing, computer vision, remote sensing, and programming. His research interests address the development of solutions to applied, real-world, problems utilizing the appropriate imaging modalities and algorithmic approaches. Dr. Salvaggio's expertise are in thermal infrared phenomenology, exploitation, and simulation; design and implementation of novel imaging and ground-based measurement systems; three-dimensional geometry extraction from multi-view imagery; material optical properties measurement and modeling; radiometric and geometric calibration of imaging systems; and still and motion image processing for various applications.

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585-475-6380

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Areas of Expertise

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Journal Paper
Lu, Yawen, et al. "3D Plant Root reconstruction Based on Fusion of Deep Structure-from-motion and IMU." Journal of Multimedia Tools and Applications. (2020): 101-105. Web.
Mamaghani, Baabak and Carl Salvaggio. "Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing." Sensors 19. 20 (2019): 4453-4482. Web.
Kaputa, Daniel, et al. "MX-1: A New Multi-Modal Remote Sensing UAS Payload with High Accuracy GPS and IMU." IEEE Xplore 2019 IEEE Systems and Technologies for Remote Sensing Applications Through Unmanned Aerial Systems (STRATUS). (2019): 1-4. Web.
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Published Conference Proceedings
Khan, Salman and Carl Salvaggio. "Automatically Gather Address Specific Dwelling Images Using Google Street View." Proceedings of the 25th International Conference on Pattern Recognition, ICPR2020. Ed. Rita Cucciarra. Milan, Italy: IEEE, 2021. Web.
Helvey, Matthew, et al. "Duck Nest Detection Through Remote Sensing." Proceedings of the IGARSS 2020, UAV and Airborne Platforms Applications I, Mission, Sensors and Calibration. Ed. Bill Emery. Waikoloa, Hawaii: IEEE, 2020. Web.
Soni, Ayush, et al. "High-quality Multispectral Image Generation Using Conditional GANs." Proceedings of the International Symposium on Electronic Imaging 2020, Imaging and Multimedia Analytics in a Web and Mobile World. Ed. Radka Tezaur and Jonathan B. Phillips. Burlingame, California: IS&T, 2020. Web.
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Published Article
Bartlett, B.D., M.G. Gartley, D.W. Messinger, C. Salvaggio, J.R. Schott. “Spectro-polarimetricbidirectional reflectance distribution function determination of in-scenematerials and its use in target detection applications”, Journal of Applied Remote Sensing, 4.043552 (2010): 1-21. Print. £
Nilosek, D.R.,C. Salvaggio. “Applying computer vision techniques to perform semi-automated analytical photogrammetry.” Image Processing Workshop, April 2010. n.p. Print. " 
Garrett, A.J., C. Salvaggio, M.V. Casterline. “Thermodynamics of partially frozen coolinglakes.” Proceedings of SPIE, SPIE Defenseand Security, Thermosense XXXII, Utilities and Fluid Dynamics, 7661 (2 April 2010): n.p. Print. " 
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Formal Presentation
Salvaggio, C., R.V. Raqueno, A.R. Scott, P.Y. Youkhana.“Accurate radiometric temperature measurements using thermal infrared imagery of small targets, physics-based modeling, and companion high-resolution optical image data sets.” University and Industry Technical Interchange Review Meeting, UITI 2010, Plenary Session. Knoxville, TN. December 2010. Presentation. " 

Currently Teaching

IMGS-180
3 Credits
This project-based course is an introduction to object-oriented computer programming directed at solving scientific problems related to imaging. The student will learn the concepts of object-oriented programming using the most recent C++ programming language standard. Popular project management and modern compilation/build systems will be presented and utilized. Fundamentals of streamed input and output, data types, objects and classes, templates, lambda expressions, flow control, repetition, program decomposition and library development, software engineering/design concepts, and problem-solving approaches such as the use of randomness, divide-and-conquer, Monte Carlo simulation, ill-posed solutions, and search will be examined in detail and applied to scientific, mathematical, and imaging-specific problems. In addition to the base language concepts, students will utilize popular open-source and public-domain libraries such as Boost, Eigen, and OpenCV.
IMGS-361
3 Credits
This course provides an introduction to the concepts and methods of image processing. The student will be exposed to sampling and quantization methods; descriptors and enhancement techniques based upon the image histogram; geometrical manipulations; interpolation and resampling; feature generation with direct application to image registration/stitching and redundancy reduction; pixel and object-level classification; frequency-domain applications, including automated image registration, data embedding, and image reconstruction; and image data redundancy and compression concepts. Emphasis is placed on efficient algorithmic implementations and applications, in an object-oriented development environment.
IMGS-502
3 Credits
Part of this course is designed to develop skills in technical communication and scientific research practices. Each student is required to research, write, and present a proposal for an independent research project. Students initiate the research project defined in the proposal developed in the course. The project is supervised by a faculty member in imaging science and is expected to require 9-12 hours per week.
IMGS-589
1 - 3 Credits
IMGS-599
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 any of their years of study.
IMGS-699
0 Credits
This course is a cooperative education experience for graduate imaging science students.
IMGS-740
3 Credits
The analysis and solution of imaging science systems problems for students enrolled in the MS Project capstone paper option.
IMGS-790
1 - 6 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-791
0 Credits
Continuation of Thesis
IMGS-890
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-891
0 Credits
Continuation of Thesis

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