Eli Saber Headshot

Eli Saber

Professor

Department of Electrical and Microelectronic Engineering
Kate Gleason College of Engineering

585-475-6927
Office Location

Eli Saber

Professor

Department of Electrical and Microelectronic Engineering
Kate Gleason College of Engineering

Education

BS, State University of New York at Buffalo; MS, Ph.D., University of Rochester

Bio

Dr. Eli Saber is a Professor in the Electrical and Microelectronic Engineering Department at the Rochester Institute of Technology. Prior to that, he worked for Xerox Corporation from 1988 until 2004 in a variety of positions ending as Product Development Scientist and Manager at the Business Group Operations Unit. He received the BS degree in Electrical and Computer Engineering from the University of Buffalo in 1988, and the MS and Ph.D. degrees in the same discipline from the University of Rochester in 1992 and 1996 respectively. From 1997 until 2004, he was an adjunct faculty member at the Electrical Engineering Department of the Rochester Institute of Technology and at the Electrical & Computer Engineering Department of the University of Rochester.

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585-475-6927

Areas of Expertise

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Published Conference Proceedings
Liu, Yansong, et al. "Dense Semantic Labeling of Very High Resolution Aerial Imagery and LIDAR with Fully Convolutional Neural Networks and Higher Order CRFs." Proceedings of the CVPR Workshops 2017. Ed. CVPR. Honolulu, Hawaii: CVPR, 2017. Web.
Liu, Yansong, et al. "Semantic Segmentation of Remote Sensing Data Using Gaussian Processes and Higher Order CFRs." Proceedings of the IGARSS 2017. Ed. IEEE. Fort Worth, TX: IEEE, 2017. Web.
Liang, Yilong, Sildomar Monteiro, and Eli Saber. "Transfer Learning for High Resolution Aerial Image Classification." Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop. Washington DC, Washington DC: n.p., 2016. Web.
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Journal Paper
Vantaram, S. R., et al. "Synthesis of Intensity Gradient and Texture Information for Efficient Three-Dimensional Segmentation of Medical Volumes." Journal of Medical Imaging 2. 2 (2015) Print.
Vantaram, S. R., et al. "Automatic Spatial Segmentation of Multi/Hyperspectral Imagery by Fusion of Spectral-Gradient-Textural Attributes." Journal of Applied and Remote Sensing 9. 1 (2015): 1-37. Print.
Saber, Eli, 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.
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Invited Keynote/Presentation
Saber, E. "Segmentation of Multimodal Imagery for Remote Sensing, Multimedia and Biomedical Applications." Latin America Remote Sensing Conference. LARS. Santiago, Chile. 15 Oct. 2013. Keynote Speech.
Published Article
Fan, X., H.E. Rhody, E. Saber. “A spatial feature-enhanced MMI algorithm for multimodal airborne image registration.” IEEE Transactions on Geoscience and Remote Sensing, 48.6 (2010): 2580-2589. Print. "  £
Gurram, P., E. Saber, H.E. Rhody. “A Segment-Based Mesh Design for Building Parallel-Perspective Stereo Mosaics.” IEEE Transactions on Geoscience and Remote Sensing, 48.3 (2010): 1256-1269. Print."  £
Jaber, Mustafa and EliSaber. “Probabilistic Approach for ExtractingRegions of Interest in Digital Images.” Journal of Electronic Imaging, 19.2, (2010): 023019-1-13. Print. " É  *
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Currently Teaching

EEEE-499
0 Credits
One semester of paid work experience in electrical engineering.
EEEE-707
3 Credits
The course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. The course begins with a pertinent review of matrices, transformations, partitions, determinants and various techniques to solve linear equations. It then transitions to linear vector spaces, basis definitions, normed and inner vector spaces, orthogonality, eigenvalues/eigenvectors, diagonalization, state space solutions and optimization. Applications of linear algebra to engineering problems are examined throughout the course. Topics include: Matrix algebra and elementary matrix operations, special matrices, determinants, matrix inversion, null and column spaces, linear vector spaces and subspaces, span, basis/change of basis, normed and inner vector spaces, projections, Gram-Schmidt/QR factorizations, eigenvalues and eigenvectors, matrix diagonalization, Jordan canonical forms, singular value decomposition, functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course.
EEEE-709
3 Credits
The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration, and numerical techniques. Electrical engineering applications will be discussed throughout the course.
EEEE-789
3 Credits
Topics and subject areas that are not regularly offered are provided under this course. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor.
ENGR-709
3 Credits
Advanced Engineering Mathematics provides the foundations for complex functions, vector calculus and advanced linear algebra and its applications in analyzing and solving a variety of electrical engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: complex functions, complex integration, special matrices, vector spaces and subspaces, the nullspace, projection and subspaces, matrix factorization, eigenvalues and eigenvectors, matrix diagonalization, singular value decomposition (SVD), functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least squares, and numerical techniques. Electrical engineering applications will be discussed throughout the course.
IMGS-699
0 Credits
This course is a cooperative education experience for graduate imaging science students.
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