Prerequisite: ECGR 2112 with a C or better. Analysis of continuous-time signal and systems. Input-output relationships of linear time-invariant systems. Transient and steady state analysis. Frequency domain descriptions and Fourier analysis. Analysis and characterization of LTI systems using Laplace transforms.

Prerequisite: Permission from department. Interactive graphics; raster, character, vector, graphics, display technologies; rotation, scaling, translating of graphics image; image processing / enhancement; feature extraction; 3-D graphics; hidden lines. Credit will not be given for ECGR5103 where credit has been given for ECGR4103.

The theory of machine intelligence. Computational methods for modeling machine intelligence including machine vision and automatic decision making from sensor measurements. Applications of this theory to autonomous robotic decision making such as navigation and industrial quality control.

Prerequisites:  ECGR 3122 or equivalent. Fourier methods of medical signal processing. Physics of image formation for different medical imaging modalities including: planar x-ray, computerized tomography (CT), magnetic resonance imaging, and ultrasound. Signal processing techniques for image reconstruction, enhancement, and multimodal fusion. Credit will not be given ECGR 6127 where credit has been given for ECGR 8127. (Spring)

This course instructs students in the use probablistic and statistical mathematics to develop computational models for the purpose of recognizing patterns in low-dimensional and high-dimensional spaces. Course topics include probability theory, introductory information theory, linear regression models, linear classifiers. Advanced topics in pattern recognition are also discussed and will vary each year.

This course instructs students in the use probabilistic and statistical mathematics to develop computational models for the purpose of recognizing patterns in low-dimensional and high-dimensional spaces. Course topics include probability theory, introductory information theory, linear regression models, linear classifiers. Advanced topics in pattern recognition are also discussed and will vary each year.

This course is intended to teach students how to process images for the purpose of recognizing objects within images. The course will deal primarily with 2D images of natural scenes, i.e., images taken from a conventional digital camera, and with 3D images, i.e., images that represent the geometry of a scene as a collection of (x,y,z) coordinates.

The primary textbook for the course will be Trucco and Verri's Introductory Techniques for 3-D Computer Vision (publisher: Prentice Hall, 1998,ISBN-10: 0132611082, ISBN-13: ISBN-13: 978013261108).

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This course builds upon image and signal processing concepts and introduces the field of computer vision, modeling human perception with computers. Topics covered include image formation, radiometry, photometry, shading, 3D coordinate systems, homogeneous coordinates, stereoscopic 3D reconstruction, elementary differential geometry, and algorithms for processing 3D range and mesh surface data.

This is an inter-disciplinary course that integrates concepts from mathematics, physics, engineering and computer science to educate students on the design of intelligent spacecraft. Course instruction takes a new tact best summarized by the expression: All science was new at some point. This approach augments class topics with historic context and, in some cases, facsimiles of original works such as Galileo's theory on planetary motion. Course topics include mathematical models of planetary motion and heat transfer and how these models are used in designing intelligent spacecraft, i.e., robotic systems which can autonomously perform complex space-mission tasks.

This course builds upon image and signal processing concepts and introduces the field of computer vision, modeling human perception with computers. Topics covered include image formation, radiometry, photometry, shading, 3D coordinate systems, homogeneous coordinates, stereoscopic 3D reconstruction, elementary differential geometry, and algorithms for processing 3D range and mesh surface data.