Course Detail
Units:
3.0
Course Components:
Lecture
Enrollment Information
Enrollment Requirement:
Prerequisites: Graduate status OR Instructor Consent.
Description
Mathematical Foundations of Imaging, including Linear Systems, Probability and Random Variables and, Detection and Estimation Theory. Topics in Linear Systems include convolution, Fourier series and transforms, sampling and discrete-time processing of continuous-time signals. Topic in Probability and Random Variables include distribution functions, density functions, expectations, means, variances, combinatorial probability, joint distribution, independence, correlation, Bayes theorem, the law of large numbers, and the central limit theorem. Topic in Detection and Estimation Theory include detection of signals in noise, estimation of signal parameters, linear estimation theory.