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Daniel B. Rowe, Ph.D.
MATH 4770/MSSC 5770: Statistical Machine Vision
There is not a required book. All material will be presented via lecture.

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Course Syllabus Summer 2026
Syllabus, Schedule Flyer

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Lecture Topics
Course Slides
Lecture 00: Syllabus
Lecture 01: Matlab (Car Data, Father Marquette)
Lecture 02: Within Image Processing (Kernel Movie, Smoothing, Edges, Gradient)
Lecture 03: Image Filter Design (Gaussian, Binomial, Test Image, Test Image)
Lecture 04: Statistical Implications (My Convolution, Colormap Pos-Neg)
Lecture 05: Pixel Statistics & Template Matching (My Correlation, Colormap Pos)
Lecture 06: Through Image Processing (Cat Video)
Lecture 07: The Discrete Fourier Transform (FMRI k-space)
Lecture 08: Convolution via the DFT
Lecture 9: Fast Object Tracking (Shelby Video, Shelby Template, Tracking on Correlation, Tracking on Image)
Lecture 10: Peaks, Valleys, and Ridges
Lecture 11: Machine Vision Summary

All topics and assignments will have a computational aspect using Matlab.

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Return to Professor Rowe's Webpage

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