V22.0480-001, Computer Vision
Mon/Wed 9:30 a.m.-10:45 a.m.
CIWW 109 (Moved to the Multimedia Center, 719 Broadway, 1221, 12 floor)
This course will look at basic ideas on Computer Vision and how they
work. Students will be encouraged to understand well the material as well as
develop software that works on images. The course will cover
basic Image Processing tools (convolution, filtering, and multiscale
representation of images), Edge detection, Contour Detection/Segmentation,
Character Recognition, Skeletonization, Stereo Vision, Motion (Optial
Flow and 3D Structure), Shape From Shading and Color, Recognition.
Instead of books I recommend to read suplement course material from
and some specific materials are linked below.
There will be homeworks, about every two weeks, with programming
assignments and written assignments. The student will be exposed to
both, experiments (programming) and theory (written homeworks).
There will be a course project, starting at the beginning of the course
(by the fourth lecture it starts). There will be a choice of different
projects. The project will "substitute the mid term exam".
There will be a final exam for the students to fully review the material.
Class 1: Introduction
Class 2: Surface Reflectance, Radiance and Irradiance.
Class 4: Edge Enhacement and Detection. Problem set 1. 2 pages postscript .
Class 6 and 7: Learning with perceptron.
Class 8: Multilayer Networks.
PROJECT: Here you find a description of the
projectdata.zip is a zip file with testing and training data for
numerals. See the readme file .
Jong Oh have offered a sample of a Neural Net code to add numbers with one hidden layer: Neural Net Sample Code
Class 9 and 10: Stereo Vision.
Class 11, 12, and 13: Motion. Optical Flow and Structure from
Motion. homework 2
Class 14 and 15: Detecting Snakes and the Shortest Path
Class 15 and 16: Tutoring on Optical Flow.
Class 17 and 18: Texture.