CS4501: Introduction to Computer Vision | Spring 2021
|Tue, Feb 2nd||Introduction to Computer Vision [pptx] [pdf]|
|Thu, Feb 4th||Cameras and Image Formation [pptx] [pdf]|
Assignment 1: Image Formation, Image Processing, and Image Filtering
Due Sunday February 21st, 11:59pm (ET).
|Tue, Feb 9th||Projective Geometry and Light [pptx] [pdf]|
|Thu, Feb 11th||Human Vision and Image Processing [pptx] [pdf]|
|Tue, Feb 16th||Image Filtering and Image Frequencies [pptx] [pdf]|
|Thu, Feb 18th||Frequencies and Edges [pptx] [pdf]|
Assignment 2: Detecting Corners and Lines, Hough Transform.
Due Sunday March 7th, 11:59pm (ET).
|Tue, Feb 23th||Interest Points: Corners and Blobs [pptx] [pdf]|
|Thu, Feb 25th||Hough Transform and SIFT Feature [pptx] [pdf], RANSAC [pptx] [pdf]|
|Tue, Mar 2nd||Camera Calibration and Dense Stereo [pptx] [pdf]|
|Thu, Mar 4th||No Class -- Work on the Assignment please.|
|Tue, Mar 9th||No Class -- Spring Break Day|
Assignment 3: Dense Stereo and Depth Estimation
Due Friday March 26th, 11:59pm (ET).
|Thu, Mar 11th||Feature Matching and Dense Stereo [pptx] [pdf]|
|Tue, Mar 16th||Quiz|
|Thu, Mar 18th||Quiz Review and Introduction to Epipolar Geometry [no slides]|
|Tue, Mar 23th||The Essential Matrix and Multi-view Geometry [pptx] [pdf]|
|Thu, Mar 25th||Introduction to Machine Learning [pptx] [pdf]|
Assignment 4: Image Classification and Machine Learning [Colab].
Due Sunday April 11th, 11:59pm (ET).
|Tue, Mar 30th||Linear Classifiers (Softmax) and Stochastic Gradient Descent [pptx] [pdf]|
|Thu, Apr 1st||Stochastic Gradient Descent, Regularization, and Optmization [pptx] [pdf]|
|Tue, Apr 6th||Neural Networks and Backpropagation (by Guest Lecturer: Fuwen Tan) [pptx] [pdf]|
|Thu, Apr 8th||Convolutional Neural Networks [pptx] [pdf]|
|Assignment 5: Image Segmentation and other more advanced topics|
|Tue, Apr 13th||Convolutional Neural Networks for Image Classification [pptx] [pdf]|
|Thu, Apr 15th||No Class - Spring Break Day|
|Tue, Apr 20th||Convolutional Neural Networks for Image Segmentation|
|Thu, Apr 22th||Convolutional Neural Networks for Object Detection|
|Tue, Apr 27th||Generative Adversarial Networks (GANs)|
|Thu, Apr 29th||Optical Flow and Video Recognition|
|Tue, May 4th||Computer Vision Applications Review Session|
|Tue, May 6th||Course Recap|
Disclaimer: The professor reserves to right to make changes to the syllabus, including assignment due dates. These changes will be announced as early as possible.
Grading: To be announced officially on first day of class. Assignments: 100% (25% + 25% + 25% + 25%). There are a total of five assignments in the class, so the lowest grade will be dropped. Class Participation: +5% (extra) -- includes synchronous participation + Piazza + office hours participation, Quiz: +5% (extra). Grade cutoffs: A+ (95%) AND complete all assignments on time and with good grades, A (90%), A- (85%), B+ (80%), B (75%), B- (70%), C+ (65%), C (60%), C- (55%), D+ (50%), D (45%), D- (40%).
Late Submission Policy: No late assignments will be accepted in this class. Unless the student has procured special accommodations for warranted circumstances. We acknowledge the ongoing pandemic and want to be mindful of any special circumstances associated with it. We will be accommodating also due to exceptional circumstances but this is a large class so please make sure this is truly warranted and contact us as soon as possible. Please take into account that due to the ongoing pandemic the grading policies of this class have already been relaxed to drop the lowest scored assignment from the final grade computations, this includes an assignment with a grade of zero that was not submitted for whatever reason.
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