Date	Main Topic	Subtopics	Items due
week 1 (Tue: 01/25)	Introduction to Computer Vision (part 1)   Lecture slide 1a	Brief introduction   Course logistics   What is computer vision?	From Images to 3D Models How computers can automatically build realistic 3D models from 2D images acquired with a handheld camera. Marc Pollefeys and Luc Van Gool. ACM Communication'2002
week 1 (Thu: 01/27)	Introduction to Computer Vision (part 2)   Lecture slide 1b	Why is computer vision so primitive?   What makes vision hard?   How does human vision work?   What is state-of-the-art?   Review quiz
week 2 (Tue: 02/01)	Filtering   Lecture slide 2a   Task: manipulating an image?	Image fundamentals   What are the assumptions?   Assumptions for removing noise   Image filtering   Cross-correlation problem
week 2 (Thu: 02/03)	Filtering (continued)   Lecture slide 2b	Convolution   Filtering examples   Practical considerations   Activity: speeding up the convolution?   Review quiz
week 3 (Tue: 02/08)	Edge detection   Lecture 3 slide	Edges and gradients   Computing gradients   Dealing with noise   Canny edge detection   Implementation issues
week 3 (Thu: 02/10)	Edge detection (continued)	Line detection   Hough transform   Hough transform in practice   Activity: a convoluted way of computing gradients?   Review quiz
week 4 (Tue: 02/15)	Segmentation   Lecture 4 slide	Image segmentation   Segmentation as clustering   Clustering on real images   Mean shift clustering   Segmentation with complete graph
week 4 (Thu: 02/17)	Segmentation (continued)	Activity: the problem with cuts?   Normalized cut   Segmentation with spanning trees   Activity: applying popular segmentation on real images   Review quiz
week 5 (Tue: 02/22)	Feature points   Lecture 5 slide	Feature points   Corner detection   Activity: Matrices, error functions, and corners   Identifying corners   Identifying scale and orientation
week 5 (Thu: 02/24)	Feature points (continued)	Scale Invariant Feature Transform (SIFT)   Image and feature matching   Activity: How slow it is?   Activity: applying SIFT on real images   Deep feature learning with Siamese Neural Network (Reza AIPR'20)   Review quiz
week 6 (Tue: 03/01)	Deep Learning   Lecture 6 slide	Neural Network   Convolutional Neural Network (CNN)   CNN Success   Convolution   Spatial pooling   Non-linearity   Case studies of different CNN architectures
week 6 (Thu: 03/03)	Deep learning	Practical implementation of CNN in PyTorch   Review quiz
week 7 (Tue: 03/08)	Midterm exam review
week 7 (Thu: 03/10)	Midterm Exam
week 8 (Tue: 03/15)	Spring break (no class)
week 8 (Thu: 03/17)	Spring break (no class)
week 9 (Tue: 03/22)	Transformation   Lecture 7 slide	Image warping   Activity: Warping   Linear transformation   Homogenous coordinates   Affine and projective transformation
week 9 (Thu: 03/24)	Image matching	Image matching   RANdom SAmple Consensus (RANSAC)   Activity: how many rounds?   RANSAC parameters and implementation details   Review quiz
week 10 (Tue: 03/29)	Projection   Lecture 8 slide	Cameras and projection   Activity: shrinking apertures   Aperture trade-offs   Consequence of projection
week 10 (Thu: 03/31)	Projection (continued)	Modeling projection   Activity: projection practice   Generalizing the model   Adding a lens   Review quiz
week 11 (Tue: 04/05)	Stereo   Lecture 9 slide	Depth from stereo   Epipolar geometry   Epipolar constraint   The case of parallel cameras   Parallel cameras and rectification
week 11 (Thu: 04/07)	Stereo (continued)	Naive stereo matching   Scanline stereo   Stereo matching with deep neural network   Review quiz
week 12 (Tue: 04/12)	Markov Random Fields   Lecture 10 slide	Markov Random Field (MRF)   Some applicaiton of MRFs   Binary MRFs   Graph cuts   Dense pixel-level image labeling with graph-cuts (Reza IROS'17, IROS'19)   Advantage of MRFs   Review quiz
week 12 (Thu: 04/14)	Recognition   Lecture 11 slide	Introduction to recognition   Bag of parts models   Object localization   HOG-based detection   Deformable part models (DPM)
week 13 (Tue: 04/19)	Deep learning-based recognition   Lecture 12 slide	Introduction to deep learning-based recognition   Convolutional neural network for object detection   Fast R-CNN   Mast R-CNN   YOLO   Review quiz
week 13 (Thu: 04/21)	Beyond recognition   Lecture 13 slide	Deep learning for semantic segmentaiton   Review quiz
week 14 (Tue: 04/26)	Beyond recognition	Generative Adversarial Network (GAN)   Recurrent Neural Network (RNN, LSTM, Transformer)
week 14 (Thu: 04/28)	Final project discussion   Course Evaluation
week 15 (Tue: 05/03)	Project presentation
week 15 (Thu: 05/05)	Project presentation
Week 16 (Tue: 05/10)	Final Exam   TBA   TBA		Final Project Code + Report (due by 05/10)