Event Type | Date | Description | Course Materials |
---|---|---|---|
Lecture 1 | Tuesday September 26 |
Course introduction Computer vision overview Course logistics |
Introduction slides
[pptx]
[pdf]
Logistics slides [pptx] [pdf] Lecture 1 notes [pdf] |
Lecture 2 | Thursday September 28 |
Color + Math basics Physics of light Human encoding of color Color Spaces White Balancing Vectors and Matrices |
Color spaces slides
[pptx]
[pdf]
Lecture 2 notes [pdf] python/numpy tutorial [pdf] |
HW0 Due | Monday October 2, 11:59pm |
Homework #0 due Basics |
[Homework #0] |
Lecture 3 | Tuesday October 3 |
Linear algrebra Transformation matrixes Eigenvalues and eigenvectors Matrix calculus and hessian |
Linear algebra slides
[pptx]
[pdf]
Lecture 3 notes [pdf] |
Lecture 4 | Thursday October 5 |
Pixels and filters Pixels and image representation Linear systems Convolutions and cross-correlations |
Pixels and filters slides
[pptx]
[pdf]
Lecture 4 notes [pdf] |
HW1 Due | Monday October 10, 11:59pm |
Homework #1 due Filters - Instagram |
[Homework #1] |
Lecture 5 | Tuesday October 10 |
Edge detection Derivative of gaussians Sobel filters Canny edge detector |
Edge detection slides
[pptx]
[pdf]
Lecture 5 notes [pdf] |
Lecture 6 | Thursday October 12 |
Features and fitting RANSAC Local features Harris corner detection |
Features and fitting slides
[pptx]
[pdf]
Lecture 6 notes [pdf] |
Lecture 7 | Tuesday October 17 |
Feature descriptors Difference of gaussians Scale invariant feature transform Image stitching |
Feature descriptors slides
[pptx]
[pdf]
Lecture 7 notes [pdf] |
HW2 Due | Wednesday October 18, 11:59pm |
Homework #2 due Edges - Smart car lane detection |
[Homework #2] |
Lecture 8 | Thursday October 19 |
Resizing Energy function Seam carving |
Resizing slides
[pptx]
[pdf]
Lecture 8 notes [pdf] |
Lecture 9 | Tuesday October 24 |
Semantic segmentation Gestalt theory of perceptual grouping Aggomerative clustering Superpixels and oversegmentation |
Semantic segmentation slides
[pptx]
[pdf]
Lecture 9 notes [pdf] |
HW3 Due | Wednesday October 25, 11:59pm |
Homework #3 due Panorama - Image stitching |
[Homework #3] |
Lecture 10 | Thursday October 26 |
Clustering K-means Mean shift |
Clustering slides
[pptx]
[pdf]
Lecture 10 notes [pdf] |
Lecture 11 | Tuesday October 31 |
Object recognition Nearest neighbors Classification pipeline |
Object recognition slides
[pptx]
[pdf]
Lecture 11 notes [pdf] |
HW4 Due | Wednesday November 1, 11:59pm |
Homework #4 due Resizing - Seam carving |
[Homework #4] |
Lecture 12 | Thursday November 2 |
Dimensionality reduction Singular value decomposition Principal component analysis |
Dimensionality reduction slides
[pptx]
[pdf]
Lecture 12 notes [pdf] |
Lecture 13 | Tuesday November 7 |
Face identification Eigenfaces and fisherfaces Linear Discriminant Analysis |
Face identification slides
[pptx]
[pdf]
Lecture 13 notes [pdf] |
HW5 Due | Wednesday November 8, 11:59pm |
Homework #5 due Segmentation - Clustering |
[Homework #5] |
Lecture 14 | Thursday November 9 |
Visual Bag of Words Texture features Visual bag of words Image pyramids |
Visual bag of words slides
[pptx]
[pdf]
Lecture 14 notes [pdf] |
Lecture 15 | Tuesday November 14 |
Detecting objects by parts Deformable parts model Object detection |
Deformable parts slides
[pptx]
[pdf]
Lecture 15 notes [pdf] |
HW6 Due | Wednesday November 15, 11:59pm |
Homework #6 due Recognition - Classification |
[Homework #6] |
Lecture 16 | Thursday November 16 |
Image classification Imagenet Semantic hierarchy Fine grained classes |
Detection slides
[pptx]
[pdf]
Lecture 16 notes [pdf] |
Lecture 17 | Tuesday November 28 |
Motion Optical Flow Lucas-Kanade method Horn-Schunk Method Pyramids for large motion Common Fate |
Motion
[pptx]
[pdf]
Lecture 17 notes [pdf] |
HW7 Due | Wednesday November 29, 11:59pm |
Homework #7 due Object detection - constellation models |
[Homework #7] |
Lecture 18 | Thursday November 30 |
Tracking Feature Tracking Lucas Kanade Tomasi (KLT) tracker |
Tracking slides
[pptx]
[pdf]
Lecture 18 notes [pdf] |
Lecture 19 | Tuesday December 5 |
Introduction to deep learning Convolutional neural networks Backpropagation |
Deep learning slides
[pptx]
[pdf]
Lecture 19 notes [pdf] |
HW8 Due | Wednesday December 6, 11:59pm |
Homework #8 due Tracking - Optical flow |
[Homework #8] |
Lecture 20 | Thursday December 7 |
Final Review Summary of class |
Final review talk [pptx] [pdf] |
Final | Monday | December 11,12:15 to 3:15pm Location: 320-105 |
Practice final [pdf] |