Event Type | Date | Description | Course Materials |
---|---|---|---|
Lecture 1 | Tuesday September 25 |
Course introduction Computer vision overview Course logistics |
Slides
[pptx]
[pdf]
Logistics slides [pptx] [pdf] |
Lecture 2 | Thursday September 27 |
Linear algrebra Transformation matrixes Eigenvalues and eigenvectors Matrix calculus and hessian |
Slides
[pptx]
[pdf]
Color Slides [pptx] [pdf] |
HW0 Due | Monday October 1, 11:59pm |
Homework #0 due Basics |
[Homework #0] |
Lecture 3 | Tuesday October 2 |
Linear algebra continued & Pixels Pixels and image representation |
Slides
[pptx]
[pdf]
|
Lecture 4 | Thursday October 4 |
Filters Linear systems Convolutions and cross-correlations |
Slides
[pptx]
[pdf]
|
Lecture 5 | Tuesday October 9 |
Edge detection Derivative of gaussians Sobel filters Canny edge detector |
Slides
[pptx]
[pdf]
|
Lecture 6 | Thursday October 11 |
Features and fitting RANSAC Local features Harris corner detection |
Slides
[pptx]
[pdf]
|
HW1 Due | Friday October 12, 11:59pm |
Homework #1 due Filters - Instagram |
[Homework #1] |
Lecture 7 | Tuesday October 16 |
Feature descriptors Difference of gaussians Scale invariant feature transform Image stitching |
Slides
[pptx]
[pdf]
|
Lecture 8 | Thursday October 18 |
Resizing Energy function Seam carving |
Slides
[pptx]
[pdf]
|
HW2 Due | Friday October 19, 11:59pm |
Homework #2 due Edges - Smart car lane detection |
[Homework #2] |
Lecture 9 | Tuesday October 23 |
Semantic segmentation Gestalt theory of perceptual grouping Aggomerative clustering Superpixels and oversegmentation |
Slides
[pptx]
[pdf]
|
Lecture 10 | Thursday October 25 |
Clustering K-means Mean shift |
Slides
[pptx]
[pdf]
|
HW3 Due | Friday October 26, 11:59pm |
Homework #3 due Panorama - Image stitching |
[Homework #3] |
Lecture 11 | Tuesday October 30 |
Object recognition Nearest neighbors Classification pipeline |
Slides
[pptx]
[pdf]
|
Lecture 12 | Thursday November 1 |
Dimensionality reduction Singular value decomposition Principal component analysis |
Slides
[pptx]
[pdf]
|
HW4 Due | Friday November 2, 11:59pm |
Homework #4 due Resizing - Seam carving |
[Homework #4] |
Lecture 13 | Tuesday November 6 |
Face identification Eigenfaces and fisherfaces Linear Discriminant Analysis |
Slides
[pptx]
[pdf]
|
Lecture 14 | Thursday November 8 |
Visual Bag of Words Texture features Visual bag of words Image pyramids |
Slides
[pptx]
[pdf]
|
HW5 Due | Friday November 9, 11:59pm |
Homework #5 due Segmentation - Clustering |
[Homework #5] |
Lecture 15 | Tuesday November 13 |
Detecting objects by parts Deformable parts model Object detection |
Slides
[pptx]
[pdf]
|
Lecture 16 | Thursday November 15 |
Visual ontologies Imagenet Semantic hierarchy Fine grained classes |
Slides
[pptx]
[pdf]
|
HW6 Due | Friday November 16, 11:59pm |
Homework #6 due Recognition - Classification |
[Homework #6] |
Lecture 17 | Tuesday November 27 |
Motion Optical Flow Lucas-Kanade method Horn-Schunk Method Pyramids for large motion Common Fate |
Slides
[pptx]
[pdf]
|
Lecture 18 | Thursday November 29 |
Tracking Feature Tracking Lucas Kanade Tomasi (KLT) tracker |
Slides
[pptx]
[pdf]
|
HW7 Due | Friday November 30, 11:59pm |
Homework #7 due Object detection - constellation models |
[Homework #7] |
Lecture 19 | Tuesday December 4 |
Deep learning Convolutional neural networks Backpropagation |
Slides
[pptx]
[pdf]
|
Lecture 20 | Thursday December 7 |
Deep Learning continued + Final Review Convolutions - revisited |
Slides [pptx] [pdf] |
HW8 Due | Friday December 8, 11:59pm |
Homework #8 due Tracking - Optical flow |
[Homework #8] |
Final | Monday | December 10, 12:15 to 3:15pm Location: 420-040 |
Practice exam available on Piazza. |