All electronic submissions are due at noon on the day indicated, unless specified otherwise.
Please note that future homework assignments are tentative based on previous course offerings. We may change assigned homework at any time before it is assigned. Schedule subject to change. Corresponding sections from Sonka, et al. are given at the end where appropriate.
Schedule last updated 21 Nov.
| Week / Main topic | Monday | Tuesday | Thursday | Friday |
| 1: Intro to images, color | (1) 11/28: Intros, Images and color Start Lab 1: Intro to Matlab (Ch 1) |
(2) 11/29: Color features Due in class: Read sunset paper Lab 1 due Weds noon. (2,2, 2,4) |
(3) 12/1: Connected components, morphology in Matlab Start Fruit-finder. (13.1-13.3) |
(4) 12/2: Lab 2: Color Laptops for every lab |
| 2: Global and local features, edges | (5) 12/5: Global and local operators, filtering (5.1, 5.3) | (6) 12/6: Edge Masks Lab 2 due (Weds, noon always) (5.3) |
(7) 12/8: Edge features (5.3) |
(8) 12/9: Lab 3: Edges and filters Due: Fruit-finder 11:00 pm |
| 3: More features | (9) 12/12: Region properties (perimeter, circularity) (8.1-8.3) | (10) 12/13: Spatial moments Lab 3 due (Weds) (8.3) |
(11) 12/15: Classification concepts (9.2.1) | (12) 12/16: Lab 4: Shape. |
| 4: Classifiers | (13) 12/19: Support vector machines (9.2.4) | (14) 12/20: Finish SVMs and demo. Lab 4 due (Weds) Formally assign sunset detector |
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| Christmas Break | ||||
| 4b: Classifiers | (15) 1/5: (Optional class if you have exam clarification questions) Take-home test due 5:00 pm. |
(16) 1/6: Lab 5: SVM toolbox. |
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| 5: Sunset detector | (17) 1/9: Neural nets (9.3.1) | (18) 1/10: Neural nets and SVM Exam Q&A Lab 5 due (Weds) |
(19) 1/12: Lightning talks, assign teams | (20) 1/13: Lab day: sunset detector |
| 6: Segmentation and clustering | (21) 1/16: (Project workday, 40 min from convocation) | (22) 1/17: Midterm Exam (moved here from day 21 due to convocation)
Sunset detector due (Thursday, 11:00 pm) |
(23) 1/19: k-means segmentation (9.2.5) | (24) 1/20: Lab 6: k-means Due: Lit reviews (Sunday, 11:00 pm) |
| 7: Segmentation and object detection | (25) 1/23: PCA and applications (3.2.10) | (26) 1/24: Hough transforms (6.2.6)
Lab 6 due (Weds) |
(27) 1/26: Bayesian classifiers (9.2.2) | (28) 1/27: Lab 7: Hough transform or PCA Due: Project plans and preliminary work (Sunday, 11:00) |
| 8: Motion and special topics | (29) 1/30: Surveillance and image flow (16.2) |
(30) 1/31: Aperture problem, Motion vectors (16.3) Lab 7 due (Weds) |
(31) 2/2: Template matching and HOG (6.4) | (32) 2/3: Lab: Project Milestone Reviews Due: Status report (8:00 am) |
| 9: Special topics and project work | (33) 2/6: Convolutional neural networks | (35) 2/7: Guest lecture by David Crandall 4:20 PM (reserved for exam 2 after 2017) | (34) 2/9: Lab: Projects | (36) 2/10: Lab: Project Milestone Reviews Due: Status report (8:00 am) |
| 10: Presentations | (37) 2/13: Course evals In-class presentation: 1 Champion |
(38) 2/14: in-class presentations: 1 Face |
(39) 2/16: In-class presentations: 1 Autos 2 Stabilizer |
(40) 2/17: In-class presentations: 1 Deep 2 Math Due: final project (code, report, and presentations), 11:00 PM |