CS6476@GATech-Fall2015-OMS-Syllabus


1	General Information
2	Course Number:	CS 6476-O01
3	Course Name:	Computer Vision
4	Program:	Georgia Tech's Online MS in Computer Science		Link to GT OMS-CS Website
5
6	Team/People
7	Instructor	Irfan Essa		Irfan Essa's Home Page
8		Contact via Piazza. Email for Private and Urgent Issues ONLY		Piazza Site
9	Video Lectures by	Aaron Bobick		Aaron Bobick's Home Page
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11
12	TAs	Robert Capps		Contact via Piazza. Email for Private and Urgent Issues ONLY, Use Post to Instructors OPTION in Piazza
13		Paritosh Gote
14		Will Hancock
15		Aditya Kadur
16		Sarah Moore
17
18	Instructional Designers	Arpan Chakraborty
19	Video Production	Megan Smith
20
21	Assignments & Grading
22	A.	Assignments / Homeworks Type 1: (There will be 1 assignments of this type)	4.0%	4% Each
23	B.	Assignments / Homeworks Type 2: (There will be 7 assignments of this type)	77.0%	11% Each
24	C.	Exam: [Scheduled two weeks before end of term, cumulative, and online]	15.0%
25	F.	Participation on Piazza (Measured by Activity on Piazza)	4.0%	Entire Term
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27		Total	100.0%
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29
30	Policies
31
32	Communications
33	A.	WITH the Professor and TAs should be exclusively through Piazza. No emails! Professor and TAs will do their best to respond to questions within 2 days of posted question.
34	B.	Piazza will serve as the primary and ONLY source of communications and sharing announcements with the students.		Piazza Site for this class
35	C.	All communications should be professional and courteous. TA/Graders and Students are all required to maintian high standards of interaction on Piazza		Piazza Site for this class
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37	Assignments
38	A.	T-square will be used for all assignment submissions and grading. Dates and Deadlines in T-square are the final authority		T-square Site for this class
39	B.	Homeworks Assignments will be graded on a list of criteria (specified on the assignment) such as quality of work, completeness, insight into technical issues, insight into other relevant issues, etc.
40	C.	Each assignment will be graded and returned USUALLY within two weeks of submission. If there is delay for some reason, it will be announced.
41	D.	Late Assignments: Everything is DUE when specified. NO extensions. Each day of LATE submission will result in a 10 point penalty, for up to 2 days, after which you will receive a 0 !
42	E.	See collaboration policy below for more details on how to collaborate
43	F.	Instruction provided with the assignment, MUST be explicitly followed, especially any and all directions like how to submit and the file naming conventions specified
44	G.	Regrade requests can be made using the Google Form (on the right). Please provide clear details as to why you are requesting a regrade. All regrade requests must be made within TWO (2) weeks of the grade release. For grades released in the last two weeks of the term, the regrade request must be made by the last day of the final exams week.		Regrade Request Form
45	F.	All DUE dates will be on the T-square site, and the timezone will be ATLANTA (US-EST) time. Please plan accordingly
46
47	Websites	Following are the websites we will OFFICIALLY use for this class.
48	A.	T-square: For Assignment Submission, Grading, and Final Exam.		T-square
49	B.	Piazza: For Official Announcements, Forums for discussion.		Piazza
50	C.	WordPress Site: (This site) for syllabus/schedule and general information.		Worpdress
51	D.	Udacity for videos of lectures.		udacity.com
52	E.	No information will be shared via any other site (G+, FB, etc.). Students are welcome to create their own social media sites, but none of the instructors are required to be on those sites and will not participate there regularly.
53	G.	As we have a 8 assignments, there will be overlap on assignments. We expect students to manage their schedule to meet the deadlines for each of the assignments
54	H.	Students are welcome to work and submit assignments before their due date. The lectures will all be available from week 1. TAs will try to answer questions related to the assignments as much as they can, but most conversations maybe most active as per the Schedule planned for the class
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56	Grading	Grading Scale (for each assignment/unit and for the entire class).		T-square
57	A	Above 90%
58	B	80%-89.99%
59	C	70%-79.99%
60	D	60%-69.99%
61	F	Below 60%
62		Note: Any work that meets all the requirements will be given a 90%. For scores above 90%, work has to above and beyond meeting the basic requirements of the assigned work.
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64	Honor Code	All assigned work is expected to be individual, except where explicitly written otherwise. You are encouraged to discuss the assignments with your classmates; however, what you hand in should be your own work. If any work product was produced based on discussions with someone else (in the class OR outside), please specify clearly in the final turn-in.		GT Honor Code
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66	Collaboration Policy	As stated above with the Honor Code, but worth making explicit here. Collaboration between students on work assigned in class is fine. You are encouraged to discuss your work with each other. But each individual students MUST submit their own work, done solely by themselves. In some cases, you may have had a fellow student or a non-student friend, help you with an assignment or work (say to take a picture!). You are REQUIRED to acknowledge any help you may have received in completing the work assigned, even as small as holding the light, or suggesting a possible path to a solution. Please be explicit and provide details. We will be checking for code plagarism in our assessment, so please NO copying code from the Web/Internet


1	Week OF		Lectures					Assignments				Readings
2	#	Begin	Module	Title	Lesson	Topic	Duration (min)	PS#	Title	Out (See T-Sq)	Due (See T-Sq)	Slides	Text	Other

3			DUE DATES provided here for just for planning purposes. ACTUAL due dates are in T-Square. Time Zone for this class is Atlanta Time (EST).
4	1	17-Aug	1A	Introduction	1A-L1	Introduction	42	PS1	Images as Functions	17-Aug	31-Aug	pdf		Install Python, OpenCV
5			2A	Linear image processing	2A-L1	Images as functions	44					pdf
6					2A-L2	Filtering	24					pdf
7					2A-L3	Linearity and convolution	34					pdf
8					2A-L4	Filters as templates	13					pdf
9					2A-L5	Edge detection: Gradients	27					pdf
10					2A-L6	Edge detection: 2D operators	19					pdf
11	2	24-Aug	2B	Model fitting	2B-L1	Hough transform: Lines	36	PS2	Edges and Lines	24-Aug	8-Sep	pdf
12					2B-L2	Hough transform: Circles	13					pdf
13					2B-L3	Generalized Hough transform	16					pdf
14			2C	Frequency domain analysis	2C-L1	Fourier transform	36					pdf
15					2C-L2	Convolution in frequency domain	22					pdf
16					2C-L3	Aliasing	35					pdf
17	3	31-Aug	3A	Camera models	3A-L1	Cameras and images	33	PS3	Window-based Stereo Matching	31-Aug	14-Sep	pdf
18					3A-L2	Perspective imaging	26					pdf
19			3B	Stereo geometry	3B-L1	Stereo geometry	26					pdf
20					3B-L2	Epipolar geometry	11					pdf
21					3B-L3	Stereo correspondence	29					pdf
22	4	7-Sep	3C	Camera calibration	3C-L1	Extrinsic camera calibration	24	PS4	Geometry	8-Sep	21-Sep	pdf
23					3C-L2	Instrinsic camera calibration	16					pdf
24					3C-L3	Calibrating cameras	31					pdf
25	5	14-Sep	3D	Multiple views	3D-L1	Image to image projections	10					pdf
26					3D-L2	Homographies and mosaics	33					pdf
27					3D-L3	Projective geometry	14					pdf
28					3D-L4	Essential matrix	22					pdf
29					3D-L5	Fundamental matrix	37					pdf
30	6	21-Sep	4A	Feature detection	4A-L1	Introduction to "features"	13	PS5	Harris, SIFT, RANSAC	21-Sep	5-Oct	pdf
31					4A-L2	Finding corners	39					pdf
32					4A-L3	Scale invariance	23					pdf
33			4B	Feature descriptors	4B-L1	SIFT descriptor	27					pdf
34					4B-L2	Matching feature points (a little)	16					pdf
35			4C	Model fitting	4C-L1	Robust error functions	31					pdf
36					4C-L2	RANSAC	33					pdf
37	7	28-Sep	5A	Photometry	5A-L1	Photometry	35					pdf
38			5B	Lightness	5B-L1	Lightness	26					pdf
39			5C	Shape from shading	5C-L1	Shape from shading	34					pdf
40	8	5-Oct	6A	Overview	6A-L1	Introduction to motion	16	PS6	Optic Flow	5-Oct	20-Oct	pdf
41			6B	Optical flow	6B-L1	Dense flow: Brightness constraint	24					pdf
42					6B-L2	Dense flow: Lucas and Kanade	17					pdf
43					6B-L3	Hierarchical LK	33					pdf
44					6B-L4	Motion models	24					pdf
45	9	12-Oct	7A	Introduction to tracking	7A-L1	Introduction to tracking	14					pdf
46			7B	Parametric models	7B-L1	Tracking as inference	21					pdf
47					7B-L2	The Kalman filter	36					pdf
48	10	19-Oct	7C	Non-parametric models	7C-L1	Bayes filters	23	PS7	Particle Tracking	20-Oct	9-Nov	pdf
49					7C-L2	Particle filters	17					pdf
50					7C-L3	Particle filters for localization	24					pdf
51					7C-L4	Particle filters for real	15					pdf
52	11	26-Oct	7D	Tracking considerations	7D-L1	Tracking considerations	27					pdf
53			8A	Introduction to recognition	8A-L1	Introduction to recognition	21					pdf
54			8B	Classification: Generative models	8B-L1	Classification: Generative models	28					pdf
55					8B-L2	Principle Component Analysis	48					pdf
56					8B-L3	Appearance-based tracking	26					pdf
57	12	2-Nov	8C	Classification: Discriminative models	8C-L1	Classification: Discriminative models	27					pdf
58					8C-L2	Boosting and face detection	27					pdf
59					8C-L3	Support Vector Machines	51					pdf
60					8C-L4	Bag of visual words	14					pdf
61	13	9-Nov	8D	Action recognition	8D-L1	Introduction to video analysis	24	PS8	Motion History Images	9-Nov	23-Nov	pdf
62					8D-L2	Activity recognition	32					pdf
63					8D-L3	Hidden Markov Models	46					pdf
64	14	16-Nov	9A	Color spaces and segmentation	9A-L1	Color spaces	36					pdf
65					9A-L2	Segmentation	18					pdf
66					9A-L3	Mean shift segmentation	18					pdf
67					9A-L4	Segmentation by graph partitioning	13					pdf
68	15	23-Nov	9B	Binary morphology	9B-L1	Binary morphology	37					pdf
69			9C	3D perception	9C-L1	3D perception	34					pdf
70			10A	The retina	10A-L1	The retina	38					pdf
71	16	30-Nov	10B	Vision in the brain	10B-L1	Vision in the brain	27					pdf
72	17	1-Dec		Final Exam (T-Square)						26-Nov	9-Dec			Guide