====== Computer Vision Class - CS 600.361/461 ====== * Professor: Nicolas Padoy * Class hours: Tu/Th, 9:00-10:15am * Class location: B17, Hackerman Hall * Office hours: 306, Hackerman Hall, Fr, 2-3pm * Teaching assistant: [[mayad1@jhu.edu | Maria Ayad]] * Office hours / problem sessions : Tu 12:00-1pm & Th 4:30-5:30pm, CS undergrad lab ===== Overview ===== This course gives an overview of fundamental methods in computer vision from a computational perspective. Topics include the geometry of one and two views, camera calibration, stereo reconstruction, edge and feature detectors, motion, tracking, image segmentation and classification. The methods will be illustrated by applications in computational photography, object detection and medical image analysis. ===== Prerequisites ===== This course is intended for first year graduate students and advanced undergraduates. The only listed prerequisite is Data Structures. However, familiarity with calculus and basic linear algebra is also necessary to fully understand the material in the course. Elementary probability and statistics is also useful. ===== Announcements ===== * **NOTE** : The syllabus, references and announcements are now posted on **JHU blackboard** only. * 09/09 Assignment #1 is posted on JHU blackboard. * 09/07 A tutorial session on linear algebra & matlab will be given on Thursday, September 8, by Maria Ayad. Location is the CS department undergrad lab (in NEB, 2nd floor). Time: 4:30-5:30pm. ===== Schedule =====

	Dates	Content	Resources
1	Aug. 30	Introduction	slides
	Sep. 01	Image Processing	slides matlab demo lena
2	Sep. 06		slides
	Sep. 08	3D Vision I	slides
3	Sep. 13		slides
	Sep. 15		slides projgeom_appendix
4	Sep. 20	TA lecture	slides SIFT paper
	Sep. 22	slides Tsai_paper Zhang_paper
5	Sep. 27	3D Vision II	slides
	Sep. 29	TA lecture	mathprimer
6	Oct. 04		slides
	Oct. 06		slides
7	Oct. 11	[NO CLASS]
	Oct. 13	Motion analysis	slides
8	Oct. 18	Face recognition	slides eigenfaces paper
	Oct. 20	Segmentation	slides
9	Oct. 25		slides
	Oct. 27	Recognition	slides
10	Nov. 01		slides
	Nov. 03		slides
11	Nov. 08	Object detection	slides detection paper
	Nov. 10	Tracking	slides tracking survey
12	Nov. 15	3D Vision III	slides
	Nov. 17		slides
13	Nov. 22	Texture synthesis	slides synthesis paper
	Nov. 24	[NO CLASS]
14	Nov. 29	Project Presentations
	Dec. 01

===== Resources ===== ==== Textbooks ==== There is no required textbook. Suggested textbooks are: * Computer Vision, by R. Szeliski, Springer, 2011 [[http://szeliski.org/Book/ |(online draft)]] * Computer Vision: A Modern Approach, Forsyth and Ponce, Prentice Hall, 2002. * Multiple View Geometry in Computer Vision, by R. Hartley and A. Zisserman, Cambridge University Press, 2004 * An Invitation to 3D Vision: From Images to Geometric Models, by Y. Ma, S. Soatto, J. Kosecka and S. Sastry, 2004 * Pattern Recognition and Machine Learning, by C. Bishop, Springer, 2006 * Computer Vision: Models, Learning, and Inference, by Simon J.D. Prince, 2012 [[http://www.computervisionmodels.com/ |(online draft)]] ==== Softwares ==== * [[http://opencv.willowgarage.com/wiki/ | OpenCV]] * [[http://www.hitl.washington.edu/artoolkit/ | ARToolkit]] ==== Courses ==== * Last year's [[https://cirl.lcsr.jhu.edu/Vision_Syllabus | computer vision class]] by Prof. Hager at JHU ===== Policies ===== * Grading * Homework: 1/3 * Project: 1/3 * Exam: 1/3 * Late policy * Homeworks are due on the indicated dates * No late homeworks will be accepted * Honor policy * Homework and exams are strictly individual * Please read the [[http://cs.jhu.edu/integrity-code/ | Computer Science Academic Integrity Code]] \\ ===== Best Projects 2011 ===== ==== 3D Reconstructions ====

Marylander

Swirnow

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