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			CSE/EE486 Computer Vision I
		
            
		
            http://www.cse.psu.edu/~rtc12/CSE486/
            http://www.cse.psu.edu/~rtc12/CSE486/lecture12.pdf
            https://stackoverflow.com/questions/10163034/how-can-i-calculate-camera-position-by-comparing-two-photographs
            Background
            I have taught this course several times (almost every semester). I am always fiddling around with the course content, so the material covered and the order of presentation changes from semester to semester. Below are the lecture notes from Fall 2007. 

            In addition to slides that I created, I borrowed heavily from other lecturers whose computer vision slides are on the web. I used to put an attribution at the bottom of each slide as to where and who it came from. However, that led to cluttered slides, and was distracting. So, I dropped that format. Instead, I'm telling you up-front that a lot of the slides in the lectures below did not originate from me. Here is a partial list of the main sources that I can remember: Octavia Camps, Forsyth and Ponce, David Jacobs, Steve Seitz, Chuck Dyer, Martial Hebert. If I forgot you, and you see your slides here, well... thanks. And drop me a line so I can add your name to the list. 

            By the same token, if you are putting together a computer vision course, and want to use some of my slides, go right ahead. You are welcome to them, since the main goal here is to improve the quality of computer vision education everywhere. To quote Thomas Jefferson: "He who receives an idea from me, receives instruction himself without lessening mine; as he who lights his taper at mine, receives light without darkening me. That ideas should freely spread from one to another over the globe, for the moral and mutual instruction of man, and improvement of his condition, seems to have been peculiarly and benevolently designed by nature, when she made them, like fire, expansible over all space, without lessening their density at any point, and like the air in which we breathe, move, and have our physical being, incapable of confinement or exclusive appropriation." Jefferson was one awesome dude.
            Fall 2007 Lecture Notes
            Detailed List of Topics Covered in Fall 2007 

            Lecture 01: Intro to Computer Visionslides6 per page
            Lecture 02: Intensity Surfaces and Gradientsslides6 per page
            Lecture 03: Linear Operators and Convolutionslides6 per page
            Lecture 04: Smoothingslides6 per page
            Lecture 05: Edge Detectionslides6 per page
            Lecture 06: Corner Detectionslides6 per page
            Lecture 07: Template Matchingslides6 per page
            Lecture 08: Introduction to Stereoslides6 per page
            Lecture 09: Stereo Algorithmsslides6 per page
            Lecture 10: Image Pyramidsslides6 per page
            Lecture 11: LoG Edge and Blob Findingslides6 per page
            Lecture 12: Camera Projection (Extrinsics)slides6 per page
            Lecture 13: Camera Projection (Intrinsics)slides6 per page
            Lecture 14: Parameter Estimation; Image Warpingslides6 per page
            Lecture 15: Robust Estimation: RANSACslides6 per page
            Lecture 16: Planar Homographiesslides6 per page
            Lecture 17: Stabilization and Mosaicingslides6 per page
            Lecture 18: Generalized Stereoslides6 per page
            Lecture 19: Essential and Fundamental Matricesslides6 per page
            Lecture 20: The 8-point algorithmslides6 per page
            Lecture 21: Stereo Reconstructionslides6 per page
            Lecture 22: Camera Motion Fieldslides6 per page
            Lecture 23: Optic Flowslides6 per page
            Lecture 24: Video Change Detectionslides6 per page
            Lecture 25: Structure From Motion (SFM)slides6 per page
            Lecture 26: Color and Lightslides6 per page
            Lecture 27: Application: Skin Colorslides6 per page
            Lecture 28: Intro to Trackingslides6 per page
            Lecture 29: Video Tracking: Mean-shiftslides6 per page
            Lecture 30: Video Tracking: Lucas-Kanadeslides6 per page
            Lecture 31: Object Recognition : SIFT Keysslides6 per page
            Lecture 32: Object Recognition : PCA / Eigenfacesslides6 per page
            
		
            
			posted on 2017-11-17 13:46 zmj 閱讀(744) 評論(0)  編輯 收藏 引用  
		
            
	
            
	
	





            
	    
    




            






            

				

            


    
    







            
	   
	



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