PPT-Robust fitting Camera calibration

Given a set of correspondences between 3D world points and image points Set up an optimization problem Solve for   Homography estimation Given a set of correspondences between 3D world points . 1. Color 2. Camera models, camera calibration 3. Advanced image pre - processing e Line detection e Corner detection e Maximally stable extremal regions 4. Mathematical Morphology e binary e gray - FADGI. and . Metamorfoze. Camera Color Calibration at National Gallery of Art. Kenneth N. Fleisher. Color Scientist. National Gallery of Art. Cultural Heritage Imaging Professionals . Conference. Stanford, CA. Sean R. Stratton. TA-All Meeting – June 2010. Motivation. We’re still trying to understand the relative energy scale differences between our FDs.. The UVLED was used to . cross-calibrate all three FD sites.. -2--Consider the above calibration pattern:*itconsists of twoorthogonal grids.*equally spaced black squares drawn on white, perpendicular planes.*assume that the world reference frame is centered at t C. omputer . V. ision. CH6. Feature-based Alignment. Professor: Prof. . Fuh. Presenter: Nick Chu. . (. 祝成豪. ). Taught. . Way. Cover the main parts of textbook . c. hapter 6. . Extra resources on EGGN 512 course videos. From Electrical Engineering & Computer Science, Colorado School of Mines (CSM/Mines). Ye. Franz .  Alexander Van . Horenbeke. David . Abbott. Index. Introduction. Background. Hardware. Software. System Design. Algorithm. Pupil. . Localization. Ellipse Fitting. Calibration. Homographic Mapping. M. ulticam. .. Ettienne Cox (. ecox@csir.co.za. ). Ettienne. . Cox. CSIR . - . Where do we fit in. Multicam. background. Multicam. Commercialization. C. urrent . status. Future prospects. . Content. and calibration. 15-463, 15-663, 15-862. Computational Photography. Fall 2018, Lecture 13. http://graphics.cs.cmu.edu/courses/15-463. Course announcements. Homework 3 is out.. - Due October 12. th. . We would like to form a higher-level, more compact representation of the features in the image by grouping multiple features according to a simple model. Source: K. Grauman. Fitting. Choose a . parametric model . Perspective projection in homogenous coordinates. Matrix transformations in 2D. Translation. Scaling of Image x and y (conversion from “meters” to “pixels”). Added skew if image x and y axes are . results. Icíar. Montilla, . M. . Reyes, M. . Núñez. , J. Marco de la . R. osa, O. . Tubio. , M. . Puga. (. Instituto. de . Astrofísica. de Canarias). Ali . Basden. . (Durham University). GTCAO overview. EECS 442 – Prof. David . Fouhey. Winter 2019, University of Michigan. http://web.eecs.umich.edu/~fouhey/teaching/EECS442_W19/. Administrivia . Homework 4 due . Sunday. Homework 4 due Thursday (1 week). Final perspective projection. Camera . intrinsics. : how your camera handles pixel. Changes if you change your camera . Camera . extrinsics. : where your camera is relative to the world. Changes if you move the camera. Resectioning. Basic equations. Basic equations. minimal solution. Over-determined solution. 5½ correspondences needed (say 6) . P has 11 dof, 2 independent eq./points. n .  6 points. minimize subject to constraint .