ECE 564 Computer Vision Assignment 5

Submit HTML documentation and MATLAB code on Isidore.

  1. Use the images taken in class 14 Feb 2018 and the results of Q2 from Asgn 4, to generate figures similar to those for Room KL351G. Compare explictly the results from extrinsics to our measurements. For example, how high is the table? What are the dimensions of the room? See remarks about last assignment.

  2. Use the images taken in class 31 January 2018, particularly those in CV2018_01-31subset.zip, find the distance of the camera from each of the walls and the location camera from the hallway. Note that the zip file contains the corner locations, results of a calibration (from assignment 3, problem 2), and a script to generate the extrinsics (using the MATLAB Computer Vision toolbox).

  3. For these same images, find the associated vanishing points. The line connecting the vanishing points from a single image is the horizon line for the corresponding plane.

  4. Use fitgeotrans and imwarp to generate an ortho view of one of the checkerboard images. You may crop the results to show just the region of the target board. You can use pat2s.jpg to obtain reference points, or you can use the imagePoints/worldPoints.

  5. Use the results of the previous problem to find the projective transform. Then read Hartley and Zisserman, Section 4.1, and write a MATLAB function for the direct linear transformation (DLT) algorithm. The result should match the matrix generated by fitgeotrans or cp2tform within a multiplicative factor.

  6. Study the Bouguet code visualize_distortions and apply_distortion and document the analysis performed. Modify the scripts so they may be used with MATLAB vision toolbox distortion parameters. Show how a large square in the center of the image would be distorted. Muliply the distortion by 10 or more so that the form of distortion, barrel or pincushion is evident. Write a script to plot radial distortion vs. radius. How would you calculate the distortion in percent?

Reference

Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Cambridge, 2000. ISBN 0-521-54051-8.


Maintained by John Loomis, last updated 1 March 2016