[SOLVED] (csc420) assignment 3 1. [2 points] a robber left his/her shoe behind.

1. [2 points] A robber left his/her shoe behind. Police took a picture of it, see shoe.jpg.
Estimate the width and length (in centimeters) of the shoe from the picture as accurately as possible! Show your work.2. For this question you do not need to write code, you do need to write the equations
and show your work (i.e. how you derived them). If you need to calculate the location
of any points or lines in the image plane, state those as givens; but use the minimal
set. You can assume that the ground plane is orthogonal to the image plane. Hint:
Draw the scenes on paper from the side.(a) [2 points] Examine image tracks.jpg. Assume you are given K, R, t. Are you
able to estimate the distance between adjacent railway ties in world co-ordinates?
If so, write the necessary equations as a function of the pixel locations, camera
intrinsic and/or extrinsic matrices.(b) [2 points] Examine image man.jpg. The camera centre is 95 centimetres off
the ground. You can assume the ground is planar. Are you able to estimate the
height of the man in centimetres without K? If so, derive and write the necessary
equations as a function of the pixel locations in the image, and estimate the height.3. You’ve joined a CSI unit, our suspect mugShot.jpg has been implicated in a grisly
crime. Raiding his apartment we recovered a photograph of him sitting with his accomplice, but he knew we were on to him and shredded the photograph! We need
you to implement RANSAC and re-assemble it. You may also use a downloaded implementation of SIFT and any code you wrote for Assignment 2. Python users check
opencv-contrib.(a) [2 points] Create a controlled test case between two affine transformed images,
and develop your RANSAC algorithm to calculate the affine transformation via
least-squares. Visualize the best transformation for the best matching image just
like you did for Assignment 2, exercise 2(d). You can use any tutorial code to
help create your test-case image, or draw one, or take some pictures.(b) [2 points] Shredded contains the shredded picture pieces. Using the mugShot,
try reassembling the image in random permutations and keep the one that best fits
your model. Rank your models by the mean residual SSD. You could alternatively
try a greedy or dynamic programming approach. Show the re-constructed image.
Display your final, best reassembled image.Hint: For speed use down-sampling.4. For this question you will build your own panorama stitching program. You can use
your RANSAC code from above, in conjunction with your matching code from assignment 2 to establish correspondence. However, you now need to estimate a homography
via least squares instead of an affine transformation (the math is in Lecture 9).You
may also use SIFT and any code you wrote for Assignment 2. If you were unable to get
RANSAC working, you can manually set your valid correspondences (or just use the
top k), but state it clearly. You can look at the example here to see the workflow, but
you must write your own code to compute the homographies and blend the panorama.Do not use matchfeatures.m, estimateGeometricTransform.m, step.m. Note,
matlab users imwarp uses xA for its transform, and we’ve written things for Ax
(a) [1 point] Compute the homography between pairs of adjacent images via your
matches. Write your own solution via eigendecomposition; don’t use maketform.m or equivalents. Visualize your result by transforming your keypoints
from one image onto the next, and displaying them as a different colour. Use the
image set in hotel.(b) [1 point] Perform the projective transformation to place the images points into a
common co-ordinate system (at the central image). Remember, this must be performed sequentially and you’ll need to accumulate your transforms from/to the
centre image. Instead of the images, transform the image co-ordinates (via meshgrid, or 4-point rectangles) and display the resulting plot. Your result should look
something like panoMap.png but with axes to show your co-ordinate system.(c) [1 point] Create your panorama. First create a large enough area to display it;
look at your co-ordinate system from (b). You can use imwarp to transform your
images. You do not need to implement any fancy blending, just sum or overlap
your results.5. Extra: [3 points] total (this is an optional exercise) Augment your panorama stitching program to include image blending with centre re-weighting and Laplacian pyramids. See Chapters 3.5.3 and 9.3 in the course textbook.(a) [1 point] Use centre re-weighting to simply average the image pairs.
(b) [1 point] Use centre re-weighting to weight the Laplacian pyramids.
(c) [1 point] Create your own fun examples showing the better performance in comparison to 4d.1 Sources
1. man.jpg Chris Ford, https://www.flickr.com/photos/chrisschoenbohm/4817576839/
2. tracks.jpg Ryan Voetsch, https://www.flickr.com/photos/voetshy/