Description
1) The goal of the first questions is to implement some code that performs calibration using the
method described in the book; by first computing a projection matrix, and then decomposing
that matrix to find the extrinsic and intrinsic camera parameters. Use the approach described
in the slides. I have given you a program, written in C++ that uses OpenCV, called
projection-template.cpp. This program takes ten given 3d points and projects them into a 2d
image using the given supplied camera calibration matrix, rotation matrix and translation
vector. Your goal is to write the two routines that are missing, which are
computeprojectionmatrix and decomposeprojectionmatrix. The first routine
computes the projection matrix using the method described in Section 6.3.1 of the book, and
the second uses the method in Section 6.3.2 to decompose the projection matrix into a camera
calibration matrix, rotation matrix and translation vector. It should be the case that the
computed camera matrix, rotation matrix and translation vector are the same (or very similar)
to the original versions that were used to create the projected points. This shows that your
two routines are working properly. You hand in your program source and the resulting output
file assign3-out created by running this modified program.
5 marks
2) The goal of this question is to create a program that take as input two images that are related
by a rotation homography; a left (keble_a_half), middle (keble_b_long) and creates a single
panoramic image (same size as keble_b_long) as output. This is done by warping the left
“into” the middle image. I have made the middle image big enough to hold both the warped
left and the original middle image. I have given you a program called
akaze-match-template.cpp which takes these two images and computes a set of features that
you can use to compute the homography between them. To actually compute the homography
you use the routine findhomography(, , RANSAC) and then you use warperspective routine
with the computed homography to warp the left image into an image of the same size as the
middle image. In other words you warp img1 into img3, and after that you paste (essentially
an OR operation) img3 into img2. You should output two images; warped, which is the
warped version of img1, and merged which is the warped version of img1 (img3) combined
with img2. I have included two images called warped and merged which show you how they
should look like. Notice that the final merged image has some anomalies because of the OR
operation. In real mosaicking programs you do not see these anomalies. Write down a short
(one paragraph) description of how you would get rid of these visible anomalies. The answer
is simple.
5 marks
