Niraek Jain-Sharma
In this part of the project, I shot several pictures around my neighborhood (northside), and some in my apartment. Below are some of the pictures I took!
House 1/2 Ladder 1/2 Leaves 1/2The goal of this project was to recover the homographies from one image into the coordinate system of another. First, I used Gimp to hover over similar points in both images, wrote them down, and exported them to a csv and read them in using pandas. See below for an example of the correspondence points for one pair of images:
Now, let's describe how to solve for the homography matrix. Because we have more than 4 points in the labeling as shown above, we use least squares to solve for A, namely Min(||Ax-b||^2). We apply the following and get the homography matrices.
In this part, we use the homography matrix we calculated in the previous part, and warp our image onto the correspondence points of our second image. This will allow us to blend/merge the images once they have the same coordinate system. See below for an example of a warped image:
Image Original/warpedWe can utilize the work done above to rectify images! The idea is that if there is a slanted image that takes pictures of things that are in reality specific shapes (e.g. taking a picture of tiles on the floor at an angle, but in reality are squares), then we can actually warp these image using correspondence points to their wanted shape. In the following cases, I chose correspondence points of the corners of the rectangles - both turned out nicely!
Elephant Slanted/Rectified Table Slanted/RectifiedFinally, see below for the blended versions of the mosaics. I set an alpha channel, and put 1 in the center, and did a linspace downward from there radiating to the edges of the picture. As you can see, it does pretty well, the lines are gone!
House Blended Ladder Blended Leaves Blended