Yiwen Chen

pic1 pic2

## 2. Recover Homographies¶

40 corresponsences are defined mainly on the zebra lines and windows:

## 3. Warp the Images¶

pic 1 after warping

## 4.Image Rectification¶

example 1: Bicycle

example 2: Billboard

## Blend images into a mosaic¶

By simply taking average at overlapping pixels, I get the following result.

## 2.Implement Feature Descriptor extraction¶

By adding a 40 by 40 window around corner points, I get patches like this. After resizing 40 by 40 patches to 8 by 8 patches and normalize, the patches look like this

## 3.Feature Matching¶

I set the threshold on the ratio between the first and the second nearest neighbors to 0.5 and applied reciprocal matching. The results are as follow.

There are a few outliers.

## 4.4-point RANSAC¶

To remove outliers, I applied RANSAC and set epsilon to 6, number of loops to 600. The results are as follow.

## 5. Morphing and mosaic¶

Using the homogrphy determined by the inlier group, I get the following warping result:

Here is the comparison of automatically stitched mosaic using the above warping result and manually stitched mosaic from part 1:

automatically stitched mosaic: manually stitched mosaic

The result of manually stitched mosaic is slightly better than the automatically stiched one. This is probably because the corresponding keypoints manaully selected are more evenly distributed than the ones filterd by RANSAC. As we can see in previous part, most points on the zebra stripes are filtered out by RANSAC.

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