Tony Lian's Project 4

Note: This website has both part A and part B.

PRPJECT 4 PART A: IMAGE WARPING and MOSAICING

Shoot the Pictures (20 pts)

I shoot 10 images from a garden from the same viewing point with different perspectives on my phone, with AE/AF lock that preserves lighting and focus. Some images are too bright and too dark because of this, so I pick and choose from these 10 images.

Recover homographies (20 pts)

I add at least 4 points on pairs of images used by later parts, as an example, I add 4 points to this pair of images.

Then, I turn the projection formula: $$p' = Hp$$ where p' is the objective point and p is the point that we want to warp, into a formula $$A\textbf{h}=b$$ where $\textbf{h}$ vector includes 8 parameters that are the degrees of freedom in $H$: $$\textbf{h} = [a, b, c, d, e, f, g, h]^T$$ For each correspondence, I add two equations:

$$a p_1 + b p_2 - g p_1p_1' - hp_2p_1' + c - p_1' = 0$$

$$d p_1 + e p_2 - g p_1p_2' - hp_2p_2' + f - p_2' = 0$$

These are linear equations that can form a system, and to be solved either precisely or with least squares if the number of equations is greater than 8 (the degree of freedom).

Warp the images (20 pts) [produce at least two examples of rectified images]

I then warp two images with rectangle structure into front-facing images.

Before warping:

With warping:

Before warping:

With warping:

The second one has artifacts because the original region is too small.

Blend images into a mosaic (20 pts) [show source images and results for three mosaics.]

Mosaic 1:

Mosaic 2:

Mosaic 3:

Optional Bells and Whistles (up to 20 pts, combined from Parts A and B)

Be creative: Image-in-Image

Original image:

Image in image:

Tell us what you've learned

I finally learned how panorama taken on the phone is synthesized. It's amazing to learn these transformations.

PRPJECT 4 PART B: FEATURE MATCHING FOR AUTOSTITCHING

Example Images:

Detecting corner features in an image (10 pts)

Harris corners on my image:

With Adaptive NMS:

Extracting a Feature Descriptor for each feature point (10 pts)

Example feature:

Matching these feature descriptors between two images (20 pts)

Matches:

Note that although the number of corners decreases a lot, there are still some mismatches. It's true that we can adjust threshold to limit the number of corners, but it causes too few corners for alignment for some images. I found that these mismatches can be addressed by RANSAC, so a little higher threshold for first/second matches (more matches) actually helps.