CS 194-26 Project 5: Auto Stitching Photo Mosaics

2020 Nov 26, cs194-26 (Kecheng Chen)

Part A

Shoot the Pictures

Target at Berkeley downtown

Recover Homographies

To calculate homography matrix, the below formulation is followed. The rightest column of V which corresponds to the smallest signular value is the solution.

Control points selection

Normalized cross-correlation matching

Warp the Images

Image Rectification

Blend the images into a mosaic

Laplacian pyramid is used to do the image blending.

Naive addition

Naive blending

Two-level pyramid

Five-level pyramid

Tell us what you've learned

I learned that even without depth information, 2D scenary construction can also be done.



Part B

Detecting corner features in an image

Adaptive Non-Maximal Suppression

Feature Descriptor extraction

Feature Matching

SSD(first nearest neighbourhood)/SSD(second nearest neighbourhood)<0.3

Directly stitch without RANSAC (not good)


What have you learned?

Automatic image stitching may perform better effect compared with the approach using manually selected feature points. It saves a lot of time. However, there are also some failure cases. When I applied this automatic method on the images with large angle interval, less than 4 pairs of feature points would be selected after feature matching. It is not enough for calculating H matrix. To deal with this situation, more advanced technology like deep learning can be tried. Also, there are some cases that feature points are wrongly matched.