Programming Project #4 (second part) 15-463: Computational Photography

FEATURE MATCHING for AUTOSTITCHING

(second part of a larger project)

Due Date: by 11:59pm, Wednesday, March 17

The goal of this project is to create a system for automatically stitching images into a mosaic. A secondary goal is to learn how to read and implement a research paper. The project will consist of the following steps:

1. Detecting corner features in an image
2. Extracting a Feature Descriptor for each feature point
3. Matching these feature descriptors between two images
4. Use a robust method (RANSAC) to compute a homography
5. Proceed as in Project 3 to produce a mosaic

For steps 1-3, we will follow the paper “Multi-Image Matching using Multi-Scale Oriented Patches” by Brown et al. but with several simplifications. Read the paper first and make sure you understand it. Then implement the algorithm:

• Start with Harris Interest Point Detector (Section 2). We won’t worry about muti-scale – just do a single scale. Also, don’t worry about sub-pixel accuracy. Re-implementing Harris is a thankless task – so you can use my sample code: harris.m
• Implement Adaptive Non-Maximal Suppression (Section 3)

• Implement Feature Descriptor extraction (Section 4). Don’t worry about rotation-invariance – just extract axis-aligned 8x8 patches. Note that it’s extremely important to sample these patches from the larger 40x40 window to have a nice big blurred descriptor. Don’t forget to bias/gain-normalize the descriptors. Ignore the wavelet transform section.
• Implement Feature Matching (Section 5). That is, you will need to find pairs of features that look similar and are thus likely to be good matches. You may find dist2.m useful for fast distance computations. For thresholding, use the simpler approach due to Lowe of thresholding on the ratio between the first and the second nearest neighbors. Consult Figure 6b in the paper for picking the threshold. Ignore Section 6.

For step 4, use 4-point RANSAC as described in class to compute a robust homography estimate.

Submit Your Results

You will need to submit all your code. Show results of the different steps of your algorithm: the interest points detected, the various outlier rejection passes, and the final mosaic. Try to apply this automatic method to your (or your classmates) mosaic images from the previous project.

Bells & Whistles

• (10 points) Add multiscale processing for corner detection and feature description.
• (10 points) Add rotation invariance to the descriptors.
• (20 points) Implement panorama recognition. Given an unordered set of images, some of which might form panoramas, you need to automatically discover and stitch these panoramas together. Don’t worry about bundle adjustment, just see how far you can get with pair-wise homography estimation.