局部特征匹配 Local Feature Matching

Local Feature Matching
CS 143: Introduction to Computer Vision

Brief

Due: 11:59pm on Wednesday, October 9th, 2013
Stencil code: /course/cs143/asgn/proj2/code/
Data: /course/cs143/asgn/proj2/data/ includes 93 images from 9 different outdoor scenes.
Html writeup template: /course/cs143/asgn/proj2/html/
Partial project materials are also available in proj2.zip (1.7 MB).
Includes only the two test images shown above.
Handin: cs143_handin proj2
Required files: README, code/, html/, html/index.html

Overview

The goal of this assignment is to create a local feature matching algorithm using techniques described in Szeliski chapter 4.1. The pipeline we suggest is a simplified version of the famous SIFT pipeline.
The matching pipeline is intended to work for instance-level matching -- multiple views of the same physical scene.

Details

For this project, you need to implement the three major steps of a local feature matching algorithm:
Interest point detection in 

get_interest_points.m

 (see Szeliski 4.1.1)
Local feature description in 

get_features.m

 (see Szeliski 4.1.2)
Feature Matching in 

match_features.m

 (see Szeliski 4.1.3)

There are numerous papers in the computer vision literature addressing each stage. For this project, we will suggest specific, relatively simple algorithms for each stage. You are encouraged to experiment with more sophisticated algorithms!

Interest point detection (

get_interest_points.m

 )

You will implement the Harris corner detector as described in the lecture materials and Szeliski 4.1.1. See Algorithm 4.1 in the textbook for pseudocode. The starter code gives some additional suggestions. You do not need to worry about scale invariance or
keypoint orientation estimation for your baseline Harris corner detector.

Bells & whistles for interest point detection. Try detecting keypoints at multiple scales or using a scale selection method to pick the best scale. Try estimating the orientation of keypoints to make your local features rotation invariant. You can
also try the adaptive non-maximum suppression discussed in the textbook. Finally, you can try an entirely different interest point detection strategy like that of MSER. If you implement an additional interest point detector, you can use it alone or you can
take the union of keypoints detected by multiple methods.

Local feature description (

get_features.m

)

You will implement a SIFT-like local feature as described in the lecture materials and Szeliski 4.1.2. See the placeholder 

get_features.m

 for more details. If you want to get your matching pipeline working quickly (and maybe to help debug the other
algorithm stages), you might want to start with normalized patches as your local feature.

Bells & whistles for local feature description. The simplest thing to do is to experiment with the numerous SIFT parameters: how big should each feature be? How many local cells should it have? How many orientations should each histogram have? Different
normalization schemes can have a significant effect, as well. Don't get lost in parameter tuning, though. If your keypoint detector can estimate orientation, your local feature descriptor should be built accordingly so that your pipeline is rotation invariant.
Likewise, if you are detecting keypoints at multiple scales, you should build the features at the corresponding scales. You can also try different spatial layouts for your feature (e.g. GLOH) or entirely different features (e.g. local self-similarity).

Feature matching (

match_features.m

)

You will implement the "ratio test" or "nearest neighbor distance ratio test" method of matching local features as described in the lecture materials and Szeliski 4.1.3. See equation 4.18 in particular. Simply compute all pairs of distances between all features.

Bells & whistles for local feature description. The "ratio" test is simple and can work surprisingly well, but it will allow some obvious outliers to match. More sophisticated matching schemes involve some form of geometric verification. Such verification
can be weak (e.g. is the offset of this match roughly similar to the offset of nearby matches) or stronger (e.g. using RANSAC to estimate explicit transformations for groups of features and rejecting matches that are incompatible with the dominant transformation).
Another issue with the baseline matching algorithm is the computational expense of computing distance between all pairs of features. For a reasonable implementation of the base pipeline, this is likely to be the slowest part of the code. There are numerous
schemes to try and approximate or accelerate feature matching such as lowering the dimensionality of the features or using space partitioning data structures to accelerate the nearest neighbor computations. All of these methods are fundamentally lossy, though.

Using the starter code (

proj2.m

)

The top-level 

proj2.m

 script provided in the starter code includes file handling, visualization, and evaluation functions for you as well as calls to placeholder versions of the three functions listed above. Running the starter code without modification
will visualize random interest points matched randomly on the particular Notre Dame images shown at the top of this page. For these two images there is a ground truth evaluation in the starter code, as well. 

evaluate_correspondence.m

 will classify
each match as correct or incorrect based on similarity to hand-provided matches (run 

show_ground_truth_corr.m

 to see the ground truth annotations). The starter code only includes ground truth for this image pair, but you can create additional
ground truth matches with 

collect_ground_truth_corr.m

, although it is a tedious process.

As you implement your feature matching pipeline, you should see your performance according to 

evaluate_correspondence.m

 increase. Hopefully you find this useful, but don't overfit to these particular (and relatively easy) images. The baseline
algorithm suggested here and in the starter code will give you full credit and work fairly well on these Notre Dame images, but additional image pairs (provided in 

/course/cs143/asgn/proj2/data/

are more difficult. They might exhibit more viewpoint,
scale, and illumination variation. If you add enough Bells & Whistles you should be able to match more difficult image pairs.

Potentially useful MATLAB functions: 

imfilter(), fspecial(), bwconncomp(), colfilt(), sort()

. 
Forbidden functions you can use for testing, but not in your final code: 

extractFeatures(), detectSURFFeatures()

.

More Bells & Whistles (Extra Credit)

In addition to the specific suggestions above for each stage of the pipeline, you can try running a structure from motion algorithm based on the feature matches you find. For example, try configuring the widely used VisualSFM
package or Bundler to accept the feature matches you find rather than their default (SIFT-based)
matches. If you find many matches between many photos you can achieve a sparse 3d reconstruction of the physical scene. You can then use a stereo algorithm to generate a dense reconstruction. These Bells & Whistles may be quite involved, though, because you'll
be dealing with complicated software packages.

Writeup

For this project, and all other projects, you must do a project report in HTML. We provide you with a placeholder .html document which you can edit. In the report you will describe your algorithm and any decisions you made to write your algorithm a particular
way. Then you will show and discuss the results of your algorithm.

In the case of this project, show how well your matching method works not just on the Notre Dame image pair, but on additional test cases. For the Notre Dame images, you can show 

eval.jpg

 which the starter code generates. For other image pairs,
there is no ground truth evaluation (you can make it!) so you can show 

vis.jpg

 instead. A good writeup will assess how important various design decisions were. E.g. by using SIFT-like features instead of normalized patches, I went from 70% good
matches to 90% good matches. This is especially important if you did some of the More Bells & Whistles and want extra credit. You should clearly demonstrate how your additions changed the behavior on particular test cases.

Rubric

+30 pts: Implementation of Harris corner detector in 

get_interest_points.m

+40 pts: Implementation of SIFT-like local feature in 

get_features.m

+10 pts: Implementation of "Ratio Test" matching in 

match_features.m

+20 pts: Writeup with several examples of local feature matching.
+10 pts: Extra credit (up to ten points)
-5*n pts: Lose 5 points for every time (after the first) you do not follow the instructions for the hand in format

Web-Publishing Results

All the results for each project will be put on the course website so that the students can see each other's results. The professor and TA will select "winning" projects that impress us and there will be in class presentations for these projects. If you do
not want your results published to the web, you can choose to opt out. If you want to opt out, email cs143tas[at]cs.brown.edu saying so.

Handing in

This is very important as you will lose points if you do not follow instructions. Every time after the first that you do not follow instructions, you will lose 5 points. The folder you hand in must contain the following:
README - text file containing anything about the project that you want to tell the TAs
code/ - directory containing all your code for this assignment
html/ - directory containing all your html report for this assignment (including images). Only this folder will be published to the course web page, so your webpage cannot contain pointers to images in other folders of your handin.
html/index.html - home page for your results

Then run: cs143_handin proj2

If it is not in your path, you can run it directly: /course/cs143/bin/cs143_handin proj2

Credits

Assignment developed by James Hays

from: http://cs.brown.edu/courses/cs143/proj2/