The top 100 most confident local feature matches from a baseline implementation of project 2. In this case, 89 were correct (lines shown in green) and 11 were incorrect (lines shown in red).

Project 2: Local Feature Matching
CS 6476: Computer Vision



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.


(if you successfully set up your conda environment for project 1, you can skip this part and reactivate environment cs6476 )

  1. Install Miniconda. It doesn't matter whether you use 2.7 or 3.6 because we will create our own environment anyways.
  2. Create a conda environment, using the appropriate command. On Windows, open the installed "Conda prompt" to run this command. On MacOS and Linux, you can just use a terminal window to run the command. Modify the command based on your OS ('linux', 'mac', or 'win'):
    conda env create -f environment_<OS>.yml
  3. This should create an environment named 'cs6476'. Activate it using the following Windows command:
    activate cs6476
    or the following MacOS / Linux command:
    source activate cs6476
  4. Run the notebook using:
    jupyter notebook ./code/proj2.ipynb
  5. Generate the submission once you've finished the project using:


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

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 ( )

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. The original paper by Chris Harris and Mike Stephens describing their corner detector can be found here.

You will also implement adaptive non-maximal suppression. While most feature detectors simply look for local maxima in the interest function, this can lead to an uneven distribution of feature points across the image, e.g., points will be denser in regions of higher contrast. To mitigate this problem, Brown, Szeliski, and Winder (2005) only detect features that are both local maxima and whose response value is significantly (10%) greater than that of all of its neighbors within a radius r. The goal is to retain only those points that are a maximum in a neighborhood of radius r pixels. One way to do so is to sort all points by the response strength, from large to small response. The first entry in the list is the global maximum, which is not suppressed at any radius. Then, we can iterate through the list and compute the distance to each interest point ahead of it in the list (these are pixels with even greater response strength). The minimum of distances to a keypoint's stronger neighbors (multiplying these neighbors by >=1.1 to add robustness) is the radius within which the current point is a local maximum. We call this the suppression radius of this interest point, and we save these suppression radii. Finally, we sort the suppression radii from large to small, and return the n keypoints associated with the top n suppression radii, in this sorted order. Feel free to experiment with n, we used n=1500.

You can read more about ANMS in the textbook, this conference article, or in this paper which describes a fast variant.

Local feature description (

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

Feature matching (

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. The potential matches that pass the ratio test the easiest should have a greater tendency to be correct matches -- think about why.

Using the starter code (proj2.ipynb)

The top-level proj2.ipynb IPython notebook 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. The correspondence will be visualized with show_correspondence_circles() and show_correspondence_lines() (you can comment one or both out if you prefer).

For the Notre Dame image pair there is a ground truth evaluation in the starter code as well. evaluate_correspondence() will classify each match as correct or incorrect based on hand-provided matches (see show_ground_truth_corr() for details). The starter code also contains ground truth correspondences for two other image pairs (Mount Rushmore and Episcopal Gaudi). You can test on those images by uncommenting the appropriate lines in proj2.ipynb. You can create additional ground truth matches with the CorrespondenceAnnotator().collect_ground_truth_corr() found in annotate_correspondences/ (but it's a tedious process).

As you implement your feature matching pipeline, you should see your performance according to evaluate_correspondence() increase. Hopefully you find this useful, but don't overfit to the initial Notre Dame image pair which is relatively easy. 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 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.

Suggested implementation strategy

It is highly suggested that you implement the functions in this order:

You will likely need to do extra credit to get high accuracy on Mount Rushmore and Episcopal Gaudi.

Potentially useful NumPy (Python library), OpenCV, and SciPy functions: np.arctan2(), np.sort(), np.reshape(), np.newaxis, np.argsort(), np.gradient(), np.histogram(), np.hypot(), np.fliplr(), np.flipud(), cv2.Sobel(), cv2.filter2D(), cv2.getGaussianKernel(), scipy.signal.convolve().

Forbidden functions (you can use for testing, but not in your final code): cv2.SIFT(), cv2.SURF(), cv2.BFMatcher(), cv2.BFMatcher().match(), cv2.FlannBasedMatcher().knnMatch(), cv2.BFMatcher().knnMatch(), cv2.HOGDescriptor(), cv2.cornerHarris(), cv2.FastFeatureDetector(), cv2.ORB(), skimage.feature, skimage.feature.hog(), skimage.feature.daisy, skimage.feature.corner_harris(), skimage.feature.corner_shi_tomasi(), skimage.feature.match_descriptors(), skimage.feature.ORB().

We haven't enumerated all possible forbidden functions here but using anyone else's code that performs interest point detection, feature computation, or feature matching for you is forbidden.

Tips, Tricks, and Common Problems


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 3 image pairs with ground truth correspondence, 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_circles.jpg or vis_lines.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 Bells & Whistles and want extra credit. You should clearly demonstrate how your additions changed the behavior on particular test cases.

Bells & Whistles

Implementation of bells and whistles can increase your grade by up to 10 points (potentially over 100). The max score for all students is 110.

For all extra credit, be sure to include quantitative analysis showing the impact of the particular method you've implemented. Each item is "up to" some amount of points because trivial implementations may not be worthy of full extra credit

Interest point detection bells and whistles:

Local feature description bells and whistles:

Local feature matching bells and whistles:

An 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:


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 impressive projects to highlight on the class website and in leture. If you do not want your results published to the web, you can choose to opt out. If you want to opt out, email the head TA.

Handing in

This is very important as you will lose points if you do not follow instructions. Every time you do not follow instructions, you will lose 5 points. The folder you hand in must contain the following:

Hand in your project as a zip file through Canvas.


Assignment developed by James Hays, Samarth Brahmbhatt, and John Lambert.