Project 2, Part 1: Feature Detection and Matching

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In this component of Project 2, you will write code to detect discriminating features in an image and find the best matching features in other images.  Because features should be reasonably invariant to translation, rotation (plus illumination and scale if you do the extra credit), you'll use a feature descriptor discussed during lecture and you'll evaluate its performance on a suite of benchmark images.  As part of the extra credit, you'll have the option of creating your own feature descriptors.  If there are enough student-created features, we'll rank the performance of features that students in the class came up with, and compare them with the current state-of-the-art.

For the second part of the project, you will apply your features to automatically stitch images into a panorama (see Panorama Mosaic Stitching page). 

To help you visualize the results and debug your program, we provide a working user interface that displays detected features and best matches in other images.  We also provide sample feature files that were generated using SIFT, the current state-of-the-art technique in the vision community, for comparison.

Description

This component has three parts: feature detection, feature description, and feature matching.

Feature detection

In this step, you will identify points of interest in the image using the Harris corner detection method.  The steps are as follows (see the lecture slides/readings for more details). 

For each point in the image, consider a window of pixels around that point.  Compute the Harris matrix H for that point, defined as

 
where the summation is over all pixels p in the window.  The weights    should be chosen to be circularly symmetric (for rotation invariance).  A common choice is to use a 3x3 or 5x5 Gaussian mask.  Note that these weights were not discussed in the lecture slides, but you should use them for your computation.

Note that H is a 2x2 matrix.  To find interest points, first compute the “corner strength” function (a modified form of the "Harris operator"):

Once you've computed c for every point in the image, choose points where c is above a threshold.  You also want c to be a local maximum in at least a 3x3 neighborhood.

Feature description

Now that you've identified points of interest, the next step is to come up with a descriptor for the feature centered at each interest point.  This descriptor will be the representation you'll use to compare features in different images to see if they match.  You'll create the descriptor using the following steps in the warpComputeFeatures function in the code skeleton (also see class notes):

1.  Prefilter the image using the 7x7 Gaussian filter.   You may find the ImageLib "Convolve" function (in Convolve.h) useful here.

2.  Determine the homography (rotation, translation and scale) required to warp an 8x8 image to a rotated 40x40 window centered at the feature.  

3.  Use the WarpGlobal function to acquire the values of the 8x8 downsampled image.

4.  Update the feature descriptor with this data.

void WarpGlobal(CImageOf<T> src, CImageOf<T>& dst, CTransform3x3 M, WarpInterpolationMode interp);

WarpGlobal performs an inverse warp of the source image into the destination image.  In other words, for every pixel of the destination image, the pixel in source image is computed using the specified transformation (and interpolated, if required).  The transformation is specified by a 3x3 homography matrix that can represent rigid, affine, or perspective transformations.

Feature matching

Now that you've detected and described your features, the next step is to write code to match them, i.e., given a feature in one image, find the best matching feature in one or more other images.  This part of the feature detection and matching component is mainly designed to help you test out your feature descriptor.  You will implement a more sophisticated feature matching mechanism in the second component when you do the actual image alignment for the panorama.

The simplest approach is the following:  write a procedure that compares two features and outputs a distance between them.  For example, you could simply compute the sum of squared differences (SSD) between the descriptor elements. You could then use this distance to determine the best match between a feature in one image and the set of features in another image by finding the one with the smallest distance. Two possible strategies are:

1.  Use a threshold on the match score. This SSD distance is implemented for you as match type 1, in the function sshMatchFeatures.
2.  Compute (score of the best feature match)/(score of the second best feature match). This is called the "ratio test"; you must implement this distance.

Testing

Now you're ready to go!  Using the UI and skeleton code that we provide, you can load in a set of images, view the detected features, and visualize the feature matches that your algorithm computes.

We are providing a set of benchmark images to be used to test the performance of your algorithm as a function of different types of controlled variation (i.e., rotation, scale, illumination, perspective, blurring).  For each of these images, we know the correct transformation and can therefore measure the accuracy of each of your feature matches.  This is done using a routine that we supply in the skeleton code.

You should also test the matching against the images you will take for your panorama (described in next component).

Skeleton Code

Follow these steps to get started quickly:

1.      Download the skeleton code here. For Mac users, you can download the unofficial fixes provided by Johnathan Lyon for both Features and Panorama here.

2.      Download some image sets: graf, Yosemite.
Included with these images are some SIFT feature files and image database files.

  1. Download the sample solution Features.exe file here. If you use machines at Seig 327 to program , you may download the sample solution Features.exe file here.

After compiling and linking the skeleton code, you will have an executable Features. This can be run in several ways:

·         Features
with no command line options starts the GUI. Inside the GUI, you can load a query image and its corresponding feature file, as well as an image database file, and search the database for the image which best matches the query features. You can use the mouse buttons to select a subset of the features to use in the query.

Until you write your feature matching routine, the features are matched by minimizing the SSD distance between feature vectors.

·         Features computeFeatures imagefile featurefile [featuretype]
uses your feature detection routine to compute the features for imagefile, and writes them to featurefile. featuretype specifies which of your types of features (if you choose to implement another feature for extra credit) to compute.

·         Features matchFeatures featurefile1 featurefile2 threshold matchfile [matchtype]
takes in two sets of features, featurefile1 and featurefile2 and matches them using your matching routine (the matching routine to use is selected by [matchtype]; 1 (SSD) is the default). The threshold for determining which matches to keep is given by threshold. The results are written to a file, matchfile, which can later be read by the Panorama program.

·         Features matchSIFTFeatures featurefile1 featurefile2 threshold matchfile [matchtype]
same as above, but uses SIFT features.

To Do

We have given you a number of classes and methods to help get you started. The only code you need to write is for your feature detection methods and your feature matching methods, all in features.cpp. Then, you should modify computeFeatures and matchFeatures in the file features.cpp to call the methods you have written.  We have provided a function dummyComputeFeatures that shows how to create the code to detect and describe features,as well as integrate it into the system. The function ssdMatchFeatures implements a feature matcher which uses the SSD distance, and demonstrates how a matching function should be implemented.  The function warpComputeFeatures is the main function you will complete, along with the helper functions computeHarrisValues and computeLocalMaxima.  You will also implement the function ratioMatchFeatures for matching features using the ratio test.

You will additionally need to generate plots of the ROC curves for your feature detecting and matching code (using the 'roc' option of Features.exe), and for SIFT. For both the Yosemite test images (Yosemite1.jpg and Yosemite2.jpg), and the graf test images (img1.ppm and img2.ppm), create a plot with four curves, one using your features with the SSD distance, one using your features with the ratio test distance, and the other two using SIFT (with both the SSD and ratio test distances; these curves are provided to you in the zip files for Yosemite and graf provided above).

We have provided scripts for creating these plots using the 'gnuplot' tool. Gnuplot is installed on the lab machines, at 'C:\Program Files\gnuplot\binaries\wgnuplot.exe' ('gnuplot' is also available on 'attu', the CSE instructional machine). To generate a plot with gnuplot (using a gnuplot script 'script.txt', simply run 'C:\Program Files\gnuplot\binaries\wgnuplot.exe script.txt', and gnuplot will output an image containing the plot. The two scripts we provide are:

plot.roc.txt: plots the ROC curves for the SSD distance and the ratio test distance. These assume the two roc datafiles are called 'roc1.txt' (for the SSD distance), and 'roc2.txt' (for the ratio test distance). You will need to edit this script if your files are named differently. This script also assumes 'roc1.sift.txt' and 'roc2.sift.txt' are in the current directory (these files are provided in the zip files above). This script generates an image named 'plot.roc.png'. Again, to generate a plot with this script, simply enter 'C:\Program Files\gnuplot\binaries\wgnuplot.exe plot.roc.txt'.

plot.threshold.txt: plots the threshold on the x-axis and '(TP rate) minus (FP rate)' on the y-axis. The maximum of this function represents a point where the true positive rate is large relative to the false positive rate, and could be a good threshold to pick for the computeMatches step. This script generates an image named 'plot.threshold.png.'

Extra Credit

Here is a list of suggestions for extending the program for extra credit. You are encouraged to come up with your own extensions as well!

Features benchmark imagedir [featuretype matchtype]
Tests your feature finding and matching for all of the images in one of the four above sets. imagedir is the directory containing the image (and homography) files. This command will return the average pixel error when matching the first image in the set with each of the other five images.