Awesome

ImageCoregistration

A simple Image regestring method based on SIFT descriptors and RANSAC algorithm for filtering out the outliers.

Requirements

• Python3
• numpy
• opencv
• sklearn

Setup

git clone https://github.com/ily-R/ImageCoregistration.git
cd ImageCoregistration

Usage

Very easy to use. Put the images you want to register, the target and the source(say "img1.jpg" and "img2.jpg") in data\ folder and run the follwing.

python image_register.py img1.jpg img2.jpg --sift

Arguments

This is the full set of system arguments supported.

python image_register.py img1.jpg img2.jpg -s 0.5 --sift -r

• img1.jpg img2.jpg are positional arguments representing the names of the target and source image.
• --scale 0.5 or abbreviation -s 0.5 is the scale by which we resize the input images. default = 0.1.
• --sift a boolean flag. If True use Harris detectors then sift descriptor. If False the images will be displayed and the user has to click on both images to selected the prefered landmarks to be passed to SIFT.default = False.
• --rasnac or abbreviation -r a boolean flag. If True use RANSAC algorithm to select only the inliers for the affine matrix estimation step. Useful when the images have a lot of similar pattern in different positions in the image. default = False.

Notes

• result\ folder contains the source and target images alligned. Additionally, if True flags are passed in inner functions, the folder will contain other results.
  • if save = True in display_matches(target, source, lmk1, lmk2, name="matches, save=False") it will save both images horizontally stacked with landmarks matched.
  • if save = True in display_matches(target, source, outliers1, outliers2, name="matches_removed_by_RANSAC", num=5, save=True) it will save and image that showes the landmarks rejected by RASNAC.
  • if save = True in mi = mutual_inf(warped, target_w, verbose=True) it will save the joint-histogram of warped-source image and the target image.

Testing

Original images:

We can see that there a slight translation upward with probably small rotation to the left.

Floating the source image:

Applying the registation on the source image, we see how it floats from its original position to another upward. This is done to assure that most pixels are alligned to the target ones.

Result:

Floating the source image we see how perfectly are the images aligned. To not get distracted by the upper border moving, put the tip of the mouse on any position and verify that the mouse tip stands steel. If you do this in the above gif, the tip will change positions.

Features:

The tested image showed a perfect match of features as see below. (showing only 20 out of around 200)

This is due to the use of sift_error =0.7 which filter out outliers. Read the doc of match(lmk1, lmk2, desc1, desc2, sift_error=0.7) for more details. Using RANSAC on top of this result will not change the landmarks selection. To force outliers, we let sift_error =0.9. Now after using RANSAC, here's the outliers filtered.

Metrics

Two metrics are used plus the reconstruction error:

• Cross-correlation: for the above registration cc = 0.86086107 almost 1, which is the case when the two images are perfectly alligned. This indicates good registation.
• Multi information: it gives a probabilistic measure on how uncertain we are about the target image in the absence/presence of the warped source image. Below is the joint_histogram of both images. We get this figure because we are working in mono-modality registration. Means that the images contain almost the same range of colors with some noise.

More Testing

Here are some screen shot taken from Google maps satellite above the Eiffel tower

We confirm also what we said about the joint-histogram in this case, as seen below.

Applying the registration to float the source to the target image we get the following:

You can see how the Eiffel tower is moving, which indicates the two images present in the gif come from different perspectives. Which in our case the source and target images.

Future work:

• Add more robust feature for noisy SAR images like SAR_SIFT

• Add the spatial information to intensity information in our descriptors to limit the outliers in difficult tasks. Like what Johan et al. did Here