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Image alignment and registration with OpenCV Image alignment and registration is the process of: 1 . Taking two images of the same object from slightly
Image alignment and registration with OpenCV Image alignment and registration is the process of: Taking two images of the same object from slightly different angles. Automatically finding the transformation needed to align the images, using methods like feature matching, similarity measures. Applying this transformation to warp one image so it matches the other. How can OpenCV help with image alignment and registration? There are several methods for aligning and registering images: Image from: Benefits of dynamic susceptibilityweighted contrastenhanced perfusion MRI for glioma diagnosis and therapy The most common methods are featurebased. These include keypoint detectors like DoG, Harris local invariant descriptors like SIFT, SURF, and ORB and keypoint matching techniques like RANSAC In medical imaging, similarity measures such as crosscorrelation, sum of squared intensity differences, and mutual information are often used. We'll be using featurebased methods for our image alignment and registration, starting with detecting keypoints in the two images. Keypoints are meant to identify salient regions of an input image. For each keypoint, we extract local invariant descriptors, which quantify the region surrounding each keypoint in the input image. SIFT features, for example, are d so if we detected keypoints in a given input image, then well have a Input images: Use the given image as a first image. For the second one create a distorted version of the first image by resizing and rotating the image. Note that imrotate rotates images in a counterclockwise direction when you specify a positive angle of rotation. scaleFactor ; distorted imresizeoriginalscaleFactor; theta ; distorted imrotatedistortedtheta; imshowdistorted Here are the steps that you need to follow: Load image, convert to double and to grayscale. Create second image Detect feature points in both images. Use this Harris. Extract local neighborhoods around every keypoint in both images, and form descriptors using SIFT. You can use the OpenCV library to extract keypoints and compute descriptors through the function cvSIFTcreatedetect And Compute. This tutorial provides details about using SIFT in OpenCV. Compute distances between every descriptor in one image and every descriptor in the other image. In Python, you can use scipy.spatial.distance.cdistXY'sqeuclidean' for fast computation of Euclidean distance. Select putative matches based on the matrix of pairwise descriptor distances obtained above. You can select all pairs whose descriptor distances are below a specified threshold, or select the top few hundred descriptor pairs with the smallest pairwise distances. Implement RANSAC to estimate a homography mapping one image onto the other. Report the number of inliers and the average residual for the inliers squared distance between the point coordinates in one image and the transformed coordinates of the matching point in the other image A very simple RANSAC implementation is sufficient. Use four matches to initialize the homography in each iteration. You should output a single transformation that gets the most inliers during all the iterations. For the various RANSAC parameters number of iterations, inlier threshold play around with a few "reasonable" values and pick the ones that work best. Homography fitting, calls for homogeneous least squares. The solution to the homogeneous least squares system AX is obtained from the SVD of A by the singular vector corresponding to the smallest singular value. In Python, U s V numpy.linalg.svdA performs the singular value decomposition and VlenV gives the smallest singular value. Warp one image onto the other using the estimated transformation. In Python, useskimage.transform.ProjectiveTransform and skimage.transform.warp. Create a new image contain two images.
Image alignment and registration with OpenCV
Image alignment and registration is the process of:
Taking two images of the same object from slightly different angles.
Automatically finding the transformation needed to align the images, using methods like feature
matching, similarity measures.
Applying this transformation to warp one image so it matches the other.
How can OpenCV help with image alignment and registration?
There are several methods for aligning and registering images:
Image from: Benefits of dynamic susceptibilityweighted contrastenhanced perfusion MRI for glioma
diagnosis and therapy
The most common methods are featurebased. These include keypoint detectors like DoG,
Harris local invariant descriptors like SIFT, SURF, and ORB and keypoint matching
techniques like RANSAC
In medical imaging, similarity measures such as crosscorrelation, sum of squared intensity
differences, and mutual information are often used.
We'll be using featurebased methods for our image alignment and registration, starting with detecting
keypoints in the two images.
Keypoints are meant to identify salient regions of an input image. For each keypoint, we extract local
invariant descriptors, which quantify the region surrounding each keypoint in the input image. SIFT
features, for example, are d so if we detected keypoints in a given input image, then well have a
Input images:
Use the given image as a first image. For the second one create a distorted version of the first
image by resizing and rotating the image. Note that imrotate rotates images in a
counterclockwise direction when you specify a positive angle of rotation.
scaleFactor ;
distorted imresizeoriginalscaleFactor;
theta ;
distorted imrotatedistortedtheta;
imshowdistorted
Here are the steps that you need to follow:
Load image, convert to double and to grayscale.
Create second image
Detect feature points in both images. Use this Harris.
Extract local neighborhoods around every keypoint in both images, and form descriptors using
SIFT. You can use the OpenCV library to extract keypoints and compute descriptors through the
function cvSIFTcreatedetect And Compute. This tutorial provides details about using SIFT
in OpenCV.
Compute distances between every descriptor in one image and every descriptor in the other
image. In Python, you can use scipy.spatial.distance.cdistXY'sqeuclidean' for fast computation
of Euclidean distance.
Select putative matches based on the matrix of pairwise descriptor distances obtained above. You
can select all pairs whose descriptor distances are below a specified threshold, or select the top
few hundred descriptor pairs with the smallest pairwise distances.
Implement RANSAC to estimate a homography mapping one image onto the other. Report the
number of inliers and the average residual for the inliers squared distance between the point
coordinates in one image and the transformed coordinates of the matching point in the other
image
A very simple RANSAC implementation is sufficient. Use four matches to initialize the
homography in each iteration. You should output a single transformation that gets the most inliers
during all the iterations. For the various RANSAC parameters number of iterations, inlier
threshold play around with a few "reasonable" values and pick the ones that work best.
Homography fitting, calls for homogeneous least squares. The solution to the homogeneous least
squares system AX is obtained from the SVD of A by the singular vector corresponding to the
smallest singular value. In Python, U s V numpy.linalg.svdA performs the singular value
decomposition and VlenV gives the smallest singular value.
Warp one image onto the other using the estimated transformation. In Python,
useskimage.transform.ProjectiveTransform and skimage.transform.warp.
Create a new image contain two images.
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