One common way of doing this is to identify "interest points" or "key points" in both images, summarize their appearances using descriptors, and then establish matches between these "features" (interest points combined with their descriptors) by choosing features with similar descriptors from each image. To establish a homography between two images, you'll first need to find a set of correspondences between them. Find a homography between two images (40 points + up to 20 bonus points)Ī homography is a 2D projective transformation, represented by a 3x3 matrix, that maps points in one image frame to another, assuming both images are captured with an ideal pinhole camera. While I encourage you to make use of OpenCV's powerful libraries, for this project you must not use any of the functions in the stitcher package (although you're welcome to read its documentation and code for inspiration). composite several images in a common frame into a panorama.use the homography to warp images into a common target frame, resizing and cropping as necessary.use the corresponding points to fit a homography (2D projective transform) that maps one image into the space of the other.locate corresponding points between a pair of images.Your image stitcher will, at a minimum, do the following: A panorama is a composite image that has a wider field of view than a single image, and can combine images taken at different times for interesting effects. In this project, you'll write software that stitches multiple images of a scene together into a panorama automatically. The output limits are then used to automatically find the image that is roughly in the center of the scene.Project 1: Panorama stitching Due: 23 Sept 2014, 11:59pm Start by using the projtform2d outputLimits method to find the output limits for each transformation. This is accomplished by inverting the transformation for the center image and applying that transformation to all the others. A nicer panorama can be created by modifying the transformations such that the center of the scene is the least distorted. However, using the first image as the start of the panorama does not produce the most aesthetically pleasing panorama because it tends to distort most of the images that form the panorama. This was a convenient way to code the image registration procedure because it allowed sequential processing of all the images. Tforms(n).A = tforms(n-1).A * tforms(n).A Īt this point, all the transformations in tforms are relative to the first image. Tforms(n) = estgeotform2d(matchedPoints, matchedPointsPrev. % Estimate the transformation between I(n) and I(n-1). MatchedPointsPrev = pointsPrevious(indexPairs(:,2), :) MatchedPoints = points(indexPairs(:,1), :) IndexPairs = matchFeatures(features, featuresPrevious, 'Unique', true) % Find correspondences between I(n) and I(n-1). % Detect and extract SURF features for I(n). % Iterate over remaining image pairs for n = 2:numImages % Initialize variable to hold image sizes. For scenes captured from a further distance, you can use % affine transformations. Note that the % projective transformation is used here because the building images are fairly % close to the camera. % Initialize all the transformations to the identity matrix. % Read the first image from the image set. You can use the Camera Calibrator App to calibrate a camera if needed. However, if lens distortion is present, the camera should be calibrated and the images undistorted prior to creating the panorama. These were taken with an uncalibrated smart phone camera by sweeping the camera from left to right along the horizon, capturing all parts of the building.Īs seen below, the images are relatively unaffected by any lens distortion so camera calibration was not required. The image set used in this example contains pictures of a building. Instead of registering a single pair of images, multiple image pairs are successively registered relative to each other to form a panorama. The procedure for image stitching is an extension of feature based image registration. In this example, feature based techniques are used to automatically stitch together a set of images. Feature detection and matching are powerful techniques used in many computer vision applications such as image registration, tracking, and object detection.
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