Image stitching using homographies
In this assignment, I performed stiched of two images by first finding correspondencies between the two images and estimating the planar homography transformation matrix between the two images.
In this assignment, I implemented an interest point (keypoint) detector, a feature descriptor and an image stitching tool based on feature matching and homography.
Interest point (keypoint) detectors find particularly salient points in an image. I then extracted a feature descriptor that helped describe the region around each of the interest points. I implemented the BRIEF feature descriptor in this homework. BRIEF has a compact representation, is quick to compute, has a discriminative yet easily computed distance metric, and is relatively simple to implement. This allows for real-time computation and it is also just as powerful as more complex descriptors like SIFT for many cases.
After matching the features that were extracted, I estimated the relationship between the captured images using a 3 × 3 transformation matrix, called a planar homography. A planar homography allows us to compute how a planar scene would look from a second camera location, given only the first camera image. This allowed me to warp the second image to perspective of the first image. This was followed by stiching of the images to get a beautiful panorama of Pittsburgh
