Coming soon!

Coming soon!

A lightweight OpenCV based software system written in C/C++ to register a sequence of aerial images in near-realtime. I have tried to avoid C++ code in **most** of the files. The image registration method used here uses Shi-Tomasi's good features to track as sparse feature points in source image frame and then uses Lucas-kanade's pyramid optical flow to compute local optical flow in a neighborhood of these feature points in the subsequent destination frame. This method is also known as Kanade-Lucas-Tomasi algorithm. A cummulative homography ensemble is being output after the end of execution of FRAISE with each entry describing the projective homography between the 1st frame of the sequence to the subsequent frames. On a dual core machine, the alignment using this method has a processing speed of upto 10frames/sec. A multi-threaded version is in progress which would be even faster.

I **cannot** include the source code for this particular software because of intellectural property rights imposed by my University. However, the Linux compatible binaries of FRAISE could be downloaded from here. These include precompiled OpenCV shared libraries. A demo test video video sequence and an image sequence with corresponding FRAISE alignment is here.

A small benchmark to test the efficacy and performance of 3 feature based methods for image alignment. The first method uses SIFT features for homography estimation which I have obtained from Rob Hess' website and tweaked to suit my purpose. The next method estimates homographies between two input images (both of eaual sizes) using feature points computed using Speeded Up Robust Features. Finally I show how the sparse optical flow based method used in FRAISE compares with these two feature based techniques.