Efficient binary descriptor-based implementation of fuzzy image registration algorithms on LabVIEW

Abstract

Image registration algorithm is the process of establishing a mapping between image in a sequence and another overlapping one. Sufficient overlap ratio in the frames of the sequence is a necessary condition to find that mapping. Image mosaic is one of the important applications for which image registration may be used. Also, object tracking is another interesting image processing task that deals with finding mapping relation between successive images. Local features such as points are generally used to simplify images processing through taking significant pixels in the image and applying some operations for getting information about the whole image. Several techniques were proposed for finding corresponding features between images; among which descriptors-based matching is the most efficient approach. The suggested floating-points descriptors such as scale invariant feature transform and speed up robust feature are generally good in term of performance but require a long time; in most cases, they imply refinement methods for removing false matches, thus increasing the time cost. Therefore, we propose a fuzzy matching method that combines two binary descriptors, which are local binary patterns and Fast REtinA key point (FREAK) for image registration purpose. Our algorithms are tested on Matlab for different real scenes and satisfying results were obtained. To make program execution simple and to easily do numerical comparisons, a graphical interface has been implemented on LabVIEW platform.