A Fast Fourier based Feature Descriptor and a Cascade Nearest Neighbour Search with an Efficient Matching Pipeline for Mosaicing of Microscopy Images

Abstract

Automatic mosaicing is an important image processing application and we propose several improvements and simplifications to the image registration pipeline used in microscopy to automatically construct large images of whole specimen samples from a series of images. First of all we propose a feature descriptor based on the amplitude of a few elements of the Fourier transform, which makes it fast to compute and that can be used for any image matching and registration applications where scale and rotation invariance is not needed. Secondly, we propose a cascade matching approach that will reduce the time for the nearest neighbour search considerably, making it almost independent on feature vector length. Moreover, several improvements are proposed that will speed up the whole matching process. These are: faster interest point detection, a regular sampling strategy and a deterministic false positive removal procedure that finds the transformation. All steps of the improved pipeline are explained and the results comparative experiments are presented.