On Fast Matching Algorithm Research for big image

The matching algorithms based on features have more extensive adaptive range and better performance, because of more complexity, the ones are hard to meet real time, especially for big images, the disadvantage of non-real time further limits the application of this kind of algorithms in engineering practice. Aiming at this problem, a new fast matching algorithm for big image is proposed. The ways of spatial-domain segmentation and frequency-domain compression are applied to process images primarily. A region that contains abundant high-frequency information is extracted as sub-image of matching, then the size of target image is cut down. A high scale image of source image in scale space is extracted via wavelet transform, then the range of SIFT feature points existing in frequency domain is compressed. Algorithm adopts a coarse to fine matching process, after coarsely locating the position of sub-image, another operation of refined-match is applied, finally the fast matching for big image is completed. Simulation results testify that proposed algorithm cut down matching time greatly, after some modules are implemented on hardware system, the algorithm even meet the requirement of real time.