Robust novel feature extraction and matching algorithms

Now existing image feature matching algorithms are always high complexity and long time-consuming. A novel feature matching algorithm was proposed based on local feature points. Scale pyramid should be constructed first in which FAST key points were detected and extracted according to their Harris response. Then directions were distributed for key points using a method of intensity centroid. Finally, key point vectors were built via a sampling pattern. The hamming distance between the key point vectors in different images decided whether the two of them were matched or not. Experiments show that this algorithm is robust and reliable even under the condition of a certain degree of scaling, rotation and the effects of noise. Moreover, this algorithm is several times faster than SIFT while performing as well as SIFT in other aspects.