A New Robust Image Feature Point Detector

摘要

A scale space-variant filter (SVF) is proposed on the basis of Harris arithmetic operators, which can smoothly isolate noiseefficiently at the situation of keeping edge information of the image. Comparing SVF with Gaussian filter under step jump signal andinitial image input, the result indicates that SVF is better than Gaussian filter. Using SVF to detect feature points of an image, theexperiment shows that feature points detected from SVF output contain more edge information. Using 2D space limitations, Euclidiandistance limitation and angle limitation, we can eliminate redundant feature points so that all the useful feature points are distributed in allregions of the image evenly. From the result of the examination for noise-contained image, we can draw the conclusions that the newrobust feature point detector can get more accurate position of feature points and the distribution of the points is more rational than that ofthe points without those limitations.