Weighted Multi-Scale Image Matching Based on Harris- Sift Descriptor

According to the rotational invariance of Harris corner detector and the robustness of Sift descriptor. An improved Harris-Sift corner descriptor was proposed. At first, the algorithm given multi-scale strategy to Harris corner, improved corner counting method and removed redundant points at the same time, then, the corner was directly applied to low-pass Gaussian filter image. Based on the histogram of Sift feature descriptor, generates a new 128-dimensional feature vector descriptor by multi-scale Gauss weighted. Through the above, Harris corner detector and Sift descriptor was normalized in the scale layer and gradient features. The experiment results indicated that, the improved corner descriptor comprised both advantage of Harris corner detection and Sift feature descriptor. The method reduced the computation time and the false match rate, which could be validly applied to the robot stereo vision matching and three-dimensional reconstruction.