Abstract: A point matching algorithm based on edges of key points region was proposed to resolve the problem of IR and visible images matching．Firstly,the feature points were extracted by the CSS corner detector．Edges of key points region were reconstructed．Secondly,the normal direction of each feature point on the curve was adopted as the main direction of the point,making the point descriptor rotation invariant．Thirdly,through calculating the B-LBP weight histogram in interesting points’ neighborhood,the nearest feature point of each extracted one on the same edge was searched and the histograms of edge pixels of the two key points region were constructed．Then a 512-dimentional UB-LBP joint descriptor combining with two histograms was constructed and normalized．Finally,the feature matching was realized via the nearest neighbor algorithm．Experimental results show that the proposed algorithm can match the feature points in the IR and visible images more efficiently than the original SIFT.

Key words: Infrared image,Visual image,CSS corner detecting,Local binary patterns