A highly repeatable feature detector: improved Harris---Laplace

Authors:
Jieyu Zhang;Qiang Chen;Quansen Sun;Huaijiang Sun;Deshen Xia
Affiliations:
Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China 210094;Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China 210094;Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China 210094;Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China 210094;Department of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China 210094
Venue:
Multimedia Tools and Applications
Year:
2011

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Abstract

A feature detector named improved Harris---Laplace is proposed to obtain higher repeatability than that of original Harris---Laplace. In this novel method, all points detected at each scale are tracked and grouped beginning with the largest scale in the scale-space to make each group represent one local structure firstly. Then the point in each group which simultaneously leads to the maxima of corner points measuring and scale normalization Laplace function is selected. Finally, these points are described and matched by scale invariant feature transform (SIFT) descriptor successfully. Experimental results indicate that the proposed method has higher repeatability than original Harris---Laplace. Meanwhile, the new method was evaluated with image registration. Compared with SIFT, more accurate registration precision of multi-sensor remote sensing images was obtained by the advanced method.