Matching the template image in the target image is the fundamental task in the field of computer vision. Aiming at the
deficiency in the traditional image matching methods and inaccurate matching in scene image with rotation, illumination
and view changing, a novel matching algorithm using local features are proposed in this paper. The local histograms of
the edge pixels (LHoE) are extracted as the invariable feature to resist view and brightness changing. The merits of the
LHoE is that the edge points have been little affected with view changing, and the LHoE can resist not only illumination
variance but also the polution of noise. For the process of matching are excuded only on the edge points, the computation
burden are highly reduced. Additionally, our approach is conceptually simple, easy to implement and do not need the
training phase. The view changing can be considered as the combination of rotation, illumination and shear
transformation. Experimental results on simulated and real data demonstrated that the proposed approach is superior to
NCC(Normalized cross-correlation) and Histogram-based methods with view changing.
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