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Abstract There is no parametric formulation of corner feature. Therefore, the conventional Hough transform can not be employed to transform the corner detection into maximum search in parametric space. A randomized Hough transform in Monte Carlo framework is presented, which detects the corner by searching for the local maximum in the intersection point cumulative space instead of parametric space. The proposed intersection point cumulative space is a concept based on the fact that the corner is the intersection point of two lines. The algorithm is demonstrated and the computing procedures are given. The algorithm is isotropic, robust to image rotation, insensitive to noise and not susceptible to diagonal edge. Experimental results show that the proposed algorithm outperforms Harris detector, Shen & Wang algorithm, and SIFT feature detection algorithm.
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