In this paper a variant of the Harris corner point detector is introduced. The new algorithm use a covariance operator to compute the angular difference between dominant edges. Then, a new cornerness strength function is proposed by weighting the log Harris cornerness function by the angular difference between dominant edges. An important advantage of the proposed corner detector algorithm is its ability to reduce false corner responses in image regions where partial derivatives have similar values. In addition, we show qualitatively that ranking corner points with the new cornerness strength function better agrees with the intuitive notion of a corner than the original Harris function. To demonstrate the performance of the new algorithm, the new approach is applied on synthetic and real images. The results show that the proposed algorithm rank better the meaningful detected features and at the same time reduces false positive features detected when compared to the original Harris algorithm.
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