Corner detection is a major research area in computer vision. Many algorithms of corner detection have been proposed in recent years. The existing methods can be divided into intensity-

contour-

and template-based approaches. The detection performance of contour-based corner detection is relatively stable

but this approach is sensitive to local variation and noise on the curve. Many contour-based image corner detectors provide only an empirical threshold to extract the corner. Thus

this paper proposes a novel contour-based corner detection with adaptive threshold

and this algorithm has a robust performance in local variation and noise on the curve. The proposed method is based on anisotropic Gaussian directional derivative (ANDD) representations

and searches the invariant property of geometry and gray level in edges and corners. The adaptive threshold of the difference between the edge and corner points is obtained by the regularization calculation. This corner detection finds the edge map using the Canny edge detector and extracts edges from the edge map first. The ANDD filter is then used to filter the extracted edge curves

and the response of each pixel is calculated and compared with the adaptive threshold value. Then

the point value that is higher than the threshold value is chosen as the candidate corner. Finally

the non-maximum suppression is applied to the candidate corner set and the final corners are obtained. The proposed detector is compared with three different detectors under affine transforms and Gaussian noise degradation. The evaluation criteria of performance are average repeatability and localization error. In simulation experiments

the average rankings of four algorithms are as follows: Harris (3.375)

He and Yung (2.625)

ANDDs (2.625)

and the proposed method (1.375). The corner matching performance of the corner detection algorithms are compared under the noise-free and noisy environments. Experimental results show that the proposed method attains excellent performance on average repeatability and localization error under affine transforms and Gaussian noise degradation. The number of false and missed corners is less than that of the three other corner detectors in matching experiments. The proposed corner detection is a contour-based method with an adaptive threshold. Similar to most contour-based algorithms

the proposed method detects the edge map of input image using the edge detector first and then extracts the edges from the edge map. However

the proposed algorithm is different from the traditional contour-based corner detection using only the edge information. The proposed algorithm also utilizes intensity variations of pixels on edges. Furthermore

the new method uses a global adaptive threshold to avoid erroneous judgment of corners. The sum of normalized intensity variations effectively reduces the influence of noise or illumination on detection performance. As obtained from experiments of corner matching

affine transforms

and Gaussian noise degradation

the proposed method shows excellent performance in terms of detection accuracy

average repeatability

localization error

and noise robustness.