Many local features

such as corner and inflection points

exist in images. The corner is the basic feature of an image

and it is always defined as a point where at least two edges intersect

a point having the maxima curvature

or a point around which a significant change in intensity occurs in all directions. Many corner detectors are available

and existing approaches can be broadly classified into edge-based

model-based

and gray-based methods. Corner detectors have their respective advantages and disadvantages. Model-based corner detectors detect corner points by matching image patches to the predefined templates. However

predefined templates cannot easily cover the full corner in natural images. Gray-based corner detection algorithms measure the local intensity variation of an image to find corners. These methods are sensitive to local variation and not robust to noise. Edge-based methods extract the edges from the input image and analyze the edge's shape to find corners. These approaches cannot fully extract image information. Corner detection is a challenging task in image processing and computer vision systems

such as object recognition

object tracking

simultaneous localization and mapping (SLAM)

and pattern matching. Therefore

the performance of the corner detector is important. To improve the detection performance of the corner detector

this paper presents a new corner detector that combines the edge contour and gray information of the image and utilizes the consistency of the edge pixels with the log-Gabor gradient direction. According to the definition of “corner

” we know that intensities around a corner change extremely in every direction. The gradient direction of a corner with adjacent pixels differs significantly. However

the gradient direction of adjacent edge pixels is the same and perpendicular to the ridge of the edge. This study uses this characteristic to construct a new corner measure. The proposed algorithm employs the Canny edge detector to detect and extract the edge map of an input image. Then

the imaginary parts of log-Gabor filters are used to smooth the edge pixels along multi-directions

and the corresponding gradient directions of pixels are determined. The obtained gradient directions are used to construct the new corner measure. Afterward

both the corner measure and angle threshold are used to remove false and weak corners. The proposed detector is compared with three corner detectors on planar curves and under affine transforms. We also evaluate the performance under Gaussian noise degradation. To evaluate the performance of four detectors on planar curves

two published test image shapes of different sizes are selected. Ten different common images from standard databases are also used to evaluate the performance under affine transforms and Gaussian noise degradation. Average repeatability and localization error are the two evaluation criteria. Average repeatability measures the average number of repeated corner points between affine-transformed and original images. The localization error measures the localization deviation of the repeated corner. In the simulation experiments

the average rankings of the four approaches are as follows:CPDA is 2.00

Harris is 3.33

He and Yung is 2.83

and proposed method is 1.67. Experimental results show that the proposed method presents excellent performance in terms of average repeatability and localization error under affine transform and Gaussian noise degradation. The number of false and missed corners in published test images is less than that of the three other corner detectors in the experiments. High computation complexity is the shortcoming of the proposed method. The edge-based corner detection algorithm mostly depends only on the edge shape of the image without considering the change in image gray. The gray-based corner detection algorithm only considers the gray information of the image. The proposed method considers the image edge shape and the gray changes. The imaginary parts of log-Gabor filters are used to smooth the edge pixels along multi-directions. Meanwhile

the consistency of the gradient directions of the edge pixels is used to construct the corner measure. Experimental results show that the proposed algorithm has good stability and corner detection performance. To address the high computation complexity of the proposed method

hardware measures

such as an embedded processor and FPGA controller

should be used to improve the problem of real-time processing. In the future

the optimized algorithm should be considered. Meanwhile

the proposed method can be applied to image matching.