# A Benchmark for the Evaluation of Corner Detectors
###### Abstract  
Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corners and unified metrics to evaluate their corner detection performance. In this paper, we build three benchmark datasets for corner detection. The first two consist of those binary and gray-value images that have been commonly used in previous corner detection studies. The third one contains a set of urban images, called the Urban-Corner dataset. For each test image in these three datasets, the ground-truth corners are manually labeled as objectively as possible with the assistance of a line segment detector. Then, a set of benchmark evaluation metrics is suggested, including five conventional ones: the precision, the recall, the arithmetic mean of precision and recall (APR), the F score, the localization error (Le), and a new one proposed in this work called the _repeatability referenced to ground truth_ (RGT). Finally, a comprehensive evaluation of current state-of-the-art corner detectors is conducted. corner detection; benchmark dataset; evaluation metric; objective evaluation; subjective evaluation +
Footnote †: journal: Medical Imaging  
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The corners are then obtained through curvature thresholding. Inspired by the curvature scale space corner detector proposed by [PERSON] et al. [18], many multi-scale corner detectors have been developed, such as the multi-scale curvature product (MSCP) proposed by [PERSON] et al. [19]. [PERSON] et al. [20] proposed the direct curvature scale space (DCSS) representation of planar curves and then developed a DCSS-based corner detector. [PERSON] et al. [21] proposed a corner detector by using the characteristics of global and local curvatures. [PERSON] et al. [22] used the chord-to-point distance accumulation (CPDA) to detect corners. [PERSON] et al. [23] proposed a novel corner detector which is based on the gradient correlation matrix. [PERSON] et al. [24] constructed a new corner detector based on the triangular theory and distance calculation (CTAR). [PERSON] et al. [25] proposed a corner detector which uses anisotropic directional derivative representations (ANDDs). [PERSON] et al. [26] proposed a corner detector based on the weighted eigenvector-based angle estimator (WEAE). [PERSON] et al. [27] proposed new discrete curvature representations of single and double corner models and then further developed a corner detector (New-Curvature). [PERSON] et al. [28] used the multi-scale \(k\)-cosine angle to detect corners (MSRJ). [PERSON] et al. [29] used the relative distance and a multi-scale technique to detect corners. [PERSON] et al. [30] introduced a corner detector (RPD) based on the ratio of parallelogram diagonals. [PERSON] et al. [31] proposed a simple and fast corner detection method based on a two-stage model for vision-based alignment tasks. [PERSON] et al. [32] developed a novel method based on the EDLines algorithm for the automatic detection of chessboard corners. [PERSON] et al. [33] proposed an efficient contour-based corner detector based on a dynamic measure of cornerity. [PERSON] et al. [34] developed a corner detection algorithm in curvature scale space. To conduct an objective evaluation of the detection results of a corner detector, existing methods can be grouped into the following two types. The first is evaluation with ground-truth corner points [23; 24; 24; 24; 24; 24; 24; 25; 25; 26; 27; 28; 29; 33; 35]. In this case, the used metrics include the precision, recall, F score and localization error. The rates of undetected and falsely detected corner points are also often used. The second type is evaluation without the ground-truth corner points, in which case the _repeatability_ or _consistency_[36] as well as the localization accuracy measured under various transformations are used [24; 24; 24; 25; 26; 27; 27; 28; 29; 30; 37].