# A Benchmark for the Evaluation of Corner Detectors
## 2 Benchmark Construction
### Benchmark Datasets  
In this work, three benchmark datasets for corner detection are constructed, as shown in Figure 1. The first two consist of those binary and gray-value images that have been commonly used in previous corner detection studies [4; 38; 39; 40]. The third one, which is new, was developed by us and is called Urban-Corner. These datasets will be used to perform an extensive evaluation of 18 state-of-the-art corner detectors. In what follows, it is described in detail how the third dataset (i.e., Urban-Corner) is established. The Urban-Corner dataset contains 21 images, (These datasets with ground-truth corners have been made available online at [[https://github.com/yangzhangcv](https://github.com/yangzhangcv)]([https://github.com/yangzhangcv](https://github.com/yangzhangcv)) (accessed on 3 January 2022), and more images will be added gradually.) and the size of each image is 256 \(\times\) 256 pixels. These images were selected from the Urban100 dataset [41], which was chosen as the source of our Urban-Corner dataset due to the following two reasons. First, the Urban100 dataset has been widely used in various image processing tasks. Due to the fact that the images in this dataset all have a high resolution, the ground-truth corners can be labeled with high precision. Secondly, each image contains urban street views with clear corners. Because our human visual system is sensitive to the sharp corners of objects, we selected images which had uncontested corners for human visual perception, excluding those with vague corners that were hard to identify. The Urban-Corner dataset can be roughly divided into two categories according to the presented content: the surface structures of buildings and images that only contain outlines of buildings. When selecting images and labeling the ground truths, the details for the protocol and conditions are as follows:  
(1) Our rule was to select the test images from Urban100. In the literature, test images for evaluating corner detection performance frequently contained a lot of 'ambiguous' corners, such as 'corners' on clothes, hair, and other non-rigid objects. Even for our human visual system, such 'corners' are hard to recognize, and different people would have different judgments on whether they are true (i.e., meaningful) corners or not. Therefore, in our work, only an image containing more well-defined corners and less 'ambiguous' ones could be selected and included in our Urban-Corner dataset. This is in line with real-world applications, where only those well-defined corners can be exploited to solve the follow-up image processing tasks, such as scene analysis and 3D reconstruction. This 'rule of thumb' greatly helped us to reduce the difficulty and risk of errors in the corner labeling process. (2) For our method of labeling the ground-truth corners in a test image, it is found that confusing detection could be yielded for geometric attributes at the pixel scale (invisible to the eye and of no interest at the human observation scale), as well as the perceptual detection of such a stimulus (corner). To overcome this difficulty, our corner labeling process was performed with the help of the edge information of the input image by using an edge detector or the detected line segment information by using a line segment detector. First, the line segments were detected by using a line segment detector (LSD) [42]. Then, insignificant or redundant line segments were removed by us. After that, the initial corners were produced from the intersections of those significant line segments. As a result, the accuracy of the corner labeling was greatly improved. (3) In our method to confirm the corner labeling results, in order to finally confirm them, all the other members (14 persons in total) in our research group were recruited as volunteers to vote on whether a labeled corner could represent a ground-truth corner or not.