Journal of Advanced Research in Applied Sciences and Engineering Technology 29, Issue 1 (2022) 256-265 256 Journal of Advanced Research in Applied Sciences and Engineering Technology Journal homepage: https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index ISSN: 2462-1943 Confusion Matrix as Performance Measure for Corner Detectors Nurul Ehsan Ramli 1,* , Zainor Ridzuan Yahya 2 , Nor Azinee Said 3 1 Jabatan Matematik Sains & Komputer, Politeknik Muadzam Shah Pahang, Lebuhraya Tun Abdul Razak, 26700 Muadzam Shah, pahang, Malaysaia 2 Institute of Engineering Mathematics, Faculty of Applied and Human Science, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, Malaysia 3 Faculty of Engineering Technology, University College TATI, 24000 Kemaman, Terengganu, Malaysia ARTICLE INFO ABSTRACT Article history: Received 17 October 2022 Received in revised form 22 December 2022 Accepted 23 December 2022 Available online 31 December 2022 Nowadays, corner detection algorithms have been proposed by several researchers who described them contrarily, depending on their respective viewpoints to obtain the data and information as a human eye does. Basically, no researchers have come up with a technique to compare corner detectors with another’s. Thus, this study proposed to adapt the confusion matrix technique as a performance measure for corner detectors. The judgement accuracy of every corner detector will only be pleased if the actual corner points are already known. Therefore, this study is attracted to explore the accuracy of corner detectors, namely the Global and Local Curvature Scale space (GLCSS), Affine Resilient Curvature Scale Space (ARCSS), and Harris. These corner detectors were analysed using the nine characters selected from Jawi, Chinese, and Tamil characters, three characters each, respectively. This study specifically detected the true corners for these characters using the determined corner detectors. The actual corner of all these characters was confirmed through a survey of twenty respondents. The majority of marked corners by respondents were considered actual corner points. Then, the input image for all characters was converted into a grayscale image. Every image will undergo pre-processing step, the process of boundary extraction using Canny edge detector. Thus, the edge image was extracted to get the corner point by applying the corner detectors, and the corner point detected was marked on that image. Above and beyond, the study aims to introduce a confusion matrix approach as a performance measure to carry out the most outstanding algorithm in detecting the true corner points for all the tested characters. From the evaluation, GLCSS and Harris algorithms have shown good accuracy. Henceforth, the study is not trying to judge the goodness of each corner detector but only to introduce confusion matrix as a tool that can be considered to measure the performance of the corner detector. Keywords: Confusion Matrix, Corner Detection Algorithm, Performance Evaluation, Image Processing * Corresponding author. E-mail address: lurunnashe@gmail.com https://doi.org/10.37934/araset.29.1.256265