Version 1
: Received: 24 June 2019 / Approved: 25 June 2019 / Online: 25 June 2019 (08:27:29 CEST)
How to cite:
Al-Rawabdeh, A.; Almagbile, A.; khawaldeh, A.; Aldayafleh, O.; Zeitoun, M.; Hazaymeh, K. Evaluating the Performance of Corner Detection Approaches for Features Extraction from UAV Images. Preprints2019, 2019060245. https://doi.org/10.20944/preprints201906.0245.v1
Al-Rawabdeh, A.; Almagbile, A.; khawaldeh, A.; Aldayafleh, O.; Zeitoun, M.; Hazaymeh, K. Evaluating the Performance of Corner Detection Approaches for Features Extraction from UAV Images. Preprints 2019, 2019060245. https://doi.org/10.20944/preprints201906.0245.v1
Al-Rawabdeh, A.; Almagbile, A.; khawaldeh, A.; Aldayafleh, O.; Zeitoun, M.; Hazaymeh, K. Evaluating the Performance of Corner Detection Approaches for Features Extraction from UAV Images. Preprints2019, 2019060245. https://doi.org/10.20944/preprints201906.0245.v1
APA Style
Al-Rawabdeh, A., Almagbile, A., khawaldeh, A., Aldayafleh, O., Zeitoun, M., & Hazaymeh, K. (2019). Evaluating the Performance of Corner Detection Approaches for Features Extraction from UAV Images. Preprints. https://doi.org/10.20944/preprints201906.0245.v1
Chicago/Turabian Style
Al-Rawabdeh, A., Mohammad Zeitoun and Khaled Hazaymeh. 2019 "Evaluating the Performance of Corner Detection Approaches for Features Extraction from UAV Images" Preprints. https://doi.org/10.20944/preprints201906.0245.v1
Abstract
Many corner detector techniques have already been used in extracting information from UAV images to perform various photogrammetric and mapping activities. Among these techniques is the Feature from Accelerated Segment Test (FAST) and the Harris corner detector. It is widely agreed that the evaluation of detectors is of great importance because it evaluates and enhances the accuracy of the detected features. This research evaluates the performance of FAST-9 and FAST-12 as well as the Harris detector in terms of the repeatability rate, completeness, and correctness under different threshold values. Each method is evaluated in terms of its ability for detection UAV objects (crowd and cars features). Then the common detected features between both FAST versions and the Harris detector are extracted. This is to determine which method performs best under different image conditions (e.g., illumination variations, camera position and orientation, and image noise). The results show that the size of the threshold plays a crucial role in determining the number of detected feature points. An increase in the threshold value leads to a decrease in the number of detected points and vice versa. Thus, the correctness decreases whereas the completeness increases as a function of the threshold values. Furthermore, the relationship between the FAST-9 and the Harris detector is slightly better than those between the FAST-12 and the Harris detector. This is because the number of common features between the FAST-9 and the Harris detector are relatively higher than those between the FAST-12 and the Harris detector.
Keywords
feature extraction; corner detection; FAST algorithm; Harris detector; UAV
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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