An Adaptive FAST Corner Detector based on High Contrast Grid Image
Teng Wu, Zhili Zhang, Junyang Zhao, Fenggang Liu
Available Online May 2017.
- https://doi.org/10.2991/icmeit-17.2017.128How to use a DOI?
- image; high contrast enhancement; corner detection; adaptive threshold; entropy
- In this paper, an improved adaptive threshold algorithm was propose for grid image blocks with preprocessing in order to settle some defects as feature clustering, threshold fixation and noise sensitivity in FAST corner detection algorithms. Firstly, a high contrast enhancement filter algorithm is used to preprocess the image to suppress effects of the noise and enhance target characteristics. Then, based on the statistical analysis of image entropy and variance, a prediction equation for capacity of corner is proposed to adjust the threshold of the image block after the base threshold is determined to overcome the problem of image feature clustering. The experiments show that the adaptive thresholds of the improved algorithm are reasonable and the distribution of corner detection results is uniform, and the effect of noise and fuzzy images on the stability of the algorithm is well solved.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Teng Wu AU - Zhili Zhang AU - Junyang Zhao AU - Fenggang Liu PY - 2017/05 DA - 2017/05 TI - An Adaptive FAST Corner Detector based on High Contrast Grid Image BT - 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmeit-17.2017.128 DO - https://doi.org/10.2991/icmeit-17.2017.128 ID - Wu2017/05 ER -