This paper presents an efficient approach to corner detection in images using a spiral addressing scheme in conjunction with simulated, biological involuntary eye movements. As part of this approach, a combined gradient detection and smoothing operation is used to quickly obtain a feature representation that can be used with a standard ‘cornerness’ measure. A computationally efficient use of the spiral address scheme to apply further processing operations such as non-maximum suppression is demonstrated. Comparative evaluation of three corner detection methods is presented and results demonstrate significantly faster processing times than other well-known corner detection methods.
|Number of pages||5|
|Publication status||Accepted/In press - 20 Jun 2018|
|Event||International Conference on Artificial Intelligence and Pattern Recognition - North China University of Technology (NCUT), Beijing, China|
Duration: 18 Aug 2018 → 20 Aug 2018
|Conference||International Conference on Artificial Intelligence and Pattern Recognition|
|Abbreviated title||AIPR 2018|
|Period||18/08/18 → 20/08/18|
Fegan, J., Coleman, S., Kerr, D., & Scotney, B. (Accepted/In press). FAST, BIOLOGICALLY INSPIRED CORNER DETECTION USING A SQUARE SPIRAL ADDRESS SCHEME AND ARTIFICIAL EYE TREMOR. Paper presented at International Conference on Artificial Intelligence and Pattern Recognition , Beijing, China.