Fast, Biologically Inspired Corner Detection Using a Square Spiral Address Scheme and Artificial Eye Tremor

This paper presents an efficient approach to corner detection for 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 a spiral address scheme to apply further processing operations such as non-maximum suppression is demonstrated. An evaluation of three corner detection methods is presented and results demonstrate that a method designed for a spiral based, biologically inspired approach can achieve a significantly faster runtime than comparative methods designed for a traditional approach.