Difference of Circles Feature Detector

Feature detection is a crucial step in many Computer Vision applications such as matching, tracking, visual odometry and object recognition, etc. Detecting robust features that are persistent, rotation-invariant, and quickly calculated is a major problem in computer vision. Feature detectors using the difference of Gaussian (DoG) are computationally expensive, however, if the DoG is used with image sub sampling at higher orders, the detectors become fast but their feature localization becomes inaccurate. Detectors based on difference of octagons (DoO) or difference of stars (DoS) algorithm are fast and localize the features accurately, but they are not rotation-invariant. This paper introduces a novel technique for the difference of circles (DoC) algorithm, used for feature detection, that is perfectly rotation-invariant and has the potential of being very fast through using circular integral images. The performance of DoC algorithm is compared with the difference of stars algorithm presented by 'Willow Garage'. The experiments conducted concentrate on the rotation-invariance property of DoC.