Machine Learning for High-Speed Corner Detection

Abstract Where feature points are used in real-time frame-rate applications, a high-speed feature detector is necessary. Feature detectors such as SIFT (DoG), Harris and SUSAN are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Here we show that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time. By comparison neither the Harris detector (120%) nor the detection stage of SIFT (300%) can operate at full frame rate. Clearly a high-speed detector is of limited use if the features produced are unsuitable for downstream processing. In particular, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations[1]. Hence the second contribution of this paper is a comparison corner detectors based on this criterion applied to 3D scenes. T...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Edward Rosten, Tom Drummond
• Keywords: Comparison Corner Detectors, Computer Vision, ECCV 2006, Feature Detectors, Feature Points, Harris Detector, Real-time Frame-rate Applications