This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descr iptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descr iptor) and by simplifying these methods to the essential. This leads to a combination of novel detection, descr iption, and matching steps. The paper presents experimental results on a standard uation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF’s strong performance.

-This is achieved by relying on integral images for image convolutions　by building on the strengths of the leading existing detectors and descr iptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descr iptor)　and by simplifying these methods to the essential. This leads to a combination of novel detection, descr iption, and matching steps. The paper presents experimental results on a standard　uation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF’s strong performance.