Detecting Interpretable and Accurate Scale-Invariant keypoints

This paper presents a novel method for detecting scale invariant keypoints. It fills a gap in the set of available methods, as it proposes a scale-selection mechanism for junction-type features. The method is a scale-space extension of the detector proposed by F¨orstner (1994) and uses the general spiral feature model of Big¨un (1990) to unify different types of features within the same framework. By locally optimising the consistency of image regions with respect to the spiral model, we are able to detect and classify image structures with complementary properties over scalespace, especially star and circular shapes as interpretable and identifiable subclasses. Our motivation comes from calibrating images of structured scenes with poor texture, where blob detectors alone cannot find sufficiently many keypoints, while existing corner detectors fail due to the lack of scale invariance. The procedure can be controlled by semantically clear parameters. One obtains a set ...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Wolfgang F¨orstner, Timo Dickscheid, Falko Schindler
• Keywords: Computer Vision, ICCV 2009, Scale-Invariant Keypoints