We present a general novel image descriptor based on higherorder differential geometry and investigate the effect of common descriptor choices. Our investigation is twofold in that we develop a jet-based descriptor and perform a comparative evaluation with current state-of-the-art descriptors on the recently released DTU Robot dataset. We demonstrate how the use of higher-order image structures enables us to reduce the descriptor dimensionality while still achieving very good performance. The descriptors are tested in a variety of scenarios including large changes in scale, viewing angle and lighting. We show that the proposed jet-based descriptor is superior to state-of-the-art for DoG interest points and show competitive performance for the other tested interest points.
Lecture Notes in Computer Science: Workshops and Demonstrations, Part Iii, 2012, p. 638-650
The Faculty of Science; Datalogi; Computer Vision; Interest point detector
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Lecture Notes in Computer Science
12th European Conference on Computer Vision (ECCV 2012)European Conference on Computer Vision