Computer Vision – ECCV 2018 Workshops
Laura Leal-Taixé , Stefan Roth
AbstractNumerous computer vision applications rely on local feature descriptors, such as SIFT, SURF or FREAK, for image matching. Although their local character makes image matching processes more robust to occlusions, it often leads to geometrically inconsistent keypoint matches that need to be filtered out, e.g. using RANSAC. In this paper we propose a novel, more discriminative, descriptor that includes not only local feature representation, but also information about the geometric layout of neighbouring keypoints. To that end, we use a Siamese architecture that learns a low-dimensional feature embedding of keypoint constellation by maximizing the distances between non-corresponding pairs of matched image patches, while minimizing it for correct matches. The 48-dimensional floating point descriptor that we train is built on top of the state-of-the-art FREAK descriptor achieves significant performance improvement over the competitors on a challenging TUM dataset.
|Publisher||Springer International Publishing, MNiSW |
|Publishing place (Publisher address)||Springer, : Gewerbestrasse 11, 6330 Cham, Switzerland|
|Other ISBN||978-3-030-11021-5 (eBook)|
|Book series /Journal (in case of Journal special issue)||Lecture Notes In Computer Science, ISSN 0302-9743, [1611-3349], (0 pkt)|
|Publication size in sheets||37.65|
|Conference||European Conference on Computer Vision (ECCV 2018), 08-09-2018 - 14-09-2018, Munich, Niemcy|
|Keywords in English||Feature descriptor Image matching Siamese networks|
|Project||Development of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Arabas Jarosław,
, Phone: +48 22 234 7432, start date 15-04-2016, end date 30-11-2017, II/2016/DS/1, Completed
|Score||= 20.0, 02-02-2020, MonograhOrBookMainLanguagesEditor|
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