Jonas Toft Arnfred
University of Illinois at Urbana–Champaign
6 Papers
18 Citations
Jonas Toft Arnfred is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Feature (computer vision) & Template matching. The author has an hindex of 4, co-authored 6 publications. Previous affiliations of Jonas Toft Arnfred include Amazon.com.
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Papers
A general framework for image feature matching without geometric constraints
TL;DR: It is proved that the traditional Ratio-Match is the worst performer, and a general framework of 4 algorithms for image feature matching is proposed, proving that all three methods consistently outperform or equal ratio-Match.
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Modeling Image Appeal Based on Crowd Preferences for Automated Person-Centric Collage Creation
Vassilios Vonikakis,Ramanathan Subramanian,Jonas Toft Arnfred,Stefan Winkler +3 more
- 07 Nov 2014
TL;DR: This paper employed nine low level image attributes to model the image selection process, and trained SVRs which could adequately predict image selections for the album-specific conditions, suggesting that context greatly influences the categorization of what is more and less appealing.
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Mirror Match: Reliable Feature Point Matching without Geometric Constraints
Jonas Toft Arnfred,Stefan Winkler,Sabine Süsstrunk +2 more
- 05 Nov 2013
TL;DR: This work proposes two algorithms for matching feature points without the use of geometric constraints, one of which relies on the fact that any match between two images should be better than all possible matches within a single image.
Fast-match: Fast and robust feature matching on large images
Jonas Toft Arnfred,Stefan Winkler +1 more
- 10 Dec 2015
TL;DR: This work introduces Fast-Match, an algorithm designed to match large images efficiently without compromising matching accuracy, which derives its speed from only computing features in those parts of the image that can be confidently matched.
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BAFT: Binary affine feature transform
Jonas Toft Arnfred,Viet Dung Nguyen,Stefan Winkler +2 more
- 27 Sep 2017
TL;DR: BAFT combines the affine invariance of Harris Affine feature descriptors with the speed of binary descriptors such as BRISK and ORB to result in a fast but discriminative descriptor, especially for image pairs with large perspective changes.
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