Proceedings Article10.1109/ICIP.2017.8296797
BAFT: Binary affine feature transform
Jonas Toft Arnfred,Viet Dung Nguyen,Stefan Winkler +2 more
- 27 Sep 2017
- pp 2821-2825
2
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.
read more
Abstract: We introduce BAFT, a fast binary and quasi affine invariant local image feature. It combines the affine invariance of Harris Affine feature descriptors with the speed of binary descriptors such as BRISK and ORB. BAFT derives its speed and precision from sampling local image patches in a pattern that depends on the second moment matrix of the same image patch. This approach results in a fast but discriminative descriptor, especially for image pairs with large perspective changes. Our evaluation on 40 different image pairs shows that BAFT increases the area under the precision/recall curve (AUC) compared to traditional descriptors for the majority of image pairs. In addition we show that this improvement comes with a very low performance penalty compared to the similar ORB descriptor. The BAFT source code is available for download.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Hardware implementation of a shape recognition algorithm based on invariant moments
Clement Raffaitin,Juan-Sebastian Romero,Luis-Miguel Procel +2 more
- 26 Aug 2019
TL;DR: The present work shows the description of a simple fast shape detection algorithm and its implementation in hardware in a FPGA system, based on the concepts of Hús moments which are invariant to similarity transformations.
5
BALG: An alternative for fast and robust feature matching
TL;DR: Extensive experiments on four publicly benchmark datasets prove the proposed method to be an alternative for time critical feature matcher and improve the affine invariance of binary descriptor while enable fast processing.
4
References
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
A Combined Corner and Edge Detector
Chris Harris,Mike Stephens +1 more
- 01 Jan 1988
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
ORB: An efficient alternative to SIFT or SURF
Ethan Rublee,Vincent Rabaud,Kurt Konolige,Gary Bradski +3 more
- 06 Nov 2011
TL;DR: This paper proposes a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise, and demonstrates through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations.
11.8K
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
A performance evaluation of local descriptors
Krystian Mikolajczyk,Cordelia Schmid +1 more
- 18 Jun 2003
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.