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A Performance Evaluation of Local Descriptors
TLDR
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. Expand
Scale & Affine Invariant Interest Point Detectors
TLDR
A comparative evaluation of different detectors is presented and it is shown that the proposed approach for detecting interest points invariant to scale and affine transformations provides better results than existing methods. Expand
A Comparison of Affine Region Detectors
TLDR
A snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework. Expand
A performance evaluation of local descriptors
  • K. Mikolajczyk, C. Schmid
  • Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 18 June 2003
TLDR
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. Expand
P-N learning: Bootstrapping binary classifiers by structural constraints
TLDR
It is shown that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, and a theory that formulates the conditions under which P-N learning guarantees improvement of the initial classifier is proposed and validated on synthetic and real data. Expand
The Visual Object Tracking VOT2016 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment. Expand
An Affine Invariant Interest Point Detector
TLDR
A novel approach for detecting affine invariant interest points that can deal with significant affine transformations including large scale changes and shows an excellent performance in the presence of large perspective transformations including significant scale changes. Expand
HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors
TLDR
A novel benchmark for evaluating local image descriptors is proposed and it is shown that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation. Expand
Forward-Backward Error: Automatic Detection of Tracking Failures
TLDR
It is demonstrated that the proposed error enables reliable detection of tracking failures and selection of reliable trajectories in video sequences and is complementary to commonly used normalized cross-correlation (NCC). Expand
Indexing based on scale invariant interest points
This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: (1) Interest points can be adapted to scale and giveExpand
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