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A Performance Evaluation of Local Descriptors
In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [Mikolajczyk, K and Schmid, C, 2004]. ManyExpand
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Scale & Affine Invariant Interest Point Detectors
In this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations. Our scale and affine invariant detectors are based on the following recentExpand
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A Comparison of Affine Region Detectors
The paper gives 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. Six types ofExpand
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P-N learning: Bootstrapping binary classifiers by structural constraints
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the label of one exampleExpand
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A performance evaluation of local descriptors
  • K. Mikolajczyk, C. Schmid
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 18 June 2003
In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [Mikolajczyk, K and Schmid, C, 2004]. ManyExpand
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The Visual Object Tracking VOT2016 Challenge Results
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers areExpand
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An Affine Invariant Interest Point Detector
This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformationsExpand
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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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Forward-Backward Error: Automatic Detection of Tracking Failures
This paper proposes a novel method for tracking failure detection. The detection is based on the Forward-Backward error, i.e. the tracking is performed forward and backward in time and theExpand
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HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors
In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects ofExpand
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