Proceedings Article10.1109/ICPR.2006.479
Efficient Non-Maximum Suppression
Alexander Neubeck,L. Van Gool +1 more
- 20 Aug 2006
- Vol. 3, pp 850-855
2K
TL;DR: This work scrutinize a low level computer vision task - non-maximum suppression (NMS) - and derive several algorithms ranging from easy-to-implement to highly-efficient.
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Abstract: In this work we scrutinize a low level computer vision task - non-maximum suppression (NMS) - which is a crucial preprocessing step in many computer vision applications. Especially in real time scenarios, efficient algorithms for such preprocessing algorithms, which operate on the full image resolution, are important. In the case of NMS, it seems that merely the straightforward implementation or slight improvements are known. We show that these are far from being optimal, and derive several algorithms ranging from easy-to-implement to highly-efficient.
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Citations
SURF: speeded up robust features
Herbert Bay,Tinne Tuytelaars,Luc Van Gool +2 more
- 07 May 2006
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
14.9K
Measuring the Objectness of Image Windows
TL;DR: In this paper, a generic objectness measure is proposed to quantify how likely an image window is to contain an object of any class, such as cows and telephones, from amorphous background elements such as grass and road.
StereoScan: Dense 3d reconstruction in real-time
Andreas Geiger,Julius Ziegler,Christoph Stiller +2 more
- 05 Jun 2011
TL;DR: In this article, a sparse feature matcher and visual odometry algorithm are combined with a multi-view linking scheme for generating consistent 3D point clouds for online 3D reconstruction.
Measuring the objectness of image windows
Bogdan Alexe,Thomas Deselaers,Vittorio Ferrari +2 more
- 01 Aug 2011
TL;DR: A generic objectness measure, quantifying how likely it is for an image window to contain an object of any class, and uses objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives.
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References
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Scale & Affine Invariant Interest Point Detectors
TL;DR: 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.
Matching Widely Separated Views Based on Affine Invariant Regions
Tinne Tuytelaars,Luc Van Gool +1 more
TL;DR: To increase the robustness of the system, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric) allow to test the consistency of correspondences and hence to reject falsely matched regions.
Computing 2-D min, median, and max filters
Joseph Gil,Michael Werman +1 more
TL;DR: Fast algorithms for computing min, median, max, or any other order statistic filter transforms are described and a logarithmic time per pixel lower bound for the computation of the median filter is shown.
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