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Description: In this paper, a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust
Features) is presented. It approximates or even outperforms previously proposed
schemes with respect to repeatability, distinctiveness, and robustness, yet
can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descriptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, description, and
matching steps. The paper presents experimental results on a standard
evaluation set, as well as on imagery obtained in the context of a real-life
object recognition application. Both show SURF’s strong performance.-In this paper, a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust
Features) is presented. It approximates or even outperforms previously proposed
schemes with respect to repeatability, distinctiveness, and robustness, yet
can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descriptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, description, and
matching steps. The paper presents experimental results on a standard
evaluation set, as well as on imagery obtained in the context of a real-life
object recognition application. Both show SURF’s strong performance.
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Size: 686080 |
Author: yangwei |
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Description: Hessian interest point detector
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Size: 215040 |
Author: Mostafa Kazemi |
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Description: This document describe how to extract keypoints form pictures. Method used - harris affine and hessian affine
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Size: 2582528 |
Author: Jakub |
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Description: 考虑了放射变换因素，比sift有更好的效果，但是速度由于增加了旋转的考虑会变慢很多！-A fully affine invariant image comparison method, Affine-SIFT (ASIFT) is introduced. While SIFT is fully invariant with respect to only four parameters namely zoom, rotation and translation, the new method treats the two left over parameters : the angles defining the camera axis orientation. Against any prognosis, simulating all views depending on these two parameters is feasible. The method permits to reliably identify features that have undergone very large affine distortions measured by a new parameter, the transition tilt. State-of-the-art methods hardly exceed transition tilts of 2 (SIFT), 2.5 (Harris-Affine and Hessian-Affine) and 10 (MSER). ASIFT can handle transition tilts up 36 and higher.
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Size: 1491968 |
Author: liub |
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Description: 仿射不变Harris, Laplacian, det(Hessian) and Ridge 特征点检测
参考文献：An affine invariant interest point detector , K.Mikolajczyk and C.Schmid, ECCV 02, pp.I:128-142.-Matlab code for detecting Affine spatial interest points. Includes Harris, Laplacian, det(Hessian) and Ridge interest point operators in combination with spatial scale selection based on the extrema of scale-normalized Laplacian and affine adaptation basen on second-moment matrix. Scale and shape adaptation are optional and disjoint.
Zip archive: affintpoints.zip
Ref: An affine invariant interest point detector , K.Mikolajczyk and C.Schmid, ECCV 02, pp.I:128-142.
What is in the package:
1) ineterst point detection of different kinds (Harris, Laplace, det(H), Ridge)
2) scale, shape and position adaptation procedure
3) demo examples and a script for batch-mode computation and saving of the results
what is not in the package:
- no rotation estimation
- no region descriptor computation
Platform: |
Size: 901120 |
Author: 灵台斜月 |
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Description: 影像仿射变换特征提取，结合 sift 等局部特征可以处理较大视角变化下的图像匹配。-This is an implementation of Hessian-Affine detector.
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Size: 19456 |
Author: li |
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