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. Platform: |
Size: 686080 |
Author:yangwei |
Hits:
Description: 介绍detector和descriptor的很好文档，涉及到Harris,LOG,DOG,SIFT,Hessian以及Harris-Laplace等-A good document describes detector and descriptor,contains Harris,LOG,DOG,SIFT,Hessian and Harris-Laplace and so on. Platform: |
Size: 4268032 |
Author:caikehe |
Hits:
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 |
Hits:
Description: 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 uation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF’s strong performance.
-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 uation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF’s strong performance.
Platform: |
Size: 680960 |
Author:Amal |
Hits:
Search - hessian detector