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[Special Effectseccv06

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:

[Software Engineeringharrishessian

Description: This document describe how to extract keypoints form pictures. Method used - harris affine and hessian affine
Platform: | Size: 2582528 | Author: Jakub | Hits:

[OpenCVdemo_ASIFT_src

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.
Platform: | Size: 1491968 | Author: liub | Hits:

[Special Effectsaffintpoints

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:

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