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 |
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Description: Detecting, identifying, and recognizing salient regions or feature points
in images is a very important and fundamental problem to the computer vision
and robotics community. Tasks like landmark detection and visual odometry,
but also object recognition benefit from stable and repeatable salient features
that are invariant to a variety of effects like rotation, scale changes, view point
changes, noise, or change in illumination conditions. Recently, two promising new
approaches, SIFT and SURF, have been published. In this paper we compare and
evaluate how well different available implementations of SIFT and SURF perform
in terms of invariancy and runtime efficiency. Platform: |
Size: 869376 |
Author:yangwei |
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Description: 本文介绍了SIFT,SURF,PCA-SIFT的各自特点，阐述了各自适用的不同的场景。对图像识别和视频识别有很大的启迪作用。-This article describes the SIFT, SURF, PCA-SIFT to their own characteristics to explain their application of the different scenarios. Image recognition and video recognition a great inspiration. Platform: |
Size: 2844672 |
Author:liuweimin |
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Description: 课程设计中编写的使用surf进行视频片段特征识别的程序-Preparation of curriculum design in the use of surf video clips feature recognition process Platform: |
Size: 6498304 |
Author:天天 |
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Description: 主要是SURF算法在人脸识别 遥感图像配准和拼接等操作的实现，算法清晰。-SURF algorithm is mainly in face recognition remote sensing image registration and joining together the realization of the operation, the algorithm is clear. Platform: |
Size: 10836992 |
Author:萧萧 |
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Description: 本程序是将已有的程序针对掌纹图像识别进行了定向改进，改进之后识别率大大提升-This program is an existing program palmprint identification has been targeted for improvements, the recognition rate improved greatly enhanced after Platform: |
Size: 14454784 |
Author:张琛 |
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Description: 利用SURF算法，对人脸提取特征点。经作者检验，识别率可以在80 以上。- Extract face feature points using of SURF algorithm. With the inspection, the recognition rate can be above 80 . Platform: |
Size: 2304000 |
Author:屠震元 |
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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 |
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Description: SURF意指 加速的具有鲁棒性的特征，由Bay在2006年首次提出，这项技术可以应用于计算机视觉的物体识别以及3D重构中。SURF算子由SIFT算子改进而来，一般来说，标准的SURF算子比SIFT算子快好几倍，并且在多幅图片下具有更好的鲁棒性。SURF最大的特征在于采用了harr特征以及积分图像integral image的概念，这大大加快了程序的运行时间。-SURF (Speeded Up Robust Feature) is a robust local feature detector, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT. SURF is based on sums of 2D Haar wavelet responses and makes an efficient use of integral images.
Platform: |
Size: 2418688 |
Author:草 |
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Description: 该算法可以应用于计算机视觉的物体识别以及3D重构中。-The algorithm can be applied to object recognition in computer vision and 3D Reconstruction. Platform: |
Size: 136192 |
Author:zhaoxiaoming |
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Description: 基于SURF特征的印刷体汉字识别方法，可以将字倾斜矫正(A printed Chinese character recognition method based on SURF features) Platform: |
Size: 5964800 |
Author:丽丽6663142 |
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