文件名称:eccv06
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [PDF]
- 上传时间：
- 2012-11-26
- 文件大小：
- 670kb
- 下载次数：
- 0次
- 提 供 者：
- yan****
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
In this paper, a novel scale- and rotation-invariant interest point detector and descr iptor, 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 descr iptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descr iptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, descr iption, 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 descr iptor, 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 descr iptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descr iptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, descr iption, 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.
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 descr iptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descr iptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, descr iption, 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 descr iptor, 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 descr iptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descr iptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, descr iption, 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.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
eccv06.pdf