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1.发明授权Systems, circuits, and methods for efficient hierarchical object recognition based on clustered invariant features 有权基于聚类不变特征的有效分层对象识别的系统，电路和方法公开(公告)号：US09204112B2
公开(公告)日：2015-12-01
申请号：US13762267
申请日：2013-02-07
Applicant: STMicroelectronics S.r.l
Inventor： Paolo Pasteris , Giovanni Condello , Danilo Pietro Pau
CPC classification number: H04N19/124 , G06K9/00523 , G06K9/00671 , G06K9/46 , G06K9/4671 , G06K9/6218 , H04N19/13 , H04N19/91
Abstract: One embodiment is a method for selecting and grouping key points extracted by applying a feature detector on a scene being analyzed. The method includes grouping the extracted key points into clusters that enforce a geometric relation between members of a cluster, scoring and sorting the clusters, identifying and discarding clusters that are comprised of points which represent the background noise of the image, and sub-sampling the remaining clusters to provide a smaller number of key points for the scene.
Abstract translation: 一个实施例是一种用于选择和分组通过在正在分析的场景上应用特征检测器而提取的关键点的分组的方法。 该方法包括将所提取的关键点分组成强制簇的成员之间的几何关系，对聚类进行评分和排序，识别和丢弃由表示图像的背景噪声的点组成的簇，并对 剩余的群集为场景提供较少数量的关键点。
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2.发明申请SYSTEMS, CIRCUITS, AND METHODS FOR EFFICIENT HIERARCHICAL OBJECT RECOGNITION BASED ON CLUSTERED INVARIANT FEATURES 有权基于积分不变特征的高效分层对象识别的系统，电路和方法公开(公告)号：US20130216143A1
公开(公告)日：2013-08-22
申请号：US13762267
申请日：2013-02-07
Applicant: STMicroelectronics S.r.l
Inventor： Paolo Pasteris , Giovanni Condello , Danilo Pietro Pau
IPC: G06K9/62
CPC classification number: H04N19/124 , G06K9/00523 , G06K9/00671 , G06K9/46 , G06K9/4671 , G06K9/6218 , H04N19/13 , H04N19/91
Abstract: One embodiment is a method for selecting and grouping key points extracted by applying a feature detector on a scene being analyzed. The method includes grouping the extracted key points into clusters that enforce a geometric relation between members of a cluster, scoring and sorting the clusters, identifying and discarding clusters that are comprised of points which represent the background noise of the image, and sub-sampling the remaining clusters to provide a smaller number of key points for the scene.
Abstract translation: 一个实施例是一种用于选择和分组通过在正在分析的场景上应用特征检测器而提取的关键点的分组的方法。 该方法包括将所提取的关键点分组成强制簇的成员之间的几何关系，对聚类进行评分和排序，识别和丢弃由表示图像的背景噪声的点组成的簇，并对 剩余的群集为场景提供较少数量的关键点。
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