公开(公告)号：US20050185835A1

公开(公告)日：2005-08-25

申请号：US11044188

申请日：2005-01-28

申请人： Masakazu Matsugu ,  Pierre Cardon

发明人： Masakazu Matsugu ,  Pierre Cardon

IPC分类号： G06T1/00 ,  G06K9/46 ,  G06K9/62 ,  G06N3/00 ,  G06T1/40 ,  G06T7/00

CPC分类号： G06K9/4619

摘要： In learning for pattern recognition, an aggregation of different types of object image data is inputted, and local features having given geometric structures are detected from each object image data inputted. The detected local features are put through clustering, plural representative local features are selected based on results of the clustering, and a learning data set containing the selected representative local features as supervisor data is used to recognize or detect an object that corresponds to the object image data. The learning thus makes it possible to appropriately extract, from an aggregation of images, local features useful for detection and recognition of subjects of different categories.

摘要翻译： 在学习图形识别时，输入不同类型的对象图像数据的聚合，并且从输入的每个对象图像数据检测具有给定几何结构的局部特征。 检测到的本地特征通过聚类进行，基于聚类的结果选择多个代表性的局部特征，并且使用包含所选择的代表性局部特征作为监督数据的学习数据集来识别或检测与对象图像相对应的对象 数据。 因此，该学习使得可以从图像的聚集中适当地提取对于不同类别的对象的检测和识别有用的局部特征。

公开(公告)号：US07697765B2

公开(公告)日：2010-04-13

申请号：US11044188

申请日：2005-01-28

申请人： Masakazu Matsugu ,  Pierre Cardon

发明人： Masakazu Matsugu ,  Pierre Cardon

IPC分类号： G06K9/62

CPC分类号： G06K9/4619

摘要： In learning for pattern recognition, an aggregation of different types of object image data is inputted, and local features having given geometric structures are detected from each object image data inputted. The detected local features are put through clustering, plural representative local features are selected based on results of the clustering, and a learning data set containing the selected representative local features as supervisor data is used to recognize or detect an object that corresponds to the object image data. The learning thus makes it possible to appropriately extract, from an aggregation of images, local features useful for detection and recognition of subjects of different categories.

摘要翻译： 在学习图形识别时，输入不同类型的对象图像数据的聚合，并且从输入的每个对象图像数据中检测具有给定几何结构的局部特征。 检测到的本地特征通过聚类进行，基于聚类的结果选择多个代表性的局部特征，并且使用包含所选择的代表性局部特征作为监督数据的学习数据集来识别或检测与对象图像相对应的对象 数据。 因此，该学习使得可以从图像的聚集中适当地提取对于不同类别的对象的检测和识别有用的局部特征。