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    • 6. 发明授权
    • Apparatus for visual recognition
    • 视觉识别装置
    • US5111516A
    • 1992-05-05
    • US507406
    • 1990-04-11
    • Masaru NakanoKazutoshi Sukigara
    • Masaru NakanoKazutoshi Sukigara
    • G06F15/18G06K9/64G06N3/00G06N99/00G06T7/00
    • G06K9/6211
    • A basic image of objects is extracted from a two-dimensional image of objects. Geometrical elements of the objects are extracted from the extracted basic image. The objects to be recognized are identified by searching a combination of the geometrical elements which match a geometrical model and then utilizing candidate position/orientation of the objects to be recognized, said candidate position/orientation being determined from a relationship in relative position between the combination of geometrical elements and the geometrical model. Mesh cells fixed to the geometrical model are mapped on the basic image based on the candidate position/orientation. In addition, verification is made as to whether an image of the geometrical model mapped by the candidate position/orientation is accurately matched with an image of one of the objects to be recognized, through a neural network to which values got from the basic image included in the individual mesh cells are to be applied as input values. Combination weight factors employed in the neural network are learned according to the verified results. It is also possible to recognize the multi-purpose objects according to how to learn the combination weight factors.
    • 从对象的二维图像中提取对象的基本图像。 从提取的基本图像中提取对象的几何元素。 通过搜索与几何模型匹配的几何元素的组合然后利用要识别的对象的候选位置/取向来识别待识别的对象,所述候选位置/取向是根据组合之间的相对位置的关系确定的 的几何元素和几何模型。 固定在几何模型上的网格单元基于候选位置/方向映射到基本图像上。 此外,通过从包括基本图像的值得到的神经网络,验证由候选位置/方向映射的几何模型的图像是否与要被识别的对象之一的图像精确匹配 在单个网格单元格中将被用作输入值。 根据验证结果了解神经网络中使用的组合权重因子。 根据如何学习组合权重因子,也可以识别多用途对象。