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METHOD OF SELECTING ATTRIBUTES FOR STATISTICAL LEARNING FOR OBJECT DETECTION AND RECOGNITION

机译:统计检测对象识别和识别的属性选择方法

摘要

The invention relates to an attribute selection method for making statistical learning of descriptors intended to enable automatic recognition and/or detection of an object from a set of images, method characterized by the following steps: obtain a mask of the object in each image containing said object to be recognized, define and select at least one set of descriptors as a function of their geometric shape and/or apparent specific physical characteristics, calculate attributes associated with this shape and said specific physical characteristics, sort the descriptors as a function of their respective scores, select descriptors with the highest scores to perform said statistical learning.
机译:本发明涉及一种用于对描述符进行统计学习的属性选择方法,该描述符旨在能够从一组图像中自动识别和/或检测物体,该方法的特征在于以下步骤:在包含所述图像的每个图像中获得物体的遮罩。待识别的对象,根据其几何形状和/或表观特定物理特征定义和选择至少一组描述符,计算与该形状和所述特定物理特征相关的属性,根据描述符各自的功能对描述符进行分类得分,选择得分最高的描述符以执行所述统计学习。

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