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    • 9. 发明授权
    • Face detection using division-generated haar-like features for illumination invariance
    • 使用分割生成的哈尔状特征进行面部检测,用于照明不变性
    • US08971628B2
    • 2015-03-03
    • US12843805
    • 2010-07-26
    • George SusanuDan FilipMihnea Gangea
    • George SusanuDan FilipMihnea Gangea
    • G06K9/00G06K9/46
    • G06K9/00228G06K9/4642
    • Faces in images are quickly detected with minimal memory resource usage. Instead of calculating a Haar-like feature value by subtracting the average pixel intensity value in one rectangular region from the average pixel intensity value in another, adjacent rectangular region, a face-detection system calculates that Haar-like feature value by dividing the average pixel intensity value in one such rectangular region by the average pixel intensity value in the other such adjacent rectangular region. Thus, each Haar-like value is calculated as a ratio of average pixel intensity values rather than as a difference between such average pixel intensity values. The feature values are calculated using this ratio-based technique both during the machine-learning procedure, in which the numerical ranges for features in known face-containing images are learned based on labeled training data, and during the classifier-applying procedure, in which an unlabeled image's feature values are calculated and compared to the previously machine-learned numerical ranges.
    • 用最少的内存资源使用快速检测图像中的脸部。 代替通过从另一个相邻矩形区域中的平均像素强度值减去一个矩形区域中的平均像素强度值来计算哈尔状特征值,面部检测系统通过将平均像素 一个这样的矩形区域中的强度值乘以另一个这样的相邻矩形区域中的平均像素强度值。 因此,每个Haar样值被计算为平均像素强度值的比率而不是作为这样的平均像素强度值之间的差。 在机器学习过程中,使用这种基于比例的技术来计算特征值,其中基于标记的训练数据学习已知的含有脸谱的图像中的特征的数值范围,并且在分类器应用程序期间, 计算未标记图像的特征值并将其与之前的机器学习数值范围进行比较。
    • 10. 发明申请
    • Face Detection Using Division-Generated Haar-Like Features For Illumination Invariance
    • 用于照明不变性的分裂Haar样特征的人脸检测
    • US20120019683A1
    • 2012-01-26
    • US12843805
    • 2010-07-26
    • George SusanuDan FilipMihnea Gangea
    • George SusanuDan FilipMihnea Gangea
    • G06K9/46H04N5/228
    • G06K9/00228G06K9/4642
    • Faces in images are quickly detected with minimal memory resource usage. Instead of calculating a Haar-like feature value by subtracting the average pixel intensity value in one rectangular region from the average pixel intensity value in another, adjacent rectangular region, a face-detection system calculates that Haar-like feature value by dividing the average pixel intensity value in one such rectangular region by the average pixel intensity value in the other such adjacent rectangular region. Thus, each Haar-like value is calculated as a ratio of average pixel intensity values rather than as a difference between such average pixel intensity values. The feature values are calculated using this ratio-based technique both during the machine-learning procedure, in which the numerical ranges for features in known face-containing images are learned based on labeled training data, and during the classifier-applying procedure, in which an unlabeled image's feature values are calculated and compared to the previously machine-learned numerical ranges.
    • 用最少的内存资源使用快速检测图像中的脸部。 代替通过从另一个相邻矩形区域中的平均像素强度值减去一个矩形区域中的平均像素强度值来计算哈尔状特征值,面部检测系统通过将平均像素 一个这样的矩形区域中的强度值乘以另一个这样的相邻矩形区域中的平均像素强度值。 因此,每个Haar样值被计算为平均像素强度值的比率而不是作为这样的平均像素强度值之间的差。 在机器学习过程中,使用这种基于比例的技术来计算特征值,其中基于标记的训练数据学习已知的含有脸谱的图像中的特征的数值范围,并且在分类器应用程序期间, 计算未标记图像的特征值并将其与之前的机器学习数值范围进行比较。