face sample造句

• A method of face detection based on support vector machine ( svm ) is put forward . the features are extracted by applying the discrete cosine transform ( dct ) to the preprocessing image . the dct coefficients are inputted to the svm and the svm are trained using the cropped face samples and the " bootstrapping non - face " samples
  对预处理后的图像进行离散余弦变换提取特征，取dct系数作为支持向量机的输入，将经过裁剪的“人脸”样本与“自举”方法得到的“非人脸”样本一起用来训练支持向量机。
• We construct weak classifier by a haar feature ; then weak classifiers are combined to a strong classifier in a linear way . the final classifier is built in a cascade structure , which could reject most non - face samples in the early layer . also we use integral image to quickly calculate the feature and reduce the detection time
  本文以简单的haar特征结合阈值构造弱分类器，通过adaboost学习选择和集成弱分类器，最后按照分层结构把集成的分类器组合在一起；同时，在检测过程中采用积分图的方法计算特征，保证了检测的速度。