A fast location algorithm using classifying shrinkage in parameters space and a feature extraction method using improved wavelet cross-zero detection is presented.
提出了参数空间分级收缩的新的定位算法及其改进的小波过零检测的特征提取算法。
Then, integral image is used to rapidly calculate the convolution of rectangular feature templates, so the detection of scale-space geometric features is greatly accelerated in the method.
矩形特征模板的卷积可以用积分图进行快速计算，该方法使特征检测的速度得到了很大提高。
Lastly, based on the research of the important image feature, i. e. corner, a new Accumulative Intersection Space based corner detection method is developed.
最后，本文对一种重要的图像特征——角点进行了研究，提出了一种新的基于交点累积空间的角点提取算法。
The PCA algorithm USES the "feature-face" approach to achieve the purpose of face detection, which determines whether it belongs to the face according to the "face space" the distance of the sample.
后者是利用了“特征脸”的方法，根据待识别样本到“脸空间”的距离确定它是否属于人脸，以此达到检测人脸的目的。
The PCA algorithm USES the "feature-face" approach to achieve the purpose of face detection, which determines whether it belongs to the face according to the "face space" the distance of the sample.
后者是利用了“特征脸”的方法，根据待识别样本到“脸空间”的距离确定它是否属于人脸，以此达到检测人脸的目的。
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