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。
Comparing with the traditional method, the volume of the new threshold space is noticeably minished. As a result, the precision of examining and classifying tobacco leaves is improved.
与传统的建立阈值空间方法相比，新的阈值空间体积显著减小，有效地提高了烟叶检测和分级的精度。
Firstly, according to the properties of the histogram scale space of an original image and the result of the multiscale filtering, optimal thresholds were determined for classifying the cells image.
首先根据原始图像直方图的尺度空间特性和多尺度滤波结果，选取最佳阈值将图像分为多个类。
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