所用方法是图像处理分类技术中的最小距离分类方法。
By means of the minimum distance classification of digital image processing, the results obtained are satisfactory.
提出了一种基于类内类间离散度的最小距离分类器设计方法。
Minimum distance classifier based on between-class and within-class scatter is proposed.
论文提出了基于图像多路正交投影和最小距离分类器的人脸识别方法。
This paper proposes an image multi-orthogonal projection method for face recognition based on least class distance classifier.
然后，以特征系数作为识别特征量，采用最小距离分类法，实现了自动目标识别。
Then, the feature coefficients are classified by the minimum distance criterion to recognize the target automatically.
为了提高最小距离分类器的分类性能，主要的改进方法是选择更有效的距离度量。
To improve its classification performance, the main methods were selecting the more effective distance measure.
首先基于差分图像和肤色信息检测出人脸，其次使用改进的奇异值分解方法提取面部特征，最后运用最小距离分类器进行识别。
First based on the difference image and the skin color the face is detected and localized, then features are extracted by using improved SVD, last the features is classified by minimal distance.
通过形态特征的提取和选择，利用最小距离法和形态法对肺部细胞进行了初步的识别分类。
By extraction and selection of the cell shape feature, using the least distance method and shape method primarily identify and classify the lung cell.
对未知目标，以其子像对库矢量的欧氏距离最小为分类准则，进行了识别模拟实验。
Using the subimage of an unknown target as feature vector and minimum distance rule for target recognition, experiments on simulated data are done.
结果表明，SVM分类器的识别性能优于最小欧氏距离分类器，且KDDA特征的识别性能最优。
The experimental results show that the KDDA features achieve the best recognition performance, and the SVM classifier outperforms the minimum Euclidean distance classifier.
提出一种基于输入集分类函数的新的距离度量方法，它与前传回归的正交最小二乘法相结合，不仅可以学习分类超平面的参数，而且可以选择重要的输入节点。
Combining the new measure with the forward regression orthogonal least square (OLS), not only the parameters of the classification hyperplane, but also the important input nodes can be obtaind.
设计了一个二级组合分类器，该分类器综合使用了最小距离和BP神经网络两种模式识别方法。
A combined classifier is designed. There are two recognition ways used in the classifier. They are the least distance pattern recognition method and the BP neural network pattern recognition method.
并通过最小化分类误差准则最大化SVM两类输出值概率分布间的距离。
The proposed scheme also employs the rule that minimizes the error of classifications to maximize the distance of the output distributions of two classes.
并通过最小化分类误差准则最大化SVM两类输出值概率分布间的距离。
The proposed scheme also employs the rule that minimizes the error of classifications to maximize the distance of the output distributions of two classes.
应用推荐