| 1. | Based on general algebraic eigenvalue space to design the control law
基于广义代数特征空间的控制律设计 |
| 2. | There is still much work to do . in this paper , some of face recognition algorithms are probed based on algebraic features of the images . and the corresponding solutions are given
本文结合几种基于代数特征的人脸识别算法，对其中的部分问题分别进行了探讨，并给出了相应的解决方案。 |
| 3. | ( 4 ) algebraic feature extraction on the spectro - space has been proposed , which combines the wavelet analysis , wavelet packet analysis and the generalized optimal discriminant analysis
( 4 )提出了一种在频域上的代数特征抽取方法，该方法将小波分析、小波包分析和最优鉴别分析、广义最优鉴别分析相结合。 |
| 4. | The work including : ( 1 ) eigenfaces based on pca and the selection of the eigenvector based on algebraic features of the images , this paper first introduced the pca - based face recognition algorithm
本文工作包括： ( 1 ) 、基于pca的特征脸算法及特征值选择本文从图像整体代数特征出发，首先介绍了“特征脸”算法的原理和实现过程。 |
| 5. | The second method is to extract frequency features by multiple gabor filtering . according to the second means , the usability of common invariant features is analyzed and the invariance of algebraic features is discussed
西安理工大学硕士学位论文对第二类特征，论文首先分析了各种不变特征的可用性，对代数特征的不变性进行讨论。 |
| 6. | Face recognition based on the algebraic feature face recognition based on the algebraic feature expresses the human faces by vectors of feature too . but the vectors are algebraic feature ones . it is the projection of the human faces in the lower dimension sub - space of eigenface
基于代数特征的人脸识别方法这类识别方法也是将人脸用特征矢量表示，但采用的是代数特征矢量：即人脸图像在由“特征脸”张成的降维子空间上的投影。 |
| 7. | Standard face images are formed through the above - mentioned processing . during the feature extraction , for those standard face images , regarding the between - class scatter matrix as generating matrix , we extract the algebraic features of face images through k - l transform and singular value decomposition
在人脸特征提取过程中，对经过预处理的标准人脸图像，以类间散布矩阵为产生矩阵，通过k - l变换降维并结合奇异值分解来提取人脸代数特征。 |
| 8. | The model made rough classification to the human ears first according to their geometric features , then ica was used to extract the algebra features and support vector machine ( svm ) was for detailed classification , finally the results were achieved , which was in accordance with human natural recognition process
该模型首先根据人耳的几何特征对人耳进行粗分类；然后应用独立分量分析的方法提取代数特征，支持向量机进行细分类，最后给出分类结果。 |
| 9. | To increase speed and keep higher accuracy simultaneously , we modified traditional methods by proposing a grading match method based on both the algebraic feature and the geometric feature of fingerprints . first we extracted the algebraic feature of fingerprints through the wavelet transform , then excluded most fingerprints that ca n ' t match the source fingerprint in rough scales , finally processed further matching based on the geometric feature on possible fingerprints . as a result , amount computing is greatly reduced , and speed is increased
为了保证在取得较高识别率的同时，提高指纹识别的速度，本文对传统方法进行取长补短，即采用提取指纹图像的代数特征和几何特征相结合的方法，并辅以分阶段匹配的方式，先利用小波变换，提取指纹图像的代数特征，在粗尺度上排除大量与源指纹根本不匹配的指纹，只在可能匹配的指纹上基于几何特征作进一步的匹配，从而大量减少无用的计算量，提高指纹识别的速度。 |
