| 1. | The principle of gabor filter is expounded
阐述了gabor滤波器原理。 |
| 2. | The neural network recognizing algorithm based on multi channel gabor filter feature is presented
相应地给出了一个基于多通道gabor滤波器特征神经网络识别算法。 |
| 3. | Iris feature extraction is based on texture analysis using multi - channel gabor - wavelet filtering
特征提取阶段，采用多方向多频率的gabor滤波器来进行图像纹理特征分析。 |
| 4. | The multi channel gabor filter is designed based on theory and practicality , the texture features of gray image target are extracted
根据理论分析和实际需要，设计了多通道gabor滤波器，提取了灰度图像目标纹理特征。 |
| 5. | Different fonts are regarded as different textures , then font is identified through texture analysis with multi - channel gabor filters
将不同字体的文本看作不同的纹理，使用多通道gabor滤波器的纹理分析方法识别字体。 |
| 6. | The gabor filter is used to extract the characteristics . in this method , the jet which is a kind of the multi - scaled analysis methods is used , and get a good result
本文使用gabor滤波器，多尺度提取方向特征，在对方向性强的纹理特征的分割中有很好的效果。 |
| 7. | At the same time , gabor filter is used to extract the global feature of fingerprints from four directions and a graphic user interface is designed for this application
在指纹特征提取方面，本文基于gabor滤波器从四个方向提取指纹的全局特征，并设计了相应的图形用户界面程序。 |
| 8. | We excogitate the gabor filter based - on sub - block texture analyse to extract the feature of iris normalized image and encode them . then , we use hamming distance to match two iris codes . in order to reduce the rotation of iris , we work out a new method
4 .研究出基于子块纹理分析的gabor滤波器方法来对虹膜归一化图像进行特征提取和编码;在虹膜编码匹配中,本论文采用hamming距离来进行编码之间的匹配。 |
| 9. | Main works based on the newspaper samples is just as the following : ( 1 ) texture block is designed to embody more font characters . ( 2 ) filter orientation is optimized with genetic algorithm to make the angle set subtler . then the multi - channel gabor filter may extract better features
通过对实际字体样本的分析，本文主要完成了以下工作： ( 1 )设计了更能体现字体特征的纹理图像块； ( 2 )利用遗传算法对滤波角度进行了优化选择，得到了更为精细的角度集合，以此生成的多通道gabor滤波器能够提取稳定的字体特征； ( 3 )字体样本的分布具有多峰性质，以动态聚类算法得到的分段线性分类器更好地分类字体。 |
| 10. | The second step is to devise mechanisms for extracting the facial expression information from the observed facial image . images are transformed using a multi - scale , multi - orientation set of gabor filters . since gabor vectors at neighboring pixels are highly correlated and redundant , a rectangular grid is then automatically registered with the face based on the result of face detection
在定位人脸后，下一步就是要进行脸部表情信息的自动提取，为了描述人脸的面部结构，本文采用了gabor滤波器对图像进行滤波，由于gabor矢量在相邻像素间是高度相关和信息冗余的，本文构建了一个脸部稀疏网格，抽取网格中每个节点处的一组gabor滤波结果作为脸部表情数据，从而实现脸部表情数据的自动提取。 |
