基于图象中纹理的预知性，应用必要的约束条件推导出了该种滤波器。
Based on preknowledge of texture in an image, with needed constraint, the filters are derived.
基于图象中纹理的预知性，应用最小二乘法及必要的约束条件推导出了该种滤波器。
Based on preknowledge of texture in an image, by using least squares defintion and with needed constraint, the filters are derived.
为了更有效地利用图像的局部特征恢复被噪声感染的图像，基于图像局部纹理方向概率统计模型，提出一种针对混合噪声的非线性滤波算法。
In order to more effectively make use of local features to restore the noise-infected image, a nonlinear filtering algorithm based on local texture direction probability statistic model was proposed.
由于周期性纹理的纹元是以周期为单位的纹理块，该文试图对周期性纹理图像进行方向滤波后找出纹理的周期，然后以周期尺度为采样块的大小和采样间距，进行纹理合成。
Because the texel of periodic texture is the patch with the textures period, here try to calculate the period by direction filter, and then synthesize the periodic texture by sampling with the period.
设计频域滤波器抑制正常纹理频谱信息，通过重构对灰度图像进行分割，实现疵点与正常织物纹理的分离。
The frequency-domain filter is designed to remove normal texture information, based on the image reconstruction, fabric normal texture and defects are isolated by image segmentation.
针对这种情况，提出了一种改进的特征提取方法，将基于原图像的灰度级共生矩阵提取的纹理特征与滤波后图像的灰度特征进行组合用于分类。
Accordingly, we propose an improved feature extraction scheme, adopting the tone of filtered image combined with the texture features based on the GLCM of unfiltered image to form the feature vector.
该算法对图像进行多结构多尺度自适应形态滤波处理，从而抑制图像的暗噪声和暗纹理细节。
The algorithm first executed the adaptive morphological filtering of fusion to restrain dark noise and texture details of the images.
该方法将树型小波中颇纹理能量特征、灰度共生矩阵特征、树型小波滤波后的灰度组成的特征矢量对SAR图像进行分类。
The feature vector is composed of wavelet texture energy features, texture features based on the gray-level co-occurrence matrix and the tone of filtered SAR image by using tree wavelet.
并在不同滤波器长度下，对22幅遥感地貌纹理影像进行了分类试验，获得了较高的分类正确率。
The classification experiment had been carried out for 22 relief images for different length of filtering. And high accuracy was obtained.
对视频采集到的板材纹理图像进行灰度化、中值滤波去噪、二值化、腐蚀、膨胀等处理，得到仅包含目标纹理的图像后检测纹理边缘。
Treat with plate grain image got by video using methods of gray level transformation, median filter, erosion, expansion and so on, gain the image which only includes grain, then detect brim of grain.
该文在深入分析可调滤波器金字塔算法的基础上，提出了一种基于分形理论和可调滤波器金字塔算法的自然纹理综合方法。
This paper analyzes thoroughly the steerable filter pyramid algorithm and presents a natural texture synthesis method based on fractal theory and pyramid algorithm.
原则上，我们可以采用任意一种标准的纹理识别算法(例如：多通道伽柏滤波器方法)。
In principle, this allows us to apply any standard texture recognition algorithm for the task (e. g., the multi-channel Gabor filtering technique).
然后，对纹理图象施加滤波和纹理环境操作。
Then normal filtering and texture environment operations are performed using the texture image.
针对纹理图像分类问题，本文提出了一种应用ica滤波器技术提取图像纹理特征的方法。
A group of filters (ICA filters) is extracted from the sample texture images using ICA method.
对滤波后的纹理缺陷结果图像采用双阈值法，以确定纹理缺陷所在的位置。
Locations of texture defects were determined in the filtered image with texture defects by using the dual threshold method.
同时通过方向性滤波器判断所得的纹理角度也比较接近实际纹理角度。
Moreover, the texture Angle obtained through the directional filter is very close to the actual angel.
通过TICA从观测纹理图像学习图像分离基，并将其转化为一组滤波器，实现了一种无监督纹理分割算法。
The larger the dimension is, the more the visual patterns emerge. (2) A new method for texture segmentation by using the learned TICA unmixing bases as filters is implemented.
所提供的特征滤波器与传统的滤波器相比，可以有效挑选出数量更少、分类性能更优的纹理特征。
We can achieve better classification performance by the feature filters comparable to other traditional filter schemes while resulting in considerably smaller filters.
在大量样本分析的基础上 ，根据指纹纹理频率参数的变化动态调整方向滤波模板的大小对指纹图像进行有效的增强处理 ，对不同的纹理方向专门采用了查表的方法 ，减小了时间消耗。
According to the characteristics of fingerprints, the proposed algorithm also designs a group of oriented filters, and then manipulates them to filter the image depending on the orientation of the.
在大量样本分析的基础上 ，根据指纹纹理频率参数的变化动态调整方向滤波模板的大小对指纹图像进行有效的增强处理 ，对不同的纹理方向专门采用了查表的方法 ，减小了时间消耗。
According to the characteristics of fingerprints, the proposed algorithm also designs a group of oriented filters, and then manipulates them to filter the image depending on the orientation of the.
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