Description: 非常经典的基于模糊信息处理的数据融合方法研究.用MATLAB 实现的。
请大家好好学习-Very classical information processing based on fuzzy data fusion method. Using MATLAB realization. Please study hard Platform: |
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Author:ajie |
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Description: 提出了基于特征融合和模糊核判别分析(FKDA)的面部表情识别方法。首先，从每幅人脸图像中手工定
位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基
准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进
行特征融合，作为表情识别的输人特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最
近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和Ekman“面部表情图片”数据库上的实验，证实
了所提方法的有效性。-Proposed based on feature fusion and fuzzy kernel discriminant analysis (FKDA) facial expression recognition. First, face images of each piece of hand-set
Bit 34 basis points, as the geometric features of facial expression images, while using Gabor wavelet transform method to transform the images of each piece of expression, and extraction-based
Quasi-point of the Gabor wavelet coefficients, as Gabor features of facial expression image second, using canonical correlation analysis on the geometric features and Gabor features into
Line feature fusion, as expression recognition of input features then, using fuzzy kernel discriminant analysis method to extract and further identification features of expression Finally, the most
Neighbor classifier to complete expression of the classification. International expression by JAFFE database and Ekman "facial image" database on the experiment, confirmed
The proposed method. Platform: |
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Author:MJ |
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Description: 文中的方法是把图像分块，小波分解得到低频分量、高频分量，然后计算每一块的对比度，把图像块划分为清晰块、模糊块，把清晰块和模糊块相邻的区域定义为边界区域，融合时，直接选取清晰块作为融合后的相应块，对于边界区域，在小波分解的基础上采用基于对比度的像素选取的方法进行处理。-Paper, the method is to image segmentation, wavelet decomposition are low frequency, high frequency components, then calculate the contrast of each piece, the image block is divided into clear blocks, fuzzy block, to clear blocks and fuzzy block is defined as the border region adjacent to area, integration time, a clear block directly select the corresponding block as a fusion, for the border region, the wavelet decomposition on the basis of the pixel-based contrast method selected for processing. Platform: |
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Author:许国柱 |
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Description: 针对合成孔径雷达(SAR) 图像含有大量斑点噪声的特点,基于Contourlet 的多尺度、局部化、方向性和各向
异性等优点,并结合隐马尔科夫树( HMT) 模型和隐马尔科夫场(MRF) ,提出了一种基于Contourlet 域持续性和聚
集性的SAR 图像模糊融合分割算法。该算法有效捕获了Contourlet 子带的持续性和聚集性,并分别用HMT 和
MRF 来刻画,再依据模糊测度,将多尺度HMT 和MRF 有机融合,建立Contourlet 域HMT2MRF 融合模型,并导
出新模型下的最大后验概率(MAP) 分割公式。对实测SAR 图像进行了仿真,仿真结果和分析表明:与小波域上的
HMT2MRF 融合分割及Contourlet 域上HMT 和MRF 分割算法相比,该算法在抑制斑点噪声的同时,有效地提高
了SAR 图像的分割精度- In view of the speckle noise in the synthetic aperture radar (SAR) images , and based on the Contourlet′s
advantages of multiscale , localization , directionality , and anisot ropy , a new SAR image fusion segmentation
algorithm based on the pe rsis tence and clustering in the Contourlet domain is p roposed. The algorithm captures the
pe rsis tence and clus tering of the Contourlet t ransform , which is modeled by hidden Markov t ree (HMT) and Markov
random field (MRF) , respectively. Then , these two models are fused by fuzzy logic , resulting in a Contourlet
domain HMT2MRF fusion model . Finally , the maximum a poste rior (MAP) segmentation equation for the new fusion
model is deduced. The algorithm is used to emulate the real SAR images . Simulation results and analysis indicate that
the p roposed algorithm effectively reduces the influence of multiplicative speckle noise , imp roves the segmentation
accuracy and p rovides a bet te r visual quality for SAR images ove r the Platform: |
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Author:周二牛 |
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Description: 提出了一种基于多通道 Gabo r滤波器和 FCM聚类的图像融合新方法。该方法先利用模糊
C2均值聚类算法在多通道 Gabo r滤波器形成的特征空间上对图像进行区域分割 再对待融合图像进行多尺度小波分解 在此基础上利用 Gabo r滤波器提取高频段纹理特征构造区域相似度 ,应用区域相似度及信息量构造加权因子 ,从而得到融合图像的小波系数 最后 ,利用小波逆变换得到融合图像.-Proposed a multi-channel Gabo r filter and FCM clustering for image fusion method. The method first C2 means clustering algorithm using the fuzzy multi-channel Gabo r filter in the formation of the image feature space segmentation re fused to treat multi-scale wavelet decomposition image on this basis, the use Gabo r filter from the high frequency structural similarity region texture features, application areas and the information structure similarity weighting factor, resulting in fusion of the wavelet coefficients Finally, the inverse wavelet transform fusion image. Platform: |
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Author:guoj |
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Description: 提出了一种基于小波变换和模糊集的图像融合算法.其基本思想是: 首先对图像进行小波变换,获得图
像的低频和高频分量 随后在融合过程中, 对低频和高频分量采取不同的融合策略, 即对低频分量采用平均能
量法进行融合,对高频成分利用图像的模糊集, 寻求一个模糊隶属函数作为融合算子进行融合 最后再对融合
后的图像进行小波反变换,重构出融合后的图像.实验结果证明了方法的有效性.-Proposed image fusion algorithm based on wavelet transform and fuzzy sets. The basic idea is: First the image wavelet transform to obtain the low and high frequency components of the image followed by low and high frequency components in the fusion process to take a different the integration of strategy, integration of the low frequency components using the average energy method, the use of high frequency components of the image fuzzy sets, look for a fuzzy membership function as a fusion operator for fusion image fusion wavelet transform and re- structure of fused images. experimental results demonstrate the effectiveness of the method. Platform: |
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Author:张凡 |
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Description: 包含了三个图像处理函数，其一是图像叠加产生模糊效果，其二是与高斯算子做卷积，其三是模糊增强。另外，还包含了在同一个界面上同时显示多幅图像的函数，便于观察输出。(Contains three image processing functions, one is the image overlay to produce fuzzy effect, the second is to do with the Gaussian operator convolution, the third is fuzzy enhancement. In addition, it also contains the same interface at the same time display multiple images of the function, easy to observe the output.) Platform: |
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Author:岚
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Description: 先进行高斯金字塔分层，在拉普拉斯分解，最终将两幅局部有模糊的图像进行融合，最后显示清晰图像。(First Gaussian pyramid stratification, in Laplace decomposition, the final two local fuzzy image fusion, and finally show a clear image.) Platform: |
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Author:ruiruihaha
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