LBP: local Binary Patterns翻译成中文就是局部二进模式,是图像的一个很重要的纹理特征(Texture Feature)。这个特征的提出 … blog.csdn.net|基于7个网页 2. 纹理特徵 2.1.1纹理特徵(Texture Feature) 102.1.2 色彩讯号 ( Color Feature ) 112.2 寻找边点 ( Edge Detection ) 112.2.1 常用边点运... ...
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Texture representation, i.e., the extraction of features that describe texture information, is at the core of texture analysis. After over five decades of continuous research, many kinds of theories and algorithms have emerged, with major surveys and some representative work as follows. The majorit...
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Densely sampled 3D blocks of a dynamic texture are first normalized to have zero mean and are then filtered by 3D filters that are learned in advance. To preserve more of the structure information, the filter response vectors are decomposed into two complementary components, namely, the signs ...
The combination of co-occurrence texture features (contrast, entropy, mean, standard deviation and correlation included) and edge density are used to substitute for spectral features as classification features. A mixture density function is employed to represent classes' distribution in texture spaces. ...
where m is the mean vector of {vi}i=1,…,n. Since covariance matrices do not live in the Euclidean space, the difference between two matrices would not quantify the similarity or dissimilarity between the corresponding regions. Under the Log-Euclidean Riemannian metric, it is possible to measu...
Results of monomineralic simulations with the same crystallization parameters (i.e., total crystallization time, crystal growth rate, domain length) show that the number and mean sizes of crystals are sensitive to the degree of spatial and temporal discretization within the model. For a particular...
The correlation coefficients were estimated between the mean AUC values and mean texture values corresponding to these five sets with the number of projections \(P \in \{3, 7, 11, 25, 45\}\). Comparing once more this AUC data with the texture features of these filtered images, we find ...
mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] These were the training transformations: train_transforms = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor()]) and those the validation transformations: val_transforms = transfo...