Description: 实现纹理模式的LBP特征表示及分类。
实现一种基于局部二值模式LBP（Local Binary Pattern）的多分辨率灰度尺度及旋转不变性的纹理分类方法-LBP texture model to achieve that as well as the breakdown characteristics. The realization of a model based on local binary LBP (Local Binary Pattern) Multiresolution gray-scale and rotation invariant texture classification Platform: |
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Author:张阳军 |
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Description: 利用LBP纹理模型进行纹理分类，其论文<..Rotation Invariant Texture Classification using LBP Variance (LBPV) with Global Matching>发表在2009年Pattern Recognition，效果很不错。-Texture classification by LBP texture model. The related paper <..Rotation Invariant Texture Classification using LBP Variance (LBPV) with Global Matching> has been published in Pattern Recognition 2009. Platform: |
Size: 12288 |
Author:宁纪锋 |
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Description: Gabor小波变换技术对医学CT图像进行纹理特征分类时，由于图像拍摄角度的变化会造成分类的误差。针对以上问题，在Gabor小波变换的基础上提出一种用于分析旋转不变医学图像的方法。该方法采用旋转规范化，即特征元素的循环移位使规范化后所有的图像都具有相同的主方向。实验结果表明，加入旋转规范化循环算子的Gabor小波变换在医学CT图像纹理特征分类时能够达到较好的精确度。-Gabor wavelet transform lacks in its ability to classify the medical CT image if it’s rotation invariant image. Aiming at the problem, an approach is presented for rotation invariant medical texture classification based on Gabor wavelet transform. Rotation normalization is achieved by circular shift of the feature elements, so that all images have the same dominant direction. Experimental result shows that Gabor wavelet transform with circular operator of rotation normalization has well precision to classify the medical CT image. Platform: |
Size: 407552 |
Author:li |
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Description: 来自<Multiresolution gray-scale and rotation invariant texture
classification with local binary patterns>,LBP，用于图像的纹理特征分析-From <Multiresolution gray-scale and rotation invariant texture
classification with local binary patterns>,LBP, used for image textural feature analysis Platform: |
Size: 2048 |
Author:汪洋 |
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Description: （1）计算图像中每个像素点的LBP模式(等价模式,或者旋转不变+等价模式)。
（2）然后计算每个cell的LBP特征值直方图，然后对该直方图进行归一化处理(每个cell中，对于每个bin,h[i]/=sum,sum就是一副图像中所有等价类的个数)。
（3）最后将得到的每个cell的统计直方图进行连接成为一个特征向量，也就是整幅图的LBP纹理特征向量；
然后便可利用SVM或者其他机器学习算法进行分类识别了。((1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode).
(2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images).
(3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture.
Then, SVM or other machine learning algorithms can be used for classification and recognition.) Platform: |
Size: 68608 |
Author:刘宇123 |
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Search - rotation invariant texture classification