Hyperspectral-Data-Classification

整合了不完整张量的平滑平行因子分解和最小化张量秩模型的有效选择的张量补全，用于图像处理，(The proposed method admits significant advantages, owing to the integration of smooth PARAFAC decomposition for incomplete tensors and the efficient selection of models in order to minimize the tensor rank.)

matlab最小二乘法曲面拟合程序可以得到函数具体解析式(Matlab least squares surface fitting program can get the specific analytic expression of the function)

说明：  DOG高斯差分实现物体识别中的特征提取,对不同尺度空间的图像进行差分，从而获得描述物体的特征值(difference of gaussian to achieve the feature point exaction of object recognition)

图片黑白化，使彩色图片实现黑白化功能，还能还原色彩(Black and white pictures )

不同信噪比下周期FRFT和FRFT两种方法对LFMCW信号的检测能力对比(FRFT different SNR period and the comparison of the two methods FRFT LFMCW signal detection capability)

基于主成分分析与灰度（颜色）、纹理（灰度共生矩阵）的图像分类matlab程序（附示例图像）。(Image classification matlab program based on principal component analysis and gray (color), texture (GLCM) (with sample images).)

利用matlab来实现人脸识别，人脸识别模板匹配，(Face Recognition Template Matching)

用共轭梯度下降算法 求解图像压缩感知 并进行比较(Conjugate gradient descent algorithm for image compression and compared perception)

说明：  合成孔径雷达点目标仿真，线性变标算法（CSA），注释完整，可以直接运行，信号模型参考书目《合成孔径雷达成像算法与实现》。(Synthetic aperture radar point target simulation, linear scaling algorithm (CSA), complete annotations, can be run directly, the signal model reference bibliography "Synthetic aperture radar imaging algorithm and implementation".)

摘　要:舌象中的裂纹是中医舌诊中的重要内容。由于拍摄条件的不同,舌象质量有较大差异,传统的阈值选取方法并不 适用。文中提出一种舌象裂纹检测的自适应阈值选择方法。该方法利用舌象的L 3 a 3 b 色彩特征及区域的分裂- 合并, 对舌象进行区域分割,自适应地选取舌中部区域的色彩值作为阈值,对整个舌象进行裂纹提取。经验证,本方案对不同的 舌象能较好地提取出裂纹,实现舌象裂纹诊断的客观化。(Tongue’ s crack is a very important part of herbalist doctor diagnosis. For the different screen conditions , there are many differ2 ences on the tongue’ s quality. It is not effective to use the t raditional threshold ext raction algorithm. In order to solve this problem , pro2 pose a new adaptive threshold algorithm. Present the segmentation of the image of tongue by using the feature on tongue in the L 3 a 3 b color space and the split- combining algorithm. Then use the threshold , which is the color value ext racted automatically in the middle of the tongue , to detect the crack of the whole tongue. The experiments show that this approach is able to distill the crack effectively to different tongue images , and achieve the objectivity of the diagnosis based on the tongue’ s crack.)