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RI_lrt
- 图像的变化通常没有先验信息，或者即便有也很难用参数模型描述它，当两者都知道时，我们可以得到分布的似然比，构造比检验。-Image changes often do not have a priori information, or even if there is difficult to describe it with the parameter model, when both are known, we can get the distribution of the likelihood
myGM1_1
- 灰色预测模型，不同的是该程序具有所有参数的检验。-Grey prediction model, the difference is this procedure has all the parameters of the inspection.
SAR
- 以MSTAR为实验数据，对SAR影像进行杂波统计分析研究。利用高斯、瑞利、对数正态、LN、gamma函数模型对其拟合，通过K-S方法检验拟合精度-The MSTAR the experimental data, statistical analysis of SAR images of clutter. Gaussian, Rayleigh, lognormal, LN, gamma function model to its fitting, fitting by means of check
_draw_in_img
- 手动匹配特征点，特征点比较少，重建简单模型很有用，也可以用于检验对比其他方法。-Manual matching of feature points, feature points is relatively small, the reconstruction of a simple model is useful, other methods can be used to test contrast.
xuguanjianyan
- 仿真通信模型中的序贯检验的仿真模型，并计算概率-Simulation of communication model in sequential tests simulation model, and calculate the probability
deepsaldet-master
- 基于深度学习的图像显著性检验，在cnn框架的基础上，采用全局与局部的多上下文模型，取得了最优效果。-Significant test image based on the depth of learning, based on cnn framework on the use of global and local multi-context model, and achieved the best results.