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这是图像拼接大神的在ICCVC上的PPT报告，思路清晰，受益匪浅，(PPT report in God s image stitching on ICCVC and clear thinking, benefit,)

说明：  可改变中心时移，相位，主频的雷克子波，自己在源程序上加减时间，改变主频，加减相位即可(ricker wavelet which time,phase and frequence can be changed )

将脉冲耦合神经网络PCNN和聚类结合实现灰度图像分割(will PCNN PCNN combining clustering and gray image segmentation)

易语言动画地图编辑器源码，易语言写的图形图像编程，很好的参考。(Easy language animated map editor source code, easy graphic language programming, a good reference.)

说明：  Z源逆变器开路三相SVPWM调制，注入了三次谐波分量。(Open-circuit Three-phase SVPWM Modulation of Z-source Inverter)

图像分割及二值化源码。该程序是交通标识识别的一部分。(Image segmentation and binary code. The traffic sign recognition program is part of.)

斐波那契数的实现算法及分析,标准化实验报告样本()

ostu与鱼群算法相结合，并且用matlab实现(ostu algorithm combined with the fish, and using matlab)

图像检测的算法，Faster R-CNN算法，先对整张图像进行卷积计算，然后通过感兴趣区域池化层(RoI Pooling Layer)将选择性搜索算法推荐出来的候选区域和卷积网络计算出的特征映射图进行融合，得到候选区域对应的特征矢量，这种共享卷积计算的操作极大地减少了卷积计算的次数。而且这些特征矢量的维度统一，方便后续的分类工作。通过感兴趣区域池化层处理卷积特征，并将得到的特征送往两个并行计算任务进行训练，分类和定位回归。通过这些方法和改进的框架，Fast R-CNN 用更短的训练和测试时长，取得了比 R-CNN 更好的效果(Faster R-CNN algorithm first convolutes the whole image, then fuses the candidate regions recommended by the selective search algorithm and the feature mapping maps calculated by the convolution network through the RoI Pooling Layer to get the corresponding feature vectors of the candidate regions, which greatly reduces the number of convolution calculations. Moreover, the dimension of these feature vectors is unified, which facilitates the subsequent classification work. The convolution feature is processed by the pooling layer of the region of interest, and the obtained feature is sent to two parallel computing tasks for training, classification and positioning regression. Through these methods and improved framework, Fast R-CNN uses shorter training and testing time and achieves better results than R-CNN.)

基于小波的去噪算法，采用BAYES原理选取阈值，源代码，参考文献：Adaptive Wavelet Thresholding for Image Denoising and Compression(Wavelet-based denoising algorithm using the principle to select threshold BAYES, source code references: Adaptive Wavelet Thresholding for Image Denoisingand Compression)