kuaipipei

本程序用于CurveLab-2.0水印技术的处理．(the procedures for CurveLab-2.0 watermarking technology management.)

基于主成分分析的遥感图像融合算法，融合结果质量较好。(The fusion algorithm of remote sensing image based on principal component analysis has better fusion result.)

十字路口车道线提取算法。c++开发。特别针对十字路口电子警察系统开发。(road line fetch)

用kfcm算法实现数据的聚类和分类效果。(Kfcm algorithm is used to achieve data clustering and classification results.)

该代码是对图像进行二值化处理，进行图像分割(The code is to binarize the image, the image segmentation)

高斯混合模型的最大期望迭代求解算法,可用于图像区域灰度分布估计(Expectation maximazation(EM) for Gaussian mixture model(GMM) )

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.)

说明：  python实现双目立体匹配SGBM算法(SGBM algorithm for binocular stereo matching based on Python)

二阶B样条小波边缘检测 处理后的图像为多尺度下的边缘检测图像 还需要进行边缘图像的匹配(Second-order B-spline wavelet edge detection image processing for multi-scale edge detection to the edge of the image also need to match image)

说明：  无下采样的contourlet变换阈值去噪； 无下采样的contourlet变换matlab7.0工具箱(No down-sampling of Contourlet Transform Thresholding Denoising without down-sampling the transform Contourlet matlab7.0 Toolbox)