snake_examples

基于感兴趣区域图象处理的matlab程序和大家共享 (Region of interest based on the matlab image processing procedures and U.S. share)

基于形态学的在Matlab下进行的图象骨架化及恢复,其中包含骨架化函数Frame和恢复函数refresh(based on the morphology of Matlab under the images of the skeleton and the restoration, which includes Frame function of the skeleton and the restoration of function refresh)

基于ART算法的CT图像重建 刚找到的，不知能不能用(CT image reconstruction based on ART algorithm)

jpeg压缩matlab程序，可供学习图像视频编解码的同学学习(jpeg compression matlab program for learning image video codec students to learn)

说明：  对遥感影像融合质量评价的程序，输出均值 标准查 信息熵 相关系数 光谱保真(To evaluate the fusion quality of remote sensing image, output the mean value standard to check the spectral fidelity of entropy correlation coefficient)

国内权威大家写的信号检测理论,主要讲述了信号理论和估计。(the book is literally book for signal processing,it was use for master students who want to learning signal detect and estimate)

查询车辆油耗信息,根据车号,司机,日期等(Query the vehicle fuel consumption information)

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.)

本程序和实现对一幅包含人脸的bmp图像进行光纤补偿，对人脸区域进行检测实现人眼和人嘴的检测与定位，最后勾绘出人脸的特征以实现人脸检测的目的。(This procedure and implementation of a compensate the fiber contains face of BMP image, the face region detection to realize human eyes and mouth detection and positioning, finally drew out the facial features to achieve the purpose of face detection. )

车辆牌照识别，比较简单的识别系统，可能由于数据库的不足，导致其还存在一些误差(Vehicle license plate recognition, a relatively simple recognition system, may be due to the lack of database, resulting in some errors.)