Doppler_Center_RadonTransform

雷达识别外推程序，可用于多普勒天气雷达等。(Radar identification extrapolation procedure can be used for Doppler weather radar.)

radon变换Redistribution and use in source and binary forms, with or without modification, are permitted provided that the followin(OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.)

深度搜索算法用于一幅二值图像，指定开始点和目标点(deep first search method is used for binary image,with assigned start and end point)

图像的纹理分析应用实例,做图像处理的同志们可以参考一下哟(image texture analysis application examples and do image processing comrades can make reference to yo)

usps数据库为标准手写数据库，此为mat数据文件，含训练集和测试(USPS database is a standard handwritten database, which is a mat data file, including training set and test)

主要功能是实现相机标定，以及实现反投误差，实现特征点的提前。(The main function is to achieve camera calibration and error to achieve the anti-investment, to achieve the feature points in advance.)

EM算法。问题背景是，有N条长为L的DNA序列，每条序列里面有一条长尾K的目标序列，但不知道他的位置，目标序列内的核苷酸服从foreground product multinomial distribution，非目标序列服从background multinomial distribution，改程序意在求解两种分布的参数。(EM algorithm. Background to the issue is that there are N pieces of length L of DNA sequences, each sequence which has a target sequence Nagao K, but do not know his position, the target nucleotide sequence within obedience foreground product multinomial distribution, non-target sequence obey background multinomial distribution, the program is intended to change the parameters in solving the two distributions.)

matlab　ＰＣＡ源代码 有中文注释(source code matlab PCA has Chinese Notes)

mimics10中文教程，主要用于二维图形采集以后的三维重建(Mimics10 Chinese tutorial, it is mainly used for 2 d graphics after acquisition of 3 d reconstruction)

MATLAB在图像处理方面的优选教材，做遥感图像处理的同学可以下载学习(MATLAB in the optimization of image processing materials, remote sensing image processing students can download learning)