dengxiaoshishu

一个简单圆，有圆心半径，已知半径，求周长和面积。已知某点求是否在圆上。(A simple circle, a circle radius, known radius, strives for the pupil. Known to some point whether in the round.)

使用规定分布生成空间随机噪声，可以选择噪声类型和参数。(Using the prescribed distribution to generate spatial random noise, we can choose noise types and parameters.)

光伏逆变系统中的最大功率点跟踪算法，采用的是快速收敛的改进的变步长的电导增量方法，由s函数编写(maximum power point tracking method, using variable step size inc method,written by s-function)

本程序实现了对指纹图像的预处理、特征提取，效果很好，有图有程序，可以直接使用。(This procedure to achieve the fingerprint image preprocessing, feature extraction, the effect is very good, there are plans to have procedures can be used directly.)

说明：  通过多尺度分解，实现医学图像的图像融合，达到更好的效果(Through multi-scale decomposition, medical image fusion can be achieved to achieve better results.)

Keystone Transform in ground moving target detection，it s very useful new comer(Keystone Transform in ground moving target detection)

卡尔曼滤波的一个典型实例是从一组有限的，包含噪声的，对物体位置的观察序列（可能有偏差）预测出物体的位置的坐标及速度。在很多工程应用(如雷达、计算机视觉)中都可以找到它的身影。同时，卡尔曼滤波也是控制理论以及控制系统工程中的一个重要课题。(A typical example of the Kalman filter, from a limited set, containing the noise, the position of the object observed sequence (there may be deviation) to predict the coordinates of the location and speed of the object. Its presence can be found in many engineering applications (such as radar, computer vision). Meanwhile, the Kalman filter is control theory, and control is an important topic in systems engineering.)

对于vc++中经典的画线画图示例程序.对于类的控制和画线算法的实现.代码简洁明了(For vc++ in a classic example of procedures for drawing a line drawing. The type of control and painting line algorithm. Concise code)

图像分割关系到目标识别的精度，是目标检测的关键一步，其中提供了一些图像分割的代码，主要为otsu算法的分割方法，很实用(s related to the accuracy of target recognition, image segmentation is a critical step in target detection, provides some image segmentation code, very useful)

图像时频变换实例，包括FFT变换、FFT逆变换、DCT变换、DCT逆变换、DWT变换、DWT逆变换、WHT变换、WHT逆变换等。(Examples of image frequency conversion, including FFT transform, FFT transform, DCT transform, DCT transform, DWT transform, DWT transform, WHT transform, inverse transform, etc..)