MMSE

这是一个基于偏微分方程的图像处理问题的非线性平滑滤波器的程序!(This is a partial differential equations based on the question of image processing procedures for non-linear smoothing filters!)

该程序是将以com组建的形成Matlabe与MFC相结合，实现画图的效果(The program is connected matlabe to mfc by the form of the com, the effect of drawing )

说明：  实现HDB3编码，HDB3码是AMI码的一种改进型，其目的是为保持AMI码的优点而克服其缺点，使连“0”个数不超过3个。(Achieve HDB3 encoding, HDB3 code is a modified AMI code, its purpose is to maintain the benefits of AMI code to overcome its shortcomings, so that even the &quot 0&quot the number of not more than 3.)

auto correlation by matlap

说明：  matlab 1_Jacobi迭代解法 matlab 1_Jacobi迭代解法matlab 1_Jacobi迭代解法 matlab 1_Jacobi迭代解法 matlab 1_Jacobi迭代解法(matlab 1_Jacobi迭代解法)

应用matlab仿真改进A*算法在三维地形上的寻径过程(Application matlab simulation on improved A* algorithm in three-dimensional terrain routing process)

说明：  88行SIMP拓扑优化方法程序，采用移动渐近线法MMA求解器(The code of the classical SIMP method implemented by the MMA solver.)

说明：  该仿真模型仿真了异步电机发生三相短路时，线路中三相电压及电流的变化(The simulation model to simulate the three-phase induction motor short circuit occurs, the line in the three-phase voltage and current changes)

运动目标检测的程序是有关车辆的，其中涉及到几种方法，有一定的参考价值。(Sports target detection program about the vehicle，reference to several methods and it is worth reading.)

1 SIFT 发展历程 SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 3 SIFT算法的主要特点： a) SIFT特征是图像的局部特征，其对旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性。 b) 独特性(Distinctiveness)好，信息量丰富，适用于在海量特征数据库中进行快速、准确的匹配[23]。 c) 多量性，即使少数的几个物体也可以产生大量SIFT特征向量。 d) 高速性，经优化的SIFT匹配算法甚至可以达到实时的要求。 e) 可扩展性，可以很方便的与其他形式的特征向量进行联合。 4 SIFT算法步骤： 1） 检测尺度空间极值点 2） 精确定位极值点 3） 为每个关键点指定方向参数 4） 关键点描述子的生成 本包内容为sift算法matlab源码(1 SIFT course of development SIFT algorithm by DGLowe in 1999, the perfect summary of 2004. Later Y.Ke its description of the sub-part of the histogram with PCA instead of its improvement. 2 the SIFT main idea The SIFT algorithm is an algorithm to extract local features in scale space to find the extreme point of the extraction location, scale, rotation invariant. 3 the main features of the SIFT algorithm: a) SIFT feature is the local characteristics of the image, zoom, rotate, scale, brightness change to maintain invariance, the perspective changes, affine transformation, the noise also maintain a certain degree of stability. b) unique (Distinctiveness), informative, and mass characteristics database for fast, accurate matching [23]. c) large amounts, even if a handful of objects can also produce a large number of SIFT feature vectors. d) high-speed and optimized SIFT matching algorithm can even achieve real-time requirements. e) The scalability can be very convenient fe)