cubicfive

FK滤波分析，设计窗口！！！！！！！！！！！(FK filtering analysis, design window! ! ! ! ! ! ! ! ! ! !)

matlab下图文并茂的分析了fft算法，分析了如何从频谱如何看频率，相位，幅值(Here to enter through the abundant of matlab analyzed fft, and analyzed from the frequency spectrum how to see that phase, should be translated text)

LTE中速率匹配的代码，包括子块交织，比特搜集，比特选择和修剪(The code rate matching in LTE, including the sub-block interleaving and bit gather, bit selection and pruning)

对图像进行纹理处理，为图像识别、目标跟踪创造条件。主要分析：惯性矩、熵以及局部平衡性。(The image texture processing for image recognition, target tracking and create conditions. Main analysis: moment of inertia, entropy, and local balance.)

VisualC++数字图像处理技术详解第2版光盘-第四章(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter Four)

基于OpenCV实现的图像剪切，截取小软件，对学习OpenCV的朋友比较有帮助(OpenCV based on the realization of the image shear, the interception of a small software, to learning more OpenCV friends help)

VC++图像平滑处理+程序源码，实现图象的平滑（去噪声），锐化功能，测试时请在C盘下放一个测试用的Bmp图片，路径：C: est.bmp，没有的话没法测试。如上图所示是水平平滑处理，可以对比下，不过感觉颜色失真了，有兴趣的看一下。命令行编译过程如下： 　　vcvars32 　　rc bmp.rc 　　cl smooth.c bmp.res user32.lib gdi32.lib(VC++ image smoothing+ program source code, to achieve a smooth image (to noise), sharpening function in the C drive when testing a test of decentralization Bmp picture, path: C: test.bmp, no words can not test . Is shown above the level of smoothing, you can compare the next, but the feeling of color distortion, interested look. Command-line compiler as follows: vcvars32 rc bmp.rc cl smooth.c bmp.res user32.lib gdi32.lib)

说明：  该书介绍的用VC实现图像处理，其中包括一些图像处理知识的介绍(The book described the realization of image processing using VC, including some knowledge of image processing introduction)

源点定位程序，可用于检测连通域等功能，代码是c/c++的，放心使用。(Source point positioning procedures, can be used to detect other functions connected domain, the code is c/c++, and ease of use.)

针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下，b标状态估计不稳定和b标可观测性车弱的问题，提出了一种基于序贯融合的粒子PH D滤波方法，利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波，丙将红外传感器滤波结果作为雷达的预测值，然后利用P达观测的数据进行更新，这样通过雷达和红外传感器交替上作保证目标状态的可观测性，从滤波器输出结身即可得到目标的状态信息仿真结果表明，在虚警、漏检和密集目标环境下，该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )