kmeans-image-segmentation

一个很好类子来说明类模板的使用，可以比较明了类模板的使用方法，最好进行单步调试(use of class template )

一段很好的图像放大缩小程序，解码后可以运行。(A very good image magnification narrow program, the decoding can run. )

DDB 图像编程 实现图像的打开，保存，显示，一般处理等功能(DDB image programming to achieve the image open, save, display, general processing and other functions)

双目立体视觉，采用两张图像恢复三维形状，外国人的一个开源项目。(System Prototype to make 3D reconstruction solution using stereo images. It works with common cameras and not require large amount of memory during the images processing. It provides a low cost solution to educational environments with low budgets. )

实现了读取一个bmp图片的傅里叶变换 使用C语言实现 读取一个 24位 图 转换为8位灰度图后，进行傅里叶变换处理（二维） 采用快速傅里叶变换——蝶形算法 北航图像处理作业~需要进一步交流 请联系qq 422563357(Reads a bmp image to achieve a Fourier transform using C language Northern image processing operations)

kmeans算法实现，对图像处理。亲自使用过，可用，供参考。(Kmeans algorithm for image processing.)

利用棋盘格对相机进行标定，加入误差判定及像素空间到实际物理空间的距离计算(The camera is calibrated by checkerboard, the error is added and the distance of pixel space to the actual physical space is calculated)

该源程序可以实现BMP图片的压缩(BMP pictures)

说明：  解决一些图像处理的小问题，具体的划分和问题，文件里有提到(Solve some small problems in image processing)

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. )