GS

粒子滤波与PHD多目标跟踪比较，三目标分别进行粒子滤波和marginalPHD滤波跟踪比较结果。(PHD particle filter multi-target tracking compared with the three goals were to filter tracking particle filter and marginalPHD comparison.)

k-均值聚类算法实现灰度图像分割，输入图像矩阵和聚类中心个数，返回为最终的聚类中心和图像中每个像素所属类的编号（对应于图像矩阵）(k-means clustering algorithm to achieve gray-scale image segmentation, the input image matrix and the number of cluster centers, the return for the final image of the cluster centers and their respective categories in each pixel number (corresponding to the image matrix))

光流法的程序，利用传统光流法思想，亲测有效，效果不错(Optical flow method program, pro-test, good results)

这是一个matlab关于分数阶混沌的源程序代码，通过该程序可画出混沌图，这对初学者很有帮助。我已经调试过了。(This is a matlab fractional order to chaos on the source program code, through the program can draw a picture of the fractional order, to beginners helpful. I have the commissioning)

sar 图像边缘检测算子，代码简洁，能够运行成功。(sar image edge detection，it s simple and useful 。)

图像读取与显示，图像二值化，边缘检测，边缘追踪(Read and display image, image binarization, edge detection, edge tracking)

用微分自相关法实现运动模糊图像模糊参数估计的功能，是目前较少公布的微分自相关法实现模糊参数估计。(Autocorrelation method with a differential image to achieve motion blur blur parameter estimation function, the differential is less published method fuzzy autocorrelation parameter estimation.)

利用随机霍夫变换进行圆的检测，matlab编写(Randomized Hough transform for circle detection, Matlab prepared)

用FCM 对自然图像 sar图像以及纹理图像进行快速分割 ，用一维和二维直方图分别对图像进行特征映射 从而快速的对图像进行分割 包含结果截图 (By FCM sar image of natural images quickly and texture image segmentation, with one-dimensional and two-dimensional histogram of the image feature maps were so fast image segmentation screenshot contains the results)

本文主要介绍遗传算法的基本理论，叙述遗传算法在图像增强的的主要应用，即将原始图像变得更加清晰，特征变得更加明显。 现今关于图像增强的算法有很多，而这些算法大多是基于退化函数或者点扩展函数的知识进行图像处理的。当图像出现模糊或噪声影响大时，设计出的图像清晰化的效果肯定不够理想，因此有必要对图像进行增强处理。于是，可利用到遗传算法这种成熟稳定的仿生物进化的全局寻优算法，进行图像增强，由于遗传算法控制参数少、自适应度高，将选择该方法对图像退化分别进行不同的清晰化处理. 这样既增强了图像的对比度,又克服了传统直方图均衡化处理所造成的灰度级损失等缺点。 通过实验去表明，遗传算法从全局寻优的角度出发，能够较为精确地估计退化系统函数。和传统的线性增强、直方图均衡进行比较。实验结果表明该方法能改善原图像视觉效果，便于之后的图像分析。 (This paper mainly introduces the basic theory of genetic algorithm, genetic algorithm (ga) in the main application of image enhancement, the original image is more clear, characteristic becomes more obvious. Today about the image enhancement algorithm are many, and most of these algorithms is based on the degradation function or the knowledge of the point spread function of image processing. When the image appears fuzzy or noise influence, designed the image clarity of effect must not ideal, therefore it is necessary to enhance the image. Then, genetic algorithm is available to the mature and stable evolution of the global optimization algorithm, for image enhancement, due to less genetic algorithm control parameters, since the high fitness, will choose the method of image degradation, respectively, to manipulate the different motivation. It can enhance the image contrast, and overcome the traditional histogram equalization processing grayscale loss caused by the faults. Throug)