facedetection

本设计主要目的是实现染色体的识别计数。首先对图形进行预处理，将图像转化为灰度图、滤波去噪，然后对图像进行二值化，合理选取阈值，进行直方图阈值处理，最后去除图像中面积过小的杂点，最终实现识别计数。(The main purpose of this design is to achieve the identification of chromosome count. The graphical pre-processing, the image is converted to grayscale, filtering denoising, image binarization, reasonable selection of the threshold, histogram thresholding, and finally remove the image area is too small noise , and ultimately identify the count.)

mser算法（Maximally Stable Extremal Regions）代码，非文章作者写的，但是还不错，matlab实现。(mser algorithm (Maximally Stable Extremal Regions) code, non-article the authors write, but also good, matlab realize.)

说明：  在IDL中将.nc格式的数据读取并绘图，文件夹中附有示例数据。(Reading and drawing data in.Nc format in IDL)

说明：  雷达模拟LFM脉冲信号，模拟目标回波（目标距离50km，速度100m/s）和噪声信号（信噪比）(Radar simulated LFM pulse signal, simulated target echo (target distance 50km, speed 100M / s) and noise signal (signal-to-noise ratio))

详细介绍了常用的四中粒子重采样算法,并给出了他们的性能比较,对研究改进型粒子算法很有帮助,减轻很多工作负担(Details of the four commonly used particle resampling algorithm, and gives the performance of their comparison, the study of modified particle algorithm useful to reduce the burden of a lot of work)

利用K均值聚类的方法实现人体的检测，混合高斯模型建立背景并实时更新(The use of K-means clustering method to achieve the human body detection, Gaussian mixture model to establish the background and real-time updates)

前景检测 对动态背景进行多角度监测 动态背景检测(Foreground detection is used to monitor dynamic background from different angles)

基于人类视觉系统的融合图像质量评价算法， 频域CFS滤波，分区加权(The CFS filter for the frequency domain ，fused image quality evaluation algorithm based on human visual system, partition-weighted)

结构张量来进行自适应正则化图像重建，单幅图像重建(Structure tensor adaptive regularized image reconstruction, a single image reconstruction)

Hilditch算法是现有的一种细化算法,它适用于 输入图像为0和1的二值图像。像素为1的区 域是需要细化的部分, 像素为0 的区域是背景(Hilditch thinning algorithm is an existing algorithm, it applies to the input image is 0 and 1 of the binary image. Pixel region 1 is a need to refine the part of the pixel area is the background for the 0)