image sinusoidal noise filtering. Quite a procedure!

自己编写的图像处理工具，比较全面，包括视频处理，图像融合等，可以作为开发参考-I have written image processing tools, a more comprehensive, including video processing, image fusion and so on, can be used as the development of reference

在总结和分析现有运动目标检测与跟踪方法的基础上，建立了一个视频序列采集的动态目标检测及跟踪系统-In summing up and analysis of existing moving target detection and tracking method based on the establishment of a video sequence collected dynamic target detection and tracking system

图像处理代码，主要是人脸的自动识别和检测。-Image processing code, mainly because they face the automatic identification and testing.

批量的将DICOM文件转化为8位png文件-Quantities will be converted into DICOM files 8 png files

彩色图像做差运算，并分RGB 以及亮度进行直方图统计，计算得到二阶导数为零的点，完全自主创作，效果理想-color image so poor operation and the hours for RGB and luminance histogram statistics, calculated second-order derivative of the zero point, entirely independent creation, the result is satisfactory

根据网址http://www.caip.rutgers.edu/riul/research/code.html 提供的代码，对Meanshift图像分割进行了封装实现，提供了Matlab和C两种调用接口。-According to Web site http://www.caip.rutgers.edu/riul/research/code.html the code provided on the packaging Meanshift image segmentation was achieved, providing two kinds of Matlab and C Call Interface.

近邻法的matlab 程序,采用1近邻法,多分剪辑的方式,有助于对近邻法的理解-Matlab neighbor law procedures, the use of one neighbor method, multi-way sub-clips, contribute to the understanding of neighbors Law

exp11.m为计算出一幅灰度图象的直方图以及对灰度图像进行简单的灰度线形变换的源程序 exp12.m为将一副灰度图像转换成二值图像源程序 exp13.m为对一幅图像进行直方图均衡化的源程序-exp11.m to calculate an image

实现了3D模型变形（morphing）的隐函数插值算法。并有Matching Cube算法网格化模型。这个程序花了我不少时间编写。-Achieved a 3D model of the deformation (morphing) of the implicit function interpolation algorithm. And Matching Cube Mesh count model. This process gave me a lot of time to prepare.

awimg=rgb2gray(im1) tic [accum, circen, cirrad] = CircularHough_Grd(rawimg, [20 30],5,50) circen toc figure(1) imagesc(accum) axis image title( Accumulation Array from Circular Hough Transform ) figure(2) imagesc(rawimg) colormap( gray ) axis image hold on plot(circen(:,1), circen(:,2), r+ ) for k = 1 : size(circen, 1), DrawCircle(circen(k,1), circen(k,2), cirrad(k), 32, b- ) end hold off title([ Raw Image with Circles Detected , ... (center positions and radii marked) ]) figure(3) surf(accum, EdgeColor , none ) a