zhongxinchafenfa

说明：  利用matlab对图像进行双线性插值处理，具有良好的实现效果，亲测亲试。(Using matlab to process the image by bilinear interpolation, it has a good effect to realize the pro-test.)

社会力模型，基于社会力的行人流模型，包括行人间作用力，行人与墙体等障碍物的作用力等。(Social force model)

说明：  将小波和各向异性扩散结合，对超声图像进行去噪。效果较好(The combination of wavelet and anisotropic diffusion for ultrasonic image denoising. Better)

去噪算法matlab源码三合一，包含BLS-GSM，BM3D，NLM(Denosing algorithms 3 in 1, include BLS-GSM+ BM3D+ NLM)

双线性插值图象的旋转源码，程序结合易语言位图操作支持库和图像格式转换支持库，调用API函数和置入汇编代码，实现双线性插值图象的缩放旋转。(Source bilinear interpolation image rotation , combined with easy language program bitmap operations support libraries and image format conversion support library , call the API function and into assembly code , zoom in rotation bilinear interpolation image.)

使用CGAL库对人体三维网格分割并且把分割后的网格展开到一个平面上(using cgal to segment a mesh an expend )

Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.

随着红外成像技术的发展，红外小目标检测在军事领域有越来越重要的应用。以此为背景，研究了天空、海面、大地等各种背景下红外视频中弱小目标的检测问题。 (With the development of infrared technology,dim small targets detection play a more and more important part in the military. The problem of dim small targets detecting in a infrared video with the background of the sky,the sea and the groundhas been studied. )

编程实现大津（Otsu）自动灰度阈值分割算法的程序(Programming (Otsu) automatic gray Otsu threshold segmentation algorithm)

用matlab实现不规则光照下自适应阈值的前景背景分离算法。文件里面附测试图像。(Matlab implementation to use irregular light of the prospects for adaptive threshold background separation algorithm. Test image files attached inside.)