DOE (2)

非下采样contourlet变换用于图像降噪，多尺度几何分析的应用。 (Contourlet transform non-down-sampling for image noise reduction, multi-scale analysis of the application of geometry.)

说明：  可实现正割法求解，牛顿法，迭代法，抛物线求根(Can achieve secant method, Newton method, iterative method, parabolic root)

基于mmdetection框架的深度学习目标检测框架(deep learning object detection based on mmdetection)

vtk碰撞检测程序，基于obb包围盒的碰撞检测程序，需要用cmake进行编译(vtk collision detection procedure, based on obb bounding box collision detection program needs to be compiled with cmake)

说明：  蚁群算法的另一种实现形式,通过全局的搜索,避免陷入局部解的改进程序(Another implementation form of ant colony algorithm, through global search, avoid falling into an improved program of local solution.)

说明：  最近一直在看matlab仿真WSN的资料，今天找到一个仿真LEACH协议的。其主要是为了仿真他自己提出来的分簇协议，并给出了matlab代码，同时给出leach的代码。有兴趣的可以研究一下leach 协议和deec的协议对比(I've been looking at the MATLAB simulation WSN recently, and today I've found a simulation LEACH protocol. The main purpose is to simulate the clustering protocol proposed by himself, and give the matlab code, and give leach code at the same time. Interested can study the LEACH protocol and DEEC protocol comparison)

说明：  基于CV模型的自动分割,能够对灰度图分割操作(image segmentation Level set Chan-Vase)

深度搜索算法用于一幅二值图像，指定开始点和目标点(deep first search method is used for binary image,with assigned start and end point)

irrilicht开源引擎，用于windows，mac等各个平台的图像开发(irrilicht engine， it‘s opensource)

说明：  UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。(Its influence has reached 70% in 2015. Then it became the baseline that most of the medical image semantic segmentation tasks, and inspired a large number of researchers to think about the U-shaped semantic segmentation network. In the aspect of natural image understanding, more and more SOTA models of semantic segmentation and object detection begin to pay attention to and use U-shaped structure, such as semantic segmentation, discriminative feature network (DFN) (cvpr2018), feature pyramid networks for object detection (FPN) (CVPR 2017), etc.)