VMD

说明：  一、课题介绍 本设计为基于MATLAB的火焰烟雾火灾检测系统。传统的采用颜色的方法，误识别大，局限性强。结合火焰是实时动态跳跃的，采用面积增长率，角点和圆形度三个维度相结合的方式判断是否有火焰。该设计测试对象为视频，通过下一帧和上一帧的差异发现是否有火情，并可发出语音报警。本设计带有一个人机交互式GUI界面，界面友好。是个不错的bishe选题。 二、算法流程 结合火焰的面积增长率，角点和圆形度三个维度综合判断。并且得出每帧图像火焰部分的该三个参数，实时显示在GUI上。 三、GUI界面设计(This design is based on MATLAB flame smoke fire detection system. The traditional method of using color has large error recognition and strong limitation. Considering that the flame is real-time and dynamic jumping, the area growth rate, corner and roundness are combined to determine whether there is flame. The test object of the design is video. Through the difference between the next frame and the previous frame, whether there is fire is found and the voice alarm can be sent out. This design has a personal computer interactive GUI interface, the interface is friendly. It's a good choice for BiShe.)

PLSA源码，并附有PLSA理论在图像自动标注中的应用，有几篇具有代表性的论文，论文中讨论了PLSA的改进并应用在图像处理中。(PLSA source code, with PLSA theory in automatic image annotation application, some of the representative papers, thesis discusses the PLSA improvement and application in image processing.)

说明：  这是我编写的关于计算机视觉中的一种重要的变换---射影变换的matlab源代码，希望对您有用。(This is what I prepared on computer vision in an important transformation of the projective transformation--- matlab source code, I hope useful for you.)

Ocr related documentataion

说明：  用MATLAB进行图形样板匹配的源程序，（开发平台MATLAB7.0）(Using MATLAB graphics to match the source model, (development platform MATLAB7.0))

讲解滤波与二值图像处理，比较简单的图像处理源码(Explain the filtering and binary image processing, image processing source code is relatively simple)

用点扩散函数psf对波前编码系统产生的模糊图像进行复原得到清晰图像(use the PSF of wavefront coding system to restore its blurred image )

Real-time Specular Highlight Removal实现论文“Real-time Specular Highlight Removal Using Bilateral Filtering”中算法(Real-time Specular Highlight Removal Papers " Real-time Specular Highlight Removal Using Bilateral Filtering" in the algorithm)

I WANT SOURCE CODE FOR IMAGE SEGMENTATION

老外写的图像分割matlab代码，内有详细的说明(Foreigner to write matlab code for image segmentation, which is described in detail)