中值滤波代码matlab-SMSOM:堆叠式多层自组织图

------------------------------------------------------------ # SMSOM-BM SMSOM-BM stands for Stacked Multi-layer Self-Organizing Map Background Model, which is a background modeling method presented in our paper **[1]**. This repository is a clone of our original work flow (source code, executable, tools, etc., explained below), and the experiments of **[1]** are based on it. Please see [project homepage][project homepage] for more information about SMSOM-BM. You can continue this README file to find how to use it. If you have any questions, comments, or bug reports regarding to SMSOM-BM, please contact zhaozj89@gmail.com, or pull a request. The file tree of this repository is: <pre> . ├── Debug │   ├── cudart32_50_35.dll │   ├── cudart64_50_35.dll │   ├── smsom.exe │   ├── smsom.ilk │   └── smsom.pdb ├── README.md ├── smsom │   ├── smsom.cu │   ├── smsom.vcxproj │   ├── smsom.vcxproj.user │   └── vc100.pdb ├── smsom.sdf ├── smsom.sln ├── smsom.suo ├── src │   ├── CMakeLists.txt │   └── smsom.cu └── tools └── median_filter.m </pre> ------------------------------------------------------------ # How to use ### For Windows users: If you have a working environment of OpenCV 2.4.5, CUDA 5.0, Visual Studio 2010, Windows 7 (64 bit), then you can use this repository directly. The executable is **.\Debug\smsom.exe**; therefore, you should first use **cmd** in Windows to navigate to the directory **Debug**. Please ENSURE smsom can find OpenCV library, and you have CUDA compatible GPU installed in your computer. Then you have two options: * If you have foreground free traning images, then execute: `smsom train <start_frame_number> <end_frame_number> <input_file_name> <output_file_name>` where `<start_frame_number>` and `<end_frame_number>` stand for the index range of the training images; `<input_file_name>` is the format of the input image's name, and the last parameter `<output_file_name>` is optional, if you omit it, then the output images are just shown in your screen, but not stored in your computer. For example, if I put the input images in: **E:\Data\input\**, the image files' name format is: **in000001.jpg** (any number), and I use 1-100 images to train the model, then I can execute: `smsom train 1 100 E:\\Data\\input\\in%06d.jpg E:\\Data\\results\\bin%06d.jpg` where I put the result images in **E:\Data\results**. or `smsom train 1 100 E:\\Data\\input\\in%06d.jpg` where I do not store the output images. * If you do not have foreground free training images, you can execute: `smsom nottrain <input_file_name> <output_file_name>` where the meanings of `<input_file_name>` and `<output_file_name>` (optional) are the same as the previous case. In this situation, we set the threshold tau=0.06 (see **[1]** for more details). ### For Linux users: You have to build yourself. Please ENSURE you have installed OpenCV and CUDA. See **[2]** for how to use CUDA on Linux platform. Then you can navigate to **./src/**, and execute the following commands in order: `cmake .` `make` The usage of the generated executable `smsom` is the same as the commands shown previously in Windows. (You may use `./smsom <parameters>` instead of `smsom <parameters>`) ------------------------------------------------------------ # Demos Some demo scripts are shown as follows (assuming you have decompressed dataset **[3]** in **E:\Data\**): * fountain01: ``smsom train 1 399 E:\\Data\\dynamicBackground\\fountain01\\input\\in%06d.jpg`` * highway: ``smsom nottrain E:\\Data\\baseline\\highway\\input\\in%06d.jpg`` * traffic: ``smsom train 129 200 E:\\Data\\cameraJitter\\traffic\\input\\in%06d.jpg`` * ladeSide: ``smsom train 1 999 E:\\Data\\thermal\\lakeSide\\input\\in%06d.jpg`` ------------------------------------------------------------ # Post-processing and quantitative evaluation You should use the Matlab script **.\tools\median_filter.m** to do a 5X5 median filtering to get the same results presented in this paper. Quatitative evaluation (Precision, Recall, F-measure) is done by the [Matlab tool][Matlab tool] of **[3]**. ------------------------------------------------------------ # References [1] Zhenjie Zhao, Xuebo Zhang, and Yongchun Fang. Stacked Multilayer Self-Organizing Map for Background Modeling. In: *IEEE Transactions on Image Processing*, vol. 24, no. 9, pp. 2841-2850, Sep 2015. [2] http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux/#axzz3DimlP7Yp [3] http://www.changedetection.net/ [project homepage]: http://zhaozj89.github.io/SMSOM/ [Matlab tool]: http://wordpress-jodoin.dmi.usherb.ca/static/code/MatlabCode2012.zip ------------------------------------------------------------ All rights reserved.