Description: 数字识别代码 第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）第二步：识别。 首先，打开图像（256色） 再次，进行归一化处理。点击“一次性处理” 最后，点击“R”或者使用菜单找到相应项来进行识别识别的结果显示在屏幕上，同时也输出到文件result.txt中 该系统的识别率一般为90％ 另外，也可以单独对打开的图片一步一步进行图像预处理工作。但要注意，每一步工作只能执行一遍，而且要按顺序执行。步骤为：“256色位图转为灰度图”“灰度图二值化”“去噪”“倾斜校正”“分割”“标准化尺寸”“紧缩重排” 另外注意，要识别的图片，要与win.dat、whi.dat位于同一目录。此两文件保存训练后网络的权值参数
-digital identification code first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors) again, to a treatment. Click on the "one-stop" Finally, Click on the "R" or use the menu item corresponding to find the identification of the identification results on the screen, also result.txt output to a file which the system's recognition rate of 90% for the other, can be individually opened to the step-by-step pictures for image preprocessing work. But we know that each step of implementation can work again, but according to the implementation of the order. Steps : "256-colo Platform: |
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Author:刘嘉康 |
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Description: 图形数字识别：
第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）
第二步：识别。
首先，打开图像（256色）
再次，进行归一化处理。点击“一次性处理”
最后，点击“R”或者使用菜单找到相应项来进行识别
识别的结果显示在屏幕上，同时也输出到文件result.txt中-graphics figures recognition : the first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors) again, to a treatment. Click on the "one-stop" Finally, Click on the "R" or use the menu item corresponding to find the identification of the identification results on the screen, also output to a file which result.txt Platform: |
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Author:叶 |
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Description: 数字识别系统源代码.rar
第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）
第二步：识别。首先，打开图像（256色）；再次，进行归一化处理，点击“一次性处理”；最后，点击“R”或者使用菜单找到相应项来进行识别。识别的结果显示在屏幕上，同时也输出到文件result.txt中。
该系统的识别率一般情况下为90％。
此外，也可以单独对打开的图片一步一步进行图像预处理工作，但要注意，每一步工作只能执行一遍，而且要按顺序执行。
具体步骤为：“256色位图转为灰度图”－“灰度图二值化”－“去噪”－“倾斜校正”－“分割”－“标准化尺寸”－“紧缩重排”。
注意，待识别的图片要与win.dat和whi.dat位于同一目录，这两文件保存训练后网络的权值参数。
-digital identification system source code. Rar first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors); Again, to a treatment, click on the "one-time"; Finally, Click on the "R" or use the menu item corresponding to find identification. Recognition results on the screen, but also the output to a file result.txt China. The system's recognition rate under normal circumstances 90%. Alternatively, you could open a separate pair of step-by-step photographs for image preprocessing work, but bearing in mind that Each step can only be implemented again, but in accordance with Platform: |
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Author:shenmingdong |
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Description: 数字识别代码 第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）第二步：识别。 首先，打开图像（256色） 再次，进行归一化处理。点击“一次性处理” 最后，点击“R”或者使用菜单找到相应项来进行识别识别的结果显示在屏幕上，同时也输出到文件result.txt中 该系统的识别率一般为90％ 另外，也可以单独对打开的图片一步一步进行图像预处理工作。但要注意，每一步工作只能执行一遍，而且要按顺序执行。步骤为：“256色位图转为灰度图”“灰度图二值化”“去噪”“倾斜校正”“分割”“标准化尺寸”“紧缩重排” 另外注意，要识别的图片，要与win.dat、whi.dat位于同一目录。此两文件保存训练后网络的权值参数
-digital identification code first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors) again, to a treatment. Click on the "one-stop" Finally, Click on the "R" or use the menu item corresponding to find the identification of the identification results on the screen, also result.txt output to a file which the system's recognition rate of 90% for the other, can be individually opened to the step-by-step pictures for image preprocessing work. But we know that each step of implementation can work again, but according to the implementation of the order. Steps : "256-colo Platform: |
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Author: |
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Description: 图形数字识别：
第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）
第二步：识别。
首先，打开图像（256色）
再次，进行归一化处理。点击“一次性处理”
最后，点击“R”或者使用菜单找到相应项来进行识别
识别的结果显示在屏幕上，同时也输出到文件result.txt中-graphics figures recognition : the first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors) again, to a treatment. Click on the "one-stop" Finally, Click on the "R" or use the menu item corresponding to find the identification of the identification results on the screen, also output to a file which result.txt Platform: |
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Author:叶 |
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Description: 数字识别系统源代码.rar
第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）
第二步：识别。首先，打开图像（256色）；再次，进行归一化处理，点击“一次性处理”；最后，点击“R”或者使用菜单找到相应项来进行识别。识别的结果显示在屏幕上，同时也输出到文件result.txt中。
该系统的识别率一般情况下为90％。
此外，也可以单独对打开的图片一步一步进行图像预处理工作，但要注意，每一步工作只能执行一遍，而且要按顺序执行。
具体步骤为：“256色位图转为灰度图”－“灰度图二值化”－“去噪”－“倾斜校正”－“分割”－“标准化尺寸”－“紧缩重排”。
注意，待识别的图片要与win.dat和whi.dat位于同一目录，这两文件保存训练后网络的权值参数。
-digital identification system source code. Rar first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors); Again, to a treatment, click on the "one-time"; Finally, Click on the "R" or use the menu item corresponding to find identification. Recognition results on the screen, but also the output to a file result.txt China. The system's recognition rate under normal circumstances 90%. Alternatively, you could open a separate pair of step-by-step photographs for image preprocessing work, but bearing in mind that Each step can only be implemented again, but in accordance with Platform: |
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Author:shenmingdong |
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Description: 一张图片是之前用摄像机采集的，另一张图片是经过离散沃尔什变换再量化编码压缩后又解压所得，能看出区别吗？呵呵。flik.m是原图，newflik.m是新图。
附上所有MATLAB源程序，有兴趣的话，你也可以用这几个程序对自己的照片“加工”一番。以下6个程序分别保存为6的m文件，文件名与下面给出的一致。待“加工”图片应该存放在Matlab的Work目录下。-Before a picture is collected by the camera, another picture is the result of discrete Walsh transform and then re-quantization coding compression decompression income, you can see the difference? Ha ha. flik.m is the image, newflik.m is a new map. MATLAB source code attached to all interested, you can also use these procedures on their own photo processing of something. The following six procedures were saved as 6 m file, the file name is given with the following consensus. To be processing picture should be stored in Matlab s Work directory. Platform: |
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Author:余海风 |
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Description:
第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）
第二步：识别。首先，打开图像（256色）；再次，进行归一化处理，点击“一次性处理”；最后，点击“R”或者使用菜单找到相应项来进行识别。识别的结果显示在屏幕上，同时也输出到文件result.txt中。
该系统的识别率一般情况下为90％。-The first step: Training Network. The use of training samples for training. (This process may or may not be training, because I have trained network parameters saved, and readers can be directly used to identify) the second step: Identify. First, open the image (256 colors) again, normalized to deal with, click on the Platform: |
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Author:魏昌伟 |
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Description: 数字识别代码 第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）第二步：识别。 首先，打开图像（256色） 再次，进行归一化处理。点击“一次性处理” 最后，点击“R”或者使用菜单找到相应项来进行识别识别的结果显示在屏幕上，同时也输出到文件result.txt中 该系统的识别率一般为90％ -Digital identification code the first step: Training Network. The use of training samples for training. (This process may or may not be training, because I have trained network parameters saved, and readers can be directly used to identify) the second step: Identify. First, open the image (256 colors) once again, for normalized. Click on the " one-off deal with" Finally, click " R" or use the menu to find the corresponding items of identification to identify the results showed on the screen, but also output to a file Result.txt in the system' s recognition rate is generally 90％ Platform: |
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Author:吴学龙 |
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Description: 使用说明
第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）
第二步：识别。首先，打开图像（256色）；再次，进行归一化处理，点击“一次性处理”；最后，点击“R”或者使用菜单找到相应项来进行识别。识别的结果显示在屏幕上，同时也输出到文件result.txt中。
该系统的识别率一般情况下为90 。
此外，也可以单独对打开的图片一步一步进行图像预处理工作，但要注意，每一步工作只能执行一遍，而且要按顺序执行。
具体步骤为：“256色位图转为灰度图”－“灰度图二值化”－“去噪”－“倾斜校正”－“分割”－“标准化尺寸”－“紧缩重排”。
注意，待识别的图片要与win.dat和whi.dat位于同一目录，这两文件保存训练后网络的权值参数。
具体使用请参照书中说明。-Help
The first step: Training Network. Training in the use of training samples. (This process may or may not be trained because the training I have saved up a good network parameters, and readers can use to identify)
Step two: identification. First, open the image (256 colors) again, normalized to, click on the "one-time deal" Finally, click "R" or use the menu to find the corresponding item in the Department. Recognition results showed on the screen, as well as output to a file in result.txt.
Recognition rate of the system under normal circumstances was 90 .
In addition, you can open a separate picture of the image pre-processing step by step, but it must be noted that the implementation of each step can only work once, but according to the order of implementation.
Concrete steps as: "256-color bitmap to grayscale"- "grayscale binary"- "noise"- "tip-tilt correction"- "split"- "the standardization of size"- "tight rearrangement."
Note that picture to be Platform: |
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Author:sombad |
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Description: Windowed Fourier transform for fringe pattern analysis (with GUI)
-• Run WFT in MATLAB environment. All processed data are automatically saved in result.mat
4. File list
o wft2fw.m: the basic WFT algorithm.
o unwrapping_qg_trim.m: quality guided phase unwrapping
o WFT.m+WFT.fig: WFT overall interface
o WFTKernel.m+WFTKernel.fig: interface for WFT parameter selection
o imwritescale: to save data into image
o readme.docx: this file.
• 5. Notice
Though wft2fw.m has been used by us for some years, the GUI was developed quite recently. The purpose is to make it easier to be used. Please feedback bugs to mkmqian@yahoo.com.sg. For more information, please refer to www.ntu.edu.sg/home/mkmqian.
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Author:谢冉 |
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Description: The need to reprogramme a wireless sensor network may arise from
changing application requirements, bug fixes, or during the application
development cycle. Once deployed, it will be impractical at
best to reach each individual node. Thus, a scheme is required to
wirelessly reprogramme the nodes. We present an energy-efficient
code distribution scheme to wirelessly update the code running in
a sensor network. Energy is saved by distributing only the changes
to the currently running code. The new code image is built using
an edit script of commands that are easy to process by the nodes.
A small change to the programme code can cause many changes to
the binary code because the addresses of functions and data change.
A naive approach to building the edit script string would result in
a large script. We describe a number of optimisations and present
experimental results showing that these significantly reduce the edit Platform: |
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Author:jok |
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Description: VC6.0下的256色图像配准源程序，可以直接在VC6.0下运行，配准图像结果保存为c:\a.bmp，注意需要在配准之前选取三个特征点，便于识别。。。具体可参考源程序，内附示例lina图像，lina_a.bmp,lina_b.bmp是原始配准图像。特征点选取越好，则效果越好。。。 -VC6.0 256 under the source color image registration, running directly in VC6.0, the image registration result is saved as c: \ a.bmp, attention needs to select the three before the registration feature points, for easy identification. . . Specifically refer to the source, containing a sample lina image, lina_a.bmp, lina_b.bmp image of the original registration. Selected feature points as possible, then the better. . . Platform: |
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Author:cmywen |
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Description: 本目录主要包括：文档和两个源代码。
其中一个源代码是识别程序的，另一个是一个矩阵类库的，书里面也使用过，所以一并附上。
因为已经保存了训练好的网络权值，所以第一次使用识别程序时，可以直接运行\\Release目录下的可执行文件，然后对图片目录中的测试图片进行读入、识别。
当然读者也可以自行用训练样本训练网络，不过要特别注意训练样本的选择，否则可能造成识别率很低。训练样本选择的原则是，尽可能的有代表性，在训练时间不至于太长的情况下，训练样本数目尽量多。
-This directory includes: documentation and two source code.
One of the source code is to identify the procedure, the other is a matrix class library, the book which is also used, so be attached.
Have been saved because of the trained network weights, so the first use of identification procedures, he can run \ \ Release directory
Under the executable file, then the test image directory read pictures, identification.
Of course, readers are free to use the training samples to train the network, but pay special attention to training samples, otherwise it may result in knowledge
Do not rate very low. Training sample selection principle is that as far as possible, representative, in the long training time Buzhi Yu s case, the training
Training sample size as much as possible. Platform: |
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Author:王兆云 |
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Description: 用来生成一个随机字幕雨,结果保存为png的图片格式,默认使用相对路径.Java代码含注释.
-Used to generate a random subtitles rain. the result is saved as png image format.Default use relative paths. Java code with annotations. Platform: |
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Author: |
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Description: 使用C或C++语言编写8位灰度图像直方图统计的程序。
照如下格式将统计结果保存到“result.txt”中。
灰度级 频率
0
……
255
-Using C or C++ language to write 8-bit grayscale image histogram statistics program.
The statistical follows the format of results are saved to the "result.txt.
Gray-scale frequency
0
......
255 Platform: |
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Author:吴昊天 |
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Description: 本程序在matlab中运行。
Generate_SystemMatrix是一个计算OSEM，MLEM迭代图像算法中系统矩阵的函数，其用法Generate_SystemMatrix（pixel），为所要重建图像的大小，一般为64,128,256，pixel也大运算越慢。
里面的diedai-test文件是一个简单的MLEM重建算法，其中要加载SystemMatrix矩阵就是Generate_SystemMatrix(128)的结果，注意如果要运行diedai-test，生成的系统矩阵名要保存为SystemMatrix，否则出错。
testsino是一个正弦图实例，就是MLEM算法中的投影矩阵，机算是数据。-This program is run in matlab. The Generate_SystemMatrix is a calculation of the OSEM, a function of the system matrix MLEM iterative image algorithm, its use Generate_SystemMatrix (pixel) to rebuild the image size, usually 64,128,256, pixel also large computing slower. Inside diedai-test file is a simple MLEM reconstruction algorithm, which load SystemMatrix matrix is the result of Generate_SystemMatrix (128), pay attention to if you want to to run diedai-test, the generated system matrix is saved as SystemMatrix, otherwise an error. testsino instance of a sinogram projection matrix MLEM algorithm, machine be regarded as data. Platform: |
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Author:cc |
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Description: MATCH_FUSE:是把配准移植到融合上程序，可以边采集，边配准，边融合。
在手工配准时，这个是针对改程序的配准，所以配准时是采用红外为基准图像，可见光为带配准图像。
配准结果生成一个.XML文件，自动保存起来，然后在融合时加载同文件名XML文件。-MATCH_FUSE: is the registration transplanted into integration program, can collection, edge matching, edge fusion.In the manual registration, this is for the program registration, so with time is using infrared as a reference image, the visible light band image registration.The matching result is to generate a.XML file, automatically saved, and then loaded in fusion with the file name XML file. Platform: |
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Author:dsfewf |
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Description: 运行程序后，可以在当前显示的图像界面手动分割目标，通过点击鼠标左键勾勒目标边界，点击鼠标右键可以删除未画好的边缘，画好边缘后，双击鼠标左键可以生成分割结果的二值模板，以.mat文件形式自动保存。(After running the program, you can manually split the target in the currently displayed image interface, click the left mouse button to outline the target boundary, click the right mouse button to delete the unpainted edge, and after drawing the edge, double click the left mouse button to generate the segmentation result. The value template is automatically saved as a .mat file.) Platform: |
Size: 21504 |
Author:小玲子 |
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Search - SAVED IMAGE result