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src
背景减除的完整算法，《A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection》作者源代码(The "src" folder contains all of the source code.
To run the program, follow these simple steps:
1. Start MATLAB and ensure the "src" folder is your current working
directory.
2. Edit "bs.m" and "classify.m" so that the file paths point to the
proper directories as specified.
3. In the MATLAB environment type "bs" which will train the background
model from the sequence you specified in (2).
4. In the MATLAB environment type "classify" which will classify the
sequence of images you specified in (2).
5. Now the classified images should be saved to disk in the folder you
specified in (2).)
- 2012-09-20 16:47:35下载
- 积分：1
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700977
反混淆直线 采用未加权的区域采样算法 效果很不错()
- 2018-05-27 14:24:12下载
- 积分：1
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150
说明： 医学图像资源 内含150张512*512乳腺癌图源 可用来医学图像学习实践(Medical image resource containing 150 512* 512 breast cancer map sources can be used to study and practice of medical image)
- 2010-03-18 11:19:21下载
- 积分：1
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pingjia
图像处理效果评价,评价指标包括峰值信噪比，熵，以及均方误差，具体公式请参考相关文献(photo)
- 2009-05-13 17:06:28下载
- 积分：1
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ostu
此代码应用于图像分割，是基于类间方差的大津法求分割阈值(This code applies to image segmentation is based on the variance between two classes of Otsu threshold segmentation uated)
- 2015-12-01 18:46:59下载
- 积分：1
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test3
利用Artoolkit实现了虚拟现实的算法，VS2008下测试通过(Use Artoolkit algorithm to achieve a virtual reality, VS2008 under test)
- 2013-12-12 12:39:48下载
- 积分：1
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TSBL_code
稀疏贝叶斯学习的代码，出自UCLA，大家可以下载看看(sparse bayesian learning)
- 2021-05-13 02:30:02下载
- 积分：1
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splitmerge_matlab_program
关于图像分割、区域分离和合并的matlab程序(On image segmentation, region separation and merger procedures matlab)
- 2008-12-30 14:57:31下载
- 积分：1
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FAST-ICA
1、对观测数据进行中心化，；
2、使它的均值为0，对数据进行白化—>Z；
3、选择需要估计的分量的个数m，设置迭代次数p<-1
4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）；
5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的）
6、用对称正交法处理下W
7、归一化W(:,p)=W(:,p)/norm(W(:,p))
8、若W不收敛，返回第5步
9、令p=p+1，若p小于等于m,返回第4步
剩下的应该都能看懂了
基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)
- 2013-06-27 15:39:00下载
- 积分：1
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zernike
矩不变量，适用于图像匹配，数字水印，图像重建(Moment invariants, applicable to the image matching, digital watermarking, image reconstruction)
- 2010-12-03 10:25:23下载
- 积分：1
