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Description: Type Classification Code [英汉计算机大词典] n.分类码 :main.m (program control)discretize.m (converts image to discrete values)plotimg.m (plots划分 images)dirImg.m (computes the directional image)extract.m (extracts square portion of image抽取块方向图 - called by dirImg) slitSum.m (computes the slit缝隙、剖面 sum direction - called by dirImg)tileImg.m (tiles平铺显示 the image and computes new directions)averImg.m (smoothes the image)angleImg.m (computes the angles from the vector representationvector representation n.向量表示 )Poincare.m (computes the core-delta points)temp.m (extracts the core-delta points and locations) Correlation相关、对比 Code:proj.m (program control)plotimg.m (plots images)discretize.m (converts image to discrete values)normalize.m (normalizes images)corrfft.m (computes the correlation)-Type Classification Code [computer English Dictionary] n. classification codes : main.m (program control) discretize.m (converts image to discrete values) plotimg.m (plots classified images) dirImg.m (computes the directional image) extract. m (extracts square image portion of the direction taken block map-called by dirImg) slitSum.m (computes the slit cracks, profile sum direction - called by dirImg) tileImg.m (smooth tiles show the image and computes new directions) averImg.m ( smooths the image) angleImg.m (computes the angles from the vector representationvector representation n. Vector) Poincare.m (computes the core-delta points) temp.m (extracts the core-delta points and locations) Correlation related contrast Code : proj.m (program control) plotimg.m (plots images) discretize.m (con
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Size: 841 |
Author: 丰逸 |
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Description: Type Classification Code [英汉计算机大词典] n.分类码 :main.m (program control)discretize.m (converts image to discrete values)plotimg.m (plots划分 images)dirImg.m (computes the directional image)extract.m (extracts square portion of image抽取块方向图 - called by dirImg) slitSum.m (computes the slit缝隙、剖面 sum direction - called by dirImg)tileImg.m (tiles平铺显示 the image and computes new directions)averImg.m (smoothes the image)angleImg.m (computes the angles from the vector representationvector representation n.向量表示 )Poincare.m (computes the core-delta points)temp.m (extracts the core-delta points and locations) Correlation相关、对比 Code:proj.m (program control)plotimg.m (plots images)discretize.m (converts image to discrete values)normalize.m (normalizes images)corrfft.m (computes the correlation)-Type Classification Code [computer English Dictionary] n. classification codes : main.m (program control) discretize.m (converts image to discrete values) plotimg.m (plots classified images) dirImg.m (computes the directional image) extract. m (extracts square image portion of the direction taken block map-called by dirImg) slitSum.m (computes the slit cracks, profile sum direction- called by dirImg) tileImg.m (smooth tiles show the image and computes new directions) averImg.m ( smooths the image) angleImg.m (computes the angles from the vector representationvector representation n. Vector) Poincare.m (computes the core-delta points) temp.m (extracts the core-delta points and locations) Correlation related contrast Code : proj.m (program control) plotimg.m (plots images) discretize.m (con
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Size: 1024 |
Author: 丰逸 |
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Description: !逐步回归分析程序：
! M：输入变量，M=N+1，其中N为自变量的个数；M包括的因变量个数
! K：输入变量，观测点数；
! F1：引入因子时显著性的F-分布值；
! F2：剔除因子时显著性的F-分布值；
! XX：存放自变量和因变量的平均值；
! B：存放回归系数；
! V：存放偏回归平方和和残差平方和Q；
! S：存放回归系数的标准偏差和估计的标准偏差；
! C：存放复相关系数；
! F：存放F-检验值；-! Stepwise regression analysis procedure:! M: input variables, M = N+ 1, in which N is the number of independent variables M, including the number of the dependent variable! K: input variables, observation points ! F1: when to introduce a significant factor of the F-distribution value ! F2: remove significant factor when the F-distribution value ! XX: storage self-variables and the dependent variable on average ! B: regression coefficient storage ! V: store partial regression sum of squares and residual sum of squares Q ! S: storage of the standard deviation of regression coefficients and the estimated standard deviation ! C: storage of multiple correlation coefficient ! F: storing F-test value
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Size: 2048 |
Author: wang hanting |
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Description: 对随机变量以自变量的几组观测数据作多元线性回归（求得平均标准偏差，复相关系数和回归平方和）-Of random variables since the variables in several groups of observation data for multiple linear regression (obtained an average standard deviation, correlation coefficient and regression sum of squares)
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Size: 1024 |
Author: fuxiao |
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Description: Relationship Between the Sum of Squared Difference (SSD)
and Cross Correlation for Template Matching
Konstantinos G. Derpanis
York University
kosta@cs.yorku.ca
Version 1.0
December 23, 2005
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Size: 41984 |
Author: li |
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Description: Type Classification Code:
main.m (program control)
discretize.m (converts image to discrete values)
plotimg.m (plots images)
dirImg.m (computes the directional image)
extract.m (extracts square portion of image - called by dirImg)
slitSum.m (computes the slit sum direction - called by dirImg)
tileImg.m (tiles the image and computes new directions)
averImg.m (smoothes the image)
angleImg.m (computes the angles from the vector representation)
Poincare.m (computes the core-delta points)
temp.m (extracts the core-delta points and locations)
Correlation Code:
proj.m (program control)
plotimg.m (plots images)
discretize.m (converts image to discrete values)
normalize.m (normalizes images)
corrfft.m (computes the correlation)
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-Type Classification Code: main.m (program control) discretize.m (converts image to discrete values) plotimg.m (plots images) dirImg.m (computes the directional image) extract.m (extracts square portion of image- called by dirImg ) slitSum.m (computes the slit sum direction- called by dirImg) tileImg.m (tiles the image and computes new directions) averImg.m (smoothes the image) angleImg.m (computes the angles from the vector representation) Poincare.m (computes the core-delta points) temp.m (extracts the core-delta points and locations) Correlation Code: proj.m (program control) plotimg.m (plots images) discretize.m (converts image to discrete values) normalize. m (normalizes images) corrfft.m (computes the correlation )--------------------------------------------------------------------------------
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Size: 6144 |
Author: 小熊 |
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Description: 基于互相关函数，采用求和广义互相关函数(summed-GCC)法用于机器人系统平台。由于采用不同的映射函数(mapping functions)，GCC法在该平台下，只需三个麦克风即可进行三维定位，突破了基于TDOA法进行三维声源定位最少需4个麦克风的限制-Based on cross-correlation function, using generalized cross-correlation function sum (summed-GCC) method for the robot system platform. Because of using different mapping function (mapping functions), GCC method in the platform, the only three to three-dimensional microphone positioning, a breakthrough TDOA method based on three-dimensional sound localization will take at least four microphone restrictions
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Size: 799744 |
Author: chen |
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Description: 通过MATLAB软件；通过仿真得出性能曲线；从而比较bpsk的性能-The bit-error rate (BER) of binary phase-shift keying
in Rayleigh fading, using the Alamouti transmission scheme and
receiver selection diversity in the presence of channel-estimation
error, is studied. Closed-form expressions for the BER of log-likelihood
ratio selection, signal-to-noise ratio (SNR) selection, switchand-
stay combining selection, and maximum ratio combining are
derived in terms of the SNR and the cross-correlation coefficient
of the channel gain and its corrupted estimate. Two new selection
schemes, space–time sum-of-squares combining selection diversity
and space–time sum-of-magnitudes selection diversity, are
proposed and proven to provide almost the same performance as
SNR selection, but with much simpler implementations. The effects
of channel-
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Size: 340992 |
Author: 刘小洋 |
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Description: matching shape can be subdivided between two approaches: feature-based and template-based matching. The feature-based approach uses the features of the search and template image, such as edges or corners, as the primary match-measuring metrics to find the best matching location of the template in the source image. The template-based, or global, approach, uses the entire template, with generally a sum-comparing metric (using SAD, SSD, cross-correlation, etc.) that determines the best location by testing all or a sample of the viable test locations within the search image that the template image may match up to.
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Size: 10481664 |
Author: gislam |
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Description: Blind, Adaptive Channel Shortening by Sum-squared Auto-correlation Minimization (SAM)," IEEE Trans. on Signal Processing, December 2003.
The two zip files below should be installed in parallel. -Blind, Adaptive Channel Shortening by Sum-squared Auto-correlation Minimization (SAM)," IEEE Trans. on Signal Processing, December 2003.
The two zip files below should be installed in parallel.
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Size: 25600 |
Author: shreedhar |
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Description: 在这项工作中所使用的方法是基于一个由大卫阿诺德教程。
http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf
这将运行该Logistic.m带来了图形用户界面。
1。放弃在列的格式文本文件中的x值
2。放弃在山口格式文本文件中的y值
3。的阴谋初始 按钮将绘制的分布
4。 查找适合 按钮，会找到最适合
5。 重置 将删除的情节（虽然我想打扫所有的领域 -没有时间）
5。地下K，糖尿病在下列公式中的值
Ÿ 等于k/（1+进出口（- G *的（十型录）））
6。上证所给出了squred误差之间的拟合函数与实际数据的总和
7。消委会提供的相关合作关系的实际数据拟合功能和效率-The method used in this work is based on a tutorial by David Arnold.
http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf
RUN The Logistic.m this will bring up the GUI.
1. Give the x values on a text file in column format
2. Give the y values on a text file in col format
3. Plot Initial Button will plot the distribution
4. Find Fit button will find the best fit
5. Reset will remove the plot (Although I wanted to clean all the fields- did not have time)
5. K, G, Dm are the values in the following equation
y = K./(1+exp(-G*(x-Dm)))
6. SSE gives the sum of squred error between the fitted function and the actual data
7. CC give the correlation co-efficient between the fitted function and actual data
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Size: 11264 |
Author: abeaqhax |
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Description: 采用模板匹配方法进行图像匹配，其中误差平方和测度经过归一化互相关处理。-Using template matching method for image matching, in which the error sum of squares measure through normalized cross correlation processing.
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Size: 112640 |
Author: ZDJ |
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Description: Simulator
这是一个仿真程序文件夹下面有：
acorr
自相关
add
向量相加
convol
卷积
corr
互相关
dlms
自适应算法
hpassfir
高通FIR算法
lpassfir
低通算法
mmul
矩阵相乘
rfft
FFT算法实验
sin
这是一个产生正弦波数据的程
sub
向量相减
-Simulator is a simulation program folder below: acorr autocorrelation vector sum convol add corr correlation dlms convolution algorithm hpassfir adaptive algorithm lpassfir high-pass FIR low-pass algorithm mmul rfft FFT algorithm for matrix multiplication is an experiment which produces sin sine wave data sub vector subtraction process
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Size: 1075200 |
Author: 醚 |
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Description: 这是模式识别中最小错误率Bayes分类器设计方案。
自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。
调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，通过比较概率大小判断一个体重身高二维向量代表的人是男是女。
主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。
-This is the minimum error rate pattern recognition Bayes classifier design.
Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array .
All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions .
Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function .
Call the minimum error rate decision Functions Bayesian
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Size: 4096 |
Author: 崔杉 |
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Description: 这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。
调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。
主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。
-This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array .
All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions .
Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function .
Bayesian classifier
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Size: 4096 |
Author: 崔杉 |
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Description: a method to calculate the sum of square differenes using the normalised fft cross correlation
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Size: 1024 |
Author: steven woolford |
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Description: 对随机变量以自变量的几组观测数据作多元线性回回归（求的平均标准偏差，复相关系数与回归平方与）
-Argument of several sets of observational data for the multiple linear regression (ask the average standard deviation of the random variable, the multiple correlation coefficient and regression sum of squares)
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Size: 1024 |
Author: 认可 |
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Description: 1.产生正弦信号，方波信号，均匀噪声；
2.信号的叠加；
3.信号的相关分析；
4.信号的卷积；
5.信号的和；
6.信号的频谱图。-1. Generate sine signal, square wave signals, uniform noise
2. Signal superposition
3. The signal correlation analysis
4. The convolution of signal
5. The sum of signal
6. The signal spectrum.
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Size: 1024 |
Author: 刘伟 |
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Description: Advanced correlation filter synthesis algorithms to achieve rotation invariance are described. We use a specified form for the filter as the rotation invariance constraint and derive a general closed-form solution for a multiclass rotation-invariant filter that can recognize a number of different objects. By requiring the filter to minimize the average correlation plane energy, we produce a multiclass rotation invariant (RI) RI-MACE filter, which controls correlation plane sidelobes and improves discrimination against false targets. To improve noise performance, we require the filter to minimize a weighted sum of correlation plane signal and noise energy. Initial test results of all filters are provided
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Size: 45056 |
Author: dhamey |
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Description: Function-Compute Correlation between two images using the similarity measure of Sum of Squared Differences (SSD) with Right Image
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Size: 1024 |
Author: aliveli |
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