5,199 results

Optimized function for mutual information of two images or signals

information is obtained on the intersection between the supports of partial histograms.Example (in mi_test.m):disp('Test: Mutual information between two images')load mri A=D(:,:,8); B=D(:,:,9);mi(A,B)disp('Test

Calculates the Mutual Average Information of a time series

Calculates the Mutual Average Information of a time series for some time lag.

Here is a function with the simplest form to calculate the mutual information between two images.

MI is a good approach to align two images from different sensor. Here is a function with the simplest form to calculate the mutual information between two images.the function f=cal_mi(I1,I2) is in

Average mutual information

Very fast implementation of average mutual information. Usage: [v,lag]=ami(x,y,lag) Calculates the mutual average information of x and y with a possible lag. v is the average mutual information

The Adjusted Mutual Information for clustering comparison

This program calculates the adjusted mutual information for comparing clusterings. It includes:- The mutual information/adjusted mutual information- The Rand index and some other indices(see

A self-contained package for computing mutual information, joint/conditional probability, entropy

A self-contained, cross-platform, package for computing mutual information, joint/conditional probability, entropy, and more. This package has also been used for general machine learning and data

Calculating mutual information and other quantities using a parametric Gaussian copula.

Functions for calculating mutual information and other information theoretic quantities using a parametric Gaussian copula.This provides a robust rank based statistic that can handle multidimensional

Estimate Mutual Information with kernel density function

The function will estimate Estimate Mutual Information with kernel density function

Estimates Mutual Information and Conditional Mutual Information between continuous random variables

Mutual information I(X,Y) measures the degree of dependence (in terms of probability theory) between two random variables X and Y. Is is non-negative and equal to zero when X and Y are mutually

FMI is a non-reference image fusion metric based on mutual information of image features.

FMI calculates the Feature Mutual Information (FMI), the non-reference performance metric for fusion algorithms, proposed in: M.B.A. Haghighat, A. Aghagolzadeh, H. Seyedarabi, "A Non-Reference

Code for marginally and conditional mutual information in probability and information theory

The definition of mutual information could resort to wiki: http://en.wikipedia.org/wiki/Mutual_informationFor marginal mutual information, we say it is : I(A,B)=sum sum P(A,B) log[P(A,B)/P(A)P(B)]For

An implementation of the theory of fuzzy entropy and fuzzy mutual information.

Nowadays there are heaps of articles on the theory of fuzzy entropy and fuzzy mutual information. However, there is a clear significant lack for a Matlab implementation of these concepts. Based on

this software finds mutual information between two images

this software takes two images and return their mutual information. generally, If these two image do not belong to a same scene, their MI is minimum.

Multiple mutual information (interaction information)

MUTUALINFO(X,P,idx) returns the multiple mutual information (interaction information) for the joint distribution provided by object matrix X and probability vector P. Each row of MxN matrix X is an

a algorithm of feature selection, called BBPSO-based feature selection with mutual information (MIBBPSO)

Particle Swarm Optimization with Mutual Information ", which have been submitted to the journal Pattern Recognition. In this toolbox, the main function is named as “main”.

mrTools - matlab based tools for fMRI

This function computes the mutual information between two quantized images.

function M = MI_GG(X,Y)Computes the mutual information between two quantized images: X and Y.For Citation: M. Ceccarelli, M. di Bisceglie, C. Galdi, G. Giangregorio, S.L. Ullo, “Image Registration

Signal processing related functions.

Fully vectorized implementation NMI. NMI is often used for evaluating clustering results.

Normalized mutual information is often used for evaluating clustering results, information retrieval, feature selection etc. This is a optimized implementation of the function which has no for

Implementation for state-of-the-art mutual information based feature selection methods

)- Quadratic programming feature selection (QPFS)- Mutual information quotient (MIQ)- Maximum relevance minimum total redundancy (MRMTR) or extended MRMR (EMRMR)- Spectral relaxation global Conditional Mutual

Built for 'highest possible' speed. Can handle any number of dimensions, given sufficient memory.

This is a cross-platform version of mimimum-redundancy maximum-relevancy feature selection

top-ranking method has been demonstrated on a number of data sets in recent publications. This version uses mutual information as a proxy for computing relevance and redundancy among variables (features). Other

Highly comparative time-series analysis

Self-contained package for feature selection based on mutual information/interaction information.

, mutual information and interaction information are included and are usable by themselves. See demo_feature_select.m for examples.

Mutual Information measures using kNN for both continuous and categorical (discrete) variables

Mutual Information (Matlab code)Calculate the mutual information using a nearest-neighbours method for both the continuousversus continuous variable (Kraskov et al. 2004) and for the continious

This is an updated automatic image registration using mutual information for users of IP toolbox.

[2D Mutual Information Matching using Optimization toolbox]This is an updated automatic image registration using mutual information for users of IP toolbox coded by Kateryna Artyushkova from The

Calculates the mutual information between two discrete variables (or a group and a single variable).

MutualInformation: returns mutual information (in bits) of discrete variables 'X' and 'Y' I = MutualInformation(X,Y); I = calculated mutual information (in bits) X = variable(s) to be analyzed

pde1dm is a 1D PDE solver that supports high order interpolation functions, coupled ODE and is compatible with pdepe input syntax

pde1dm1D Partial Differential Equation Solver for MATLAB and Octavepde1dm solves systems of partial differential equations (PDE) in a singlespatial variable and time.The input is mostly compatible

Rigid (translation and rotation) automatic registration of images using normalized mutual informati

Functions for aligning images by rotation and translation: image_registr_MI.m MI2 - calculating Mutual informationjoint_h - calculating Joint histogramMutual information is calculated using joint

This is the PECUZAL implementation for Matlab. An automated approach for attractor reconstruction of uni- and multivariate datasets.

`pecuzal-embedding.mltbx`. For further information also see the documentation (`Examples`).

Computes a windowed mutual information

Calculate windowed mutual information between two signals up to a pre-defined lag. The estimation of the (joint) probabilities is optimized, hence, entire computation is very fast.

Fast mutual information calculation for images with consistent results to MATLAB built-in entropy() function.

MATLAB mutualInfo functionFast MATLAB function to calculate the mutual information of two images. Designed specifically for speed and to emulate functionality of MATLAB native entropy function. For

Functions for Information theory, such as entropy, mutual information, KL divergence, etc

information6)Normalized mutual information7)Normalized variation informationThis package is now a part of the PRML toolbox (http://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine

Computes and plots average mutual information and correlation for time series data.

AMI computes and plots average mutual information (ami) and correlation of univariate or bivariate time series for different values of time lag.USAGE: [amis corrs] = ami(xy,nBins,nLags)INPUT: xy

This function computes the mutual coherence of a matrix

This function computes the mutual coherence of a matrix.Input: a real or complex matrix with more than one column.Ouput: the mutual coherence.

This toolbox provides functions for maximizing and minimizing submodular set functions.

detection / facility location* sfo_fn_infogain: Information gain about gaussian random variables* sfo_fn_entropy: Entropy of Gaussian random variables* sfo_fn_mi: Gaussian mutual information* sfo_fn_varred

Fast computation of part of an FFT using fractional fourier transform

Estimation of a directionality index between two time series

[Dxy,Ixy,Iyx] = CMI_PE_tau(X,Y,ord,t,Tau)PCMI: a novel methodology based on permutation analysis and conditional mutual information for estimation of a directionality index between two time series

Learning Globally Optimal Dynamic Bayesian Network with the Mutual Information Test (MIT) Criterion

present GlobalMIT, a toolbox for learning the globally optimal DBN structure using a recently introduced information theoretic based scoring metric named mutual information test (MIT). Under MIT, learning

A set of visual entropy-based tools to assess the performance of multiclass classifiers.

mutual information, variation of information of entropy decrement. We have also added a script (compareETs) to visualize your own ETs and to print the NIT and EMA vs. other measures in latex-ready format

Image copy move detection using mutual information

The code try to detect if there any copy move attempt has been done , the sample image is forged with copy move technique . The algorithm detects tampering using regional mutual information .

Suitable for self-study or presenting to others, run full Turbo code simulations.

A collection of functions for image processing and analysis that complement and extend the Image Processing Toolbox

Toolbox for the simulation and detection of coevolution in proteins

A tutorial and tool using PLS for discriminant analysis.

Patial Least-Squares (PLS) is a widely used technique in various areas. This package provides a function to perform the PLS regression using the Nonlinear Iterative Partial Least-Squares (NIPALS

Matlab class implementing several methods for the computation of EXIT charts.

EXtrinsic Information Transfer (EXIT) charts are useful for characterizing the convergence properties and the performance of turbo receivers. Turbo receivers are generally constructed from two or

Full path name for partial or relative path

GetFullPath - Get absolute path of a file or folder nameThis function converts a partial or relative name to an absolute full path name. The fast Mex works on Windows only, but the M-file runs on

To find Channel capacity by maximizing mutual information w.r.t source symbol probability matrix Pc.

To find channel capacity one must maximize the mutual information with respect to the discrete set of probabilities of the source symbols 'c' , and for a given transition probability matrix.An

Function for calculating the partial rank correlation coefficient for a variable number of model parameters

This function serves as a means of calculating the partial rank correlation coefficients (PRCCs) of a set of equally sized (n,1) input arrays, p1, p2,... where the last input is the response of the

Useful tool for rendering and outputting information rich images

Using mutual information for users with no access to IP toolbox.

Functions for aligning images by rotation and translation: im_reg_MI.m MI2 - calculating Mutual informationjoint_h - calculating Joint histogramMutual information is calculated using joint histogram

Discovering clusters with varying densities

This class will query the current system information in MATLAB. Works with R2009a and up.

can do it inside MATLAB. But with the recent and excellent .Net support, this is actually quite easy and straightforward.This class, wrapping all the basic information regarding to you Windows machine

Ring artifact suppression tool for tomograms

Interpolates (& extrapolates) NaN elements in a 2d array.

interpolation, which give tradeoffs in accuracy versus speed and memory required. All the methods currently found in inpaint_nans are based on sparse linear algebra and PDE discretizations. In essence, a PDE is

Set of scripts for source and channel coding in telecommunications.

Lempel-Ziv'78 Coding. Calculation of Mutual Information and Channel Capacity (Non-symmetric and Symmetric Channel).

Graph Clustering by Congruency Approximation(CAC)

NPC algorithm is designed for learning Bayesian network formed as DAG in 2001, by Steck

This function returns various information on a Zeiss CZI image file.

Czifinfo read information from a Zeiss CZI image file.

Fast and robust reconstruction of Cartesian partial Fourier MRI data with POCS

POCS (Projection Onto Convex Sets) is often used to reconstruct partial Fourier MRI data.This implementation works with 2D or 3D data on a Cartesian grid. It is optimized for speed and automatically