Machine Learning Archives - Page 4 of 5

Expectation Maximization algorithm to obtain Gaussian mixture models for ROS

Leave a Comment / C++, Machine Learning, ROS, Tutorials / admin

I found a really good code at GitHub for fitting a Gaussian Mixture Model (GMM) with Expectation Maximization (EM) for ROS. There are so many parameters that you can change. Some of the most important ones are:

// minimum number of gaussians
#define PARAM_NAME_GAUSSIAN_COUNT_MIN    "gaussian_count_min"
#define PARAM_DEFAULT_GAUSSIAN_COUNT_MIN 1

// search will terminate when the gaussian count reaches this
#define PARAM_NAME_GAUSSIAN_COUNT_MAX    "gaussian_count_max"
#define PARAM_DEFAULT_GAUSSIAN_COUNT_MAX 10

// minimum number of gaussians

#define PARAM_NAME_GAUSSIAN_COUNT_MIN "gaussian_count_min"

#define PARAM_DEFAULT_GAUSSIAN_COUNT_MIN 1

// search will terminate when the gaussian count reaches this

#define PARAM_NAME_GAUSSIAN_COUNT_MAX "gaussian_count_max"

#define PARAM_DEFAULT_GAUSSIAN_COUNT_MAX 10

To find the optimal number of components, it uses Bayesian information criterion (BIC). There are other methods to find […]

Expectation Maximization algorithm to obtain Gaussian mixture models for ROS Read More »

Generating multivariate normal distribution samples using C++11 and Eigen library.

Leave a Comment / C++, Linear Algebra, Machine Learning / admin

Eigen is a great tool for matrix operations, here I found a small piece of code in Github that enables you to generate multivariate normal distribution samples using C++11 and Eigen library. The below is an example:

#include <fstream>
#include <Eigen/Eigen>
#include <Eigen/Core>
#include <Eigen/Dense>
#include "eigenmvn.h"



int main(int argc, char ** argv)
{
	//number of columns
	const uint n_cols = 2; 
	// number of rows
	const uint n_rows = 5000; 

	Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> total_gaussian_data_eigen, first_gaussian_data_eigen,second_gaussian_data_eigen;
	first_gaussian_data_eigen.setZero(n_rows,n_cols);
	second_gaussian_data_eigen.setZero(n_rows,n_cols);
	//2*n_rows since we want to concat first and second and put it in this matrix
	total_gaussian_data_eigen.setZero(2*n_rows,n_cols);



	Eigen::Vector2f mean;
	Eigen::Matrix2f covar;
	float theta;

	mean << 0,0; // Set the mean
	
	/* our covar matrix:
	|10  0|
	|0   1|
	*/
	covar <<10,0,0,1;

	//first gaussian
	Eigen::EigenMultivariateNormal<float> normX_solver1(mean,covar);
	first_gaussian_data_eigen<< normX_solver1.samples(n_rows).transpose() ;
	
	
	//secodn gaussian
	mean << 4,4; // Set the mean
// 	Create a covariance matrix rotated clockwise by theta
	theta=M_PI/4.0;
	covar=genCovar(1,4,theta);
	
	Eigen::EigenMultivariateNormal<float> normX_solver2(mean,covar);
	second_gaussian_data_eigen<<normX_solver2.samples(n_rows).transpose() ;


	//concating two gaussians
	total_gaussian_data_eigen<<first_gaussian_data_eigen,second_gaussian_data_eigen;

	
	//writing data into output in csv format
    std::cout<<"x,y\n";
    for (uint i = 0; i < total_gaussian_data_eigen.rows(); i++)
    {
        for (uint h = 0; h < n_cols-1; h++)
        {
                    std::cout<<total_gaussian_data_eigen(i,h);
                    std::cout<<",";
        }
        std::cout<<total_gaussian_data_eigen(i,n_cols-1);
        std::cout<<std::endl;
    }

}

#include <fstream>

#include <Eigen/Eigen>

#include <Eigen/Core>

#include <Eigen/Dense>

#include "eigenmvn.h"

int main(int argc, char ** argv)

{

//number of columns

const uint n_cols = 2;

// number of rows

const uint n_rows = 5000;

Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> total_gaussian_data_eigen, first_gaussian_data_eigen,second_gaussian_data_eigen;

first_gaussian_data_eigen.setZero(n_rows,n_cols);

second_gaussian_data_eigen.setZero(n_rows,n_cols);

//2*n_rows since we want to concat first and second and put it in this matrix

total_gaussian_data_eigen.setZero(2*n_rows,n_cols);

Eigen::Vector2f mean;

Eigen::Matrix2f covar;

float theta;

mean << 0,0; // Set the mean

/* our covar matrix:

|10 0|

|0 1|

covar <<10,0,0,1;

//first gaussian

Eigen::EigenMultivariateNormal<float> normX_solver1(mean,covar);

first_gaussian_data_eigen<< normX_solver1.samples(n_rows).transpose() ;

//secodn gaussian

mean << 4,4; // Set the mean

// Create a covariance matrix rotated clockwise by theta

theta=M_PI/4.0;

covar=genCovar(1,4,theta);

Eigen::EigenMultivariateNormal<float> normX_solver2(mean,covar);

second_gaussian_data_eigen<<normX_solver2.samples(n_rows).transpose() ;

//concating two gaussians

total_gaussian_data_eigen<<first_gaussian_data_eigen,second_gaussian_data_eigen;

//writing data into output in csv format

std::cout<<"x,y\n";

for (uint i = 0; i < total_gaussian_data_eigen.rows(); i++)

{

for (uint h = 0; h < n_cols-1; h++)

{

std::cout<<total_gaussian_data_eigen(i,h);

std::cout<<",";

}

std::cout<<total_gaussian_data_eigen(i,n_cols-1);

std::cout<<std::endl;

}

Generating multivariate normal distribution samples using C++11 and Eigen library. Read More »

Multi scale face detector using HOG features and support vector machine

Leave a Comment / Computer Vision, Image Processing, Machine Learning / admin

In this part, I trained an SVM over images of “face” or “not face” (36 × 36 pixels), using HOG features. I used VLFeat library for both HOG and the SVM. Example of face images: Example of nonface images: I divided the dataset into a training and a test set (80% and 20% respectively) and computed the HOG

Multi scale face detector using HOG features and support vector machine Read More »

2D pose estimation of human body using CNS and PCA

1 Comment / C++, Computer Vision, Image Processing, Linear Algebra, Machine Learning / admin

This work is the second part of my master thesis (part I). In this part, I developed an algorithm for 2D pose estimation of the human body. To do this, I created a software with QT that could generate 2D contours representing human body. Then I send these contours for evaluation to CNS(Contrast Normalized Sobel) [1]

2D pose estimation of human body using CNS and PCA Read More »

Human detection on mobile camera using HOG and tracking them using Kalman filter

1 Comment / Computer Vision, Image Processing, Machine Learning, Tutorials / admin

This is the part I of the work that I did for my master thesis (part II). In this work first, I computed HOG (Histogram of oriented gradients) on my images and then sent the computed histogram to a linear SVM (support vector machine). The SVM was trained with human and non-human images. The output of the classifier was

Human detection on mobile camera using HOG and tracking them using Kalman filter Read More »

Real time object recognition and 6DOF pose estimation with PCL pointcloud and ROS

2 Comments / C++, Computer Vision, Machine Learning, ROS, Tutorials / admin

Real-time object recognition and 6DOF pose estimation with PCL pointcloud and ROS. This approach is based on normal coherence

Real time object recognition and 6DOF pose estimation with PCL pointcloud and ROS Read More »

Real-time object recognition and 6DOF pose estimation based on Linemod algorithm with ROS and PCL pointcloud

5 Comments / Computer Vision, Machine Learning, ROS, Tutorials / admin

In this tutorial, I’m gonna show you how to do object recognition and 6DOF pose estimation in real-time based on Linemod algorithm with ROS and PCL pointcloud. First, you need to install ork:

sudo apt-get install ros-indigo-object-recognition-ros-visualization ros-indigo-object-recognition-msgs ros-indigo-object-recognition-core

1	sudo apt-get install ros-indigo-object-recognition-ros-visualization ros-indigo-object-recognition-msgs ros-indigo-object-recognition-core

Then add the model of your object for tracking to Couch DB:

 rosrun object_recognition_core object_add.py -n coke -d "A universal can of coke" --commit

1	rosrun object_recognition_core object_add.py -n coke -d "A universal can of coke" --commit

rosrun object_recognition_core mesh_add.py <YOUR_OBJECT_ID_FROM_PREVIOUS_STEP> /home/behnam/ork_tutorials/data/coke.obj --commit

1	rosrun object_recognition_core mesh_add.py <YOUR_OBJECT_ID_FROM_PREVIOUS_STEP> /home/behnam/ork_tutorials/data/coke.obj --commit

You need to install Kinect driver, if you don’t

Real-time object recognition and 6DOF pose estimation based on Linemod algorithm with ROS and PCL pointcloud Read More »

Gradient descent method for finding the minimum

Leave a Comment / Machine Learning, Python / admin

Gradient descent is a very popular method for finding the maximum/ minimum point of a given function. It’s very simple yet powerful but may trap in the local minima. Here I try to find the minimum of the following function: $$ z= -( 4 \times e^{- ( (x-4)^2 +(y-4)^2 ) }+ 2 \times e^{- ( (x-2)^2 +(y-2)^2

Gradient descent method for finding the minimum Read More »

Sample based-optimisation-based planner with signed distance fields cost map

Leave a Comment / C++, Machine Learning, Manipulation, ROS, Tutorials / admin

Rapidly-exploring random trees (RRT) and their variant are a very power solution for solving motion planning problem in robotics, but they suffer from finding an optimise solution and the generated path is usually jerky with redundant movements. Sample based-optimisation-based planners benefit the robustness of RRT and the possibility of imposing a cost function. Here in this work, I

Sample based-optimisation-based planner with signed distance fields cost map Read More »

Solving of optimisation problem with evolutionary algorithm (LM-CMA-ES)

Leave a Comment / C++, Machine Learning, Tutorials / admin

Limited-Memory CMA-ES (LM-CMA-ES) is a variation of CMA-ES which has been tailored for large-scale optimisation with limited memory. As a warm up here, I have used this algorithm to solve the min point of the following function: $$ z= -( 4 \times e^{- ( (x-4)^2 +(y-4)^2 ) }+ 2 \times e^{- ( (x-2)^2 +(y-2)^2 ) } )$$ The function

Solving of optimisation problem with evolutionary algorithm (LM-CMA-ES) Read More »

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