Usually, I write about robotics, computer vision and machine learning in this website but today I decided to make back of my website in my PC and I felt it’s not bad ti know a bit about XAMPP and especially Apache. 1) First, download and install XAMPP 2) Then edit “/opt/lampp/etc/httpd.conf” and uncomment the line:
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# Virtual hosts Include etc/extra/httpd-vhosts.conf |
[…]
Creating Virtual Directory in Apache Read More »
PS4 controllers work out of the box in Ubuntu with USB cable but I was looking for a way to get it work via Bluetooth as well. After installing a couple of packages I found “ds4drv”. To install it:
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sudo pip install ds4drv |
Then you have to either add the user to the list of root user or simply run
Connecting PS4 Controller dualshock via Bluetooth in Ubuntu Read More »
In this tutorial, I explain the “Maximum likelihood” and MLE (maximum likelihood estimation) for binomial and Gaussian distribution.
Maximum likelihood estimation explained Read More »
In this video, I explain the “Naive Bayes Classifier”. The example has been solved with phyton in my other post here
Naive Bayes Classifier Explained Read More »
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im = imread('chessboard.jpg'); im = double(rgb2gray(im))/255; sigma = 5; g = fspecial('gaussian', 2*sigma*3+1, sigma); dx = [-1 0 1;-1 0 1; -1 0 1]; Ix = imfilter(im, dx, 'symmetric', 'same'); Iy = imfilter(im, dx', 'symmetric', 'same'); Ix2 = imfilter(Ix.^2, g, 'symmetric', 'same'); Iy2 = imfilter(Iy.^2, g, 'symmetric', 'same'); Ixy = imfilter(Ix.*Iy, g, 'symmetric', 'same'); k = 0.04; % k is between 0.04 and 0.06 % --- r = Det(M) - kTrace(M)^2 --- r = (Ix2.*Iy2 - Ixy.^2) - k*(Ix2 + Iy2).^2; norm_r = r - min(r(:)); norm_r = norm_r ./ max(norm_r(:)); subplot(1,2,1), imshow(im) subplot(1,2,2), imshow(norm_r) %hLocalMax = vision.LocalMaximaFinder; %hLocalMax.MaximumNumLocalMaxima = 100; %hLocalMax.NeighborhoodSize = [3 3]; %hLocalMax.Threshold = 0.05; %location = step(hLocalMax, norm_r); |
HARRIS Corner detector explained Read More »
In the below example I implemented a “Naive Bayes classifier” in python and in the following I used “sklearn” package to solve it again: and the output is:
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male posterior is: 1.54428667821e-07 female posterior is: 0.999999845571 Then our data must belong to the female class Then our data must belong to the class number: [2] |
Naive Bayes Classifier Example with Python Code Read More »
In this tutorial, I explain the math and theory of robot localization and I will solve an example of Markov localization.
Markov Localization Explained Read More »
In this tutorial, I will explain the maths behind the Kalman Filter and I will drive the equations and their parameters.
Kalman Filter Explained Read More »
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. It starts with an arbitrary starting point that has not been visited. This point’s epsilon-neighborhood is retrieved, and if it
Density-Based Spatial Clustering (DBSCAN) with Python Code Read More »
In this tutorial I explain the bayes filter from scratch:
Bayes Filter Explained Read More »
