image processing Archives - Robotics with ROS

Open source Structure-from-Motion and Multi-View Stereo tools with C++

Leave a Comment / C++, Computer Vision, Image Processing, Linear Algebra, Tutorials / admin

Structure-from-Motion (SFM) is genuinely an interesting topic in computer vision, Basically making 3D structure from something 2D is absolutely mesmerizing 🙂 There two open source yet very robust tools for SFM, which sometimes compiling them might be complicated, here I will share my experience with you. 1)VisualSFM Prerequisite: 1)Glew Download the glew from SF at http://glew.sourceforge.net/. Do NOT get it from Github as […]

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Peak Signal-to-Noise Ratio (PSNR) in Image using OpenCV and Matlab

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Peak signal-to-noise ratio (PSNR) shows the ratio between the maximum possible power of a signal and the power of the same image with noise. PSNR is usually expressed in logarithmic decibel scale. \( MSE =1/m*n \sum_{i=0}^{m-1} \sum_{j=0}^{n-1} [ Image( i,j) -NoisyImage( i,j) ] ^2 \) \( PSNR =10* \log_{10} \Bigg(MAXI^2/MSE\Bigg) \) MSE is Mean Square Error and MAXI is

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Seam Carving Algorithm for Content-Aware Image Resizing with Matlab Code

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

Seam carving is an algorithm for resizing images while keeping the most prominent and conspicuous pixels in the image. The important pixels in an image are usually those who are located over horizontal or vertical edges, so to throw away some pixels we first find horizontal and vertical edges and store their magnitude as pixel

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HARRIS Corner detector explained

4 Comments / Computer Vision, Image Processing, Linear Algebra, Machine Learning, Tutorials / admin

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);

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);

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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]

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Effect of focal length on image

Leave a Comment / Computer Vision, Image Processing / admin

Have you ever wondered why the selfie that you are taking with your phone is so bad while when you taking a photo a decent Nikon or Canon DSLR you get a nice portrait? This happens due to the size of the sensor and focal length. Since the sensor in your cell-phone is very small

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Creating 3D model of buildings and monuments using structure from motion

Leave a Comment / Computer Vision, Image Processing / admin

Creating 3D model of buildings and monuments using structure from motion Read More »

Creating Hybrid Image with Matlab

Leave a Comment / Matlab, Signal Processing, Tutorials / admin

In this tutorial, I’m gonna show you how to create hybrid images . Basically, you need to filter your first image with high pass filter and the second image with a low pass filter and then add theses two images. The following code shows how to do that. The full code is available at my GitHub.

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Stitching image using SIFT and Homography

Leave a Comment / Computer Vision, Image Processing, Linear Algebra, Matlab, Tutorials / admin

This Matlab tutorial I use SIFT, RANSAC, and homography to find corresponding points between two images. Here I have used vlfeat to find SIFT features. Full code is available at my GitHub repository Major steps are: 0.Adding vlfeat to your Matlab workspace:

run('<path_to_vlfeat>/toolbox/vl_setup')

1	run('<path_to_vlfeat>/toolbox/vl_setup')

1.Detect key points and extract descriptors. In the image below you can see some SIFT key

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Colour based object Tracking with OpenCV

1 Comment / C++, Computer Vision, Image Processing, Tutorials / admin

In many applications, you need to track an object. One simple method is color based tracking. I have developed a simple tool for that with OpenCV. All you have to do is just to adjust the High and Low values of HSV slider in the left window till you filter the image and you only see your

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