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Tag Archives: computer vision
Randomized Hough Transform 2: Rototranslations
Here I briefly explained how the RHT works, with reference to a very simple case (finding the directions of rows and columns of a grid). Now I’ll spend some words on a more complex application. When scanning the surface of … Continue reading
Gaze tracking as a novel input method
Smartphones and tablets usually have a camera on their back, to take photographs, and a frontal camera for videoconferencing. In a recent model (Samsung Galaxy S4) the frontal camera can be used as an input device too: … Continue reading
Posted in Uncategorized
Tagged computer vision, cone, digital camera, ellipse, eye, fitting, gaze tracking, iris, least squares, outlier, RANSAC, shape, smartphone, tablet
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Randomized Hough Transform
The Hough transform can be used to extract shapes from images. As an example, consider a binary image (pixel values can be only 0 – black, belonging to the background – or 1 – white, belonging to the shape) from … Continue reading
Reconstruction of 3D points from images in a calibrated binocular system
In a binocular system one can write and where is an object point in homogeneous coordinates in mm (*), and are its image points in homogeneous coordinates in pixels, and are the projection matrices of the two cameras, and are … Continue reading
Vanishing points in presence of noise
Most selfcalibration algorithms require a prior knowledge of the camera calibration matrix ; as an instance, you need it to normalize the image points as and therefore fit the essential matrix . With most commercial cameras it is safe to … Continue reading
Posted in Uncategorized
Tagged Alciatore and Miranda, calibration matrix, computer vision, constrained minimum, essential matrix, fitting, focal length, image of the absolute conic, Lagrange multipliers, lagrangian, least squares, noise, nonlinear regression, outlier, pixel pitch, principal point, RANSAC, vanishing point
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Forward bundle adjustment/2
Some more notes about my implementation of the forward bundle adjustment algorithm (see my previous post). Monocular systems. According to Mitchell, Warren, McKinnon and Upcroft, the original algorithm does not work with monocular systems, due to a limitation of the subalgorithm used … Continue reading
Forward bundle adjustment
In a recent post I wrote about the essential matrix. If the internal parameters (pixel pitch, focal length, principal point) of both cameras in a stereo rig are known, the essential matrix can be obtained from at least eight couples … Continue reading