22 June 2015 The algorithm for generation of panoramic images for omnidirectional cameras
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Abstract
The omnidirectional cameras are used in areas where large field-of-view is important. Omnidirectional cameras can give a complete view of 360° along one of direction. But the distortion of omnidirectional cameras is great, which makes omnidirectional image unreadable. One way to view omnidirectional images in a readable form is the generation of panoramic images from omnidirectional images. At the same time panorama keeps the main advantage of the omnidirectional image - a large field of view. The algorithm for generation panoramas from omnidirectional images consists of several steps. Panoramas can be described as projections onto cylinders, spheres, cubes, or other surfaces that surround a viewing point. In practice, the most commonly used cylindrical, spherical and cubic panoramas. So at the first step we describe panoramas field-of-view by creating virtual surface (cylinder, sphere or cube) from matrix of 3d points in virtual object space. Then we create mapping table by finding coordinates of image points for those 3d points on omnidirectional image by using projection function. At the last step we generate panorama pixel-by-pixel image from original omnidirectional image by using of mapping table. In order to find the projection function of omnidirectional camera we used the calibration procedure, developed by Davide Scaramuzza – Omnidirectional Camera Calibration Toolbox for Matlab. After the calibration, the toolbox provides two functions which express the relation between a given pixel point and its projection onto the unit sphere. After first run of the algorithm we obtain mapping table. This mapping table can be used for real time generation of panoramic images with minimal cost of CPU time.
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Vasiliy P. Lazarenko, Vasiliy P. Lazarenko, Sergey Yarishev, Sergey Yarishev, Valeriy Korotaev, Valeriy Korotaev, } "The algorithm for generation of panoramic images for omnidirectional cameras", Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 95300K (22 June 2015); doi: 10.1117/12.2184584; https://doi.org/10.1117/12.2184584
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