This paper proposes an improvement for an existing and widely spread approach of panorama stitching for images of planar objects. The proposed method is based on projective transformations graph adjustment. Evaluation is presented on a heterogeneous dataset which contains images of Earth’s and Mars’s surfaces, images taken using a microscope, as well as handwritten and printed text documents. Quality enhancement of panorama stitching method is illustrated on this dataset and shows more than twofold reduction in the accumulated computation error of projective transformations.
In this paper we describe stitching protocol, which allows to obtain high resolution images of long length monochromatic objects with periodic structure. This protocol can be used for long length documents or human-induced objects in satellite images of uninhabitable regions like Arctic regions. The length of such objects can reach notable values, while modern camera sensors have limited resolution and are not able to provide good enough image of the whole object for further processing, e.g. using in OCR system. The idea of the proposed method is to acquire a video stream containing full object in high resolution and use image stitching. We expect the scanned object to have straight boundaries and periodic structure, which allow us to introduce regularization to the stitching problem and adapt algorithm for limited computational power of mobile and embedded CPUs. With the help of detected boundaries and structure we estimate homography between frames and use this information to reduce complexity of stitching. We demonstrate our algorithm on mobile device and show image processing speed of 2 fps on Samsung Exynos 5422 processor
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