Image Stitching Using Non-Extensive Statistics

Everton VIlhena Cardoso, Helmuth A. Risch, Lucas P. Laheras, Vinícius Luiz, P. S. Rodrigues, G. Wachs-Lopes
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引用次数: 1

Abstract

Nowadays there are different ways to make image stitching with help of Fiducial Point Descriptors (FPD), whose find the matches between images for an application, such as SIFT and calculates the homography with RANSAC. However, by finding the right match when we have images on differents points of view could be difficult. This paper introduces the application of q-SFT, a newest variation of SFT in a stitch algorithm, that can recognize large viewpoint changes called as LVC.
使用非广泛统计的图像拼接
目前,利用基准点描述子(Fiducial Point Descriptors, FPD)进行图像拼接的方法多种多样,FPD为应用寻找图像之间的匹配,如SIFT,并利用RANSAC计算图像的单应性。然而,当我们有不同角度的图像时,找到合适的匹配可能是困难的。本文介绍了一种新的SFT算法——q-SFT在针迹算法中的应用,它可以识别视点的大变化,称为LVC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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