A missing data estimation approach for small size image sequence

Zhanli Sun, Yuan Fang, L. Shang, Xiantan Zhu
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引用次数: 1

Abstract

Data missing is a frequently encountered problem for structure-from-motion (SFM). In this paper, a sub-sequence based approach is proposed to deal with the missing data estimation problem for small size image sequence. In the proposed method, the sub-sequences are first extracted from the original sequence. Further, multiple weaker estimators are constructed by means of the column space fitting (CSF) algorithm. Finally, the missing entries are estimated by a linear programming based weighted model. Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm.
一种小尺寸图像序列缺失数据估计方法
数据丢失是运动构造(SFM)中经常遇到的问题。本文提出了一种基于子序列的方法来解决小尺寸图像序列的缺失数据估计问题。该方法首先从原始序列中提取子序列。进一步,利用列空间拟合(CSF)算法构造了多个弱估计量。最后,利用基于线性规划的加权模型对缺失条目进行估计。在几个广泛应用的图像序列上的实验结果证明了该算法的有效性和可行性。
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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