Accurate Estimation of Missing Data under Noise Distribution

Abstract

3D contents have been becoming one of attractive multimedia and reality. In computer vision, 2D-to-3D conversion techniques require estimating missing data from noisy observations. When there is no missing data in the observation matrix, the accurate solution of such problem is known to be given by singular value decomposition (SVD). In the case of converting already recorded monoscopic video contents to 3D, several entries of the matrix have not been observed. Therefore, the problem has no simple solution, so it is necessary to estimate missing data. In this paper, we propose an estimation algorithm of missing data with minimizing the influence of noise embedded when tracking feature points from partial observations. The proposed method is an iterative affine SVD factorization method which can estimate the model parameters, given an incomplete set of the observation matrix. The main idea of our algorithm is to estimate missing data accurately even under noise distribution by using geometrical correlations between 2D and 3D error space. This paper consists of three main phases: geometrical correlations for estimating missing data, estimation algorithm, and analyzing the results for video sequences. The accurate results in practical situations as demonstrated here with synthetic and real video sequences show the efficiency and flexibility of the proposed method.