A Prioritized Gauss-Seidel Method for Dense Correspondence Estimation and Motion Segmentation in Crowded Urban Areas with a Moving Depth Camera
Date Issued
2014
Date
2014
Author(s)
Chiang, Yi
Abstract
Dense RGB-D video motion segmentation is an important preprocessing module in computer vision, image processing and robotics. A motion segmentation algorithm based on an optimization framework which utilizes depth information only is presented in this thesis. The proposed optimization framework segments and estimates rigid motion parameters of each locally rigid moving objects with coherent motion. The proposed method also calculates dense point correspondences while performing segmentation. An efficient numerical algorithm based on Constrained Block Nonlinear Gauss-Seidel (CNLGS) algorithm [1] and Prioritized Step Search [2] is proposed to solve the optimization problem. It classifies variables including point correspondences into groups and determines the ordering of variables to optimize. We prove the proposed numerical algorithm to converge to a theoretical
bound. The proposed algorithm works well with a moving camera in highly dynamic urban scenes with non-rigid moving objects.
Subjects
對應
分割
深度
相機
高斯
賽得爾
Type
thesis
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