Occlusion Recovery Method Based on Projective Constraints

LIU Shi-gang; PENG Ya-li

doi:10.3969/j.issn.0372-2112.2017.11.006

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Occlusion Recovery Method Based on Projective Constraints

更新时间：2025-07-16

- Occlusion Recovery Method Based on Projective Constraints
- Acta Electronica Sinica Vol. 45, Issue 11, Pages: 2611-2616(2017)
- 作者机构：
  
  1. 现代教学技术教育部重点实验室,陕西,西安,710062
  2. 陕西省教学信息技术工程实验室,陕西,西安,710119
  3. 陕西师范大学计算机科学学院,陕西,西安,710119
  4. 现代教学技术教育部重点实验室,陕西,西安,710062
  5. 陕西省教学信息技术工程实验室,陕西,西安,710119
  6. 陕西师范大学计算机科学学院,陕西,西安,710119
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61672333, No.61402274);Industrial Science and Technology Research and Development Project of Shaanxi Province (No.2016GY-081);Scientific Research and Development Program of Shaanxi Province (No.2016NY-176);Key Science and Technology Innovation Team of Shaanxi Province (No.2014KTS-18);Supported by the Learning Science Interdisciplinary Program for Key Laboratory of Modern Teaching Technology of Ministry of Education of China
- DOI：10.3969/j.issn.0372-2112.2017.11.006
  CLC： TP391.41P232
- Published Online：25 November 2017，
  
  Published：2017
- 稿件说明：
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LIU Shi-gang, PENG Ya-li. Occlusion Recovery Method Based on Projective Constraints[J]. Acta Electronica Sinica, 2017, 45(11): 2611-2616.
DOI：

LIU Shi-gang, PENG Ya-li. Occlusion Recovery Method Based on Projective Constraints[J]. Acta Electronica Sinica, 2017, 45(11): 2611-2616. DOI： 10.3969/j.issn.0372-2112.2017.11.006.

摘要

为了恢复图像中的遮挡点，本文在相机为正投影模型下，提出了一种投影约束的遮挡点恢复方法.该方法利用图像矩阵的行空间和列空间都是三维子空间的特性，通过用矩阵奇异值分解分别得到图像矩阵行和列投影满足的约束条件，将遮挡点的求解转化为迭代求解二次型的极值问题.仿真实验和真实实验结果表明，本文方法具有收敛速度快，恢复精度高等优点.

Abstract

To recover the position of occlusion

an occlusion recovery method based on projective constraints under orthographic projection is presented.Utilizing the property that both the row and the column space of image matrix are projective subspaces

the method applies Singular Value Decomposition(SVD) to get the image matrix's row and column metric constraints

and replaces occlusion solution by iteratively solving the minimum of a quadratic function.The experiments with both simulated and real data show that the proposed method has the advantages of fast convergence speed and small error.

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