正投影模型下基于秩1的遮挡点恢复方法

彭亚丽; 刘侍刚

doi:10.13873/j.1000-97872010.10.017

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正投影模型下基于秩1的遮挡点恢复方法

研究与探讨 | 更新时间：2025-09-11

- 正投影模型下基于秩1的遮挡点恢复方法
- An occlusion recovery method based on rank 1 under orthographic projection model
- 传感器与微系统 2010年29卷第10期页码：39-42
- 作者机构：
  
  1. 西安电子科技大学计算机学院
  2. 陕西师范大学计算机科学学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（60805016）;高等学校博士学科点专项科研基金资助项目（200807181007）
- DOI：10.13873/j.1000-97872010.10.017
  中图分类号： TP391.41
- 纸质出版：2010
- 稿件说明：
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[1]彭亚丽,刘侍刚.正投影模型下基于秩1的遮挡点恢复方法[J].传感器与微系统,2010,29(10):39-42.

PENG Ya-li1, LIU Shi-gang1. An occlusion recovery method based on rank 1 under orthographic projection model[J]. 2010, 29(10): 39-42.
[1]彭亚丽,刘侍刚.正投影模型下基于秩1的遮挡点恢复方法[J].传感器与微系统,2010,29(10):39-42. DOI： 10.13873/j.1000-97872010.10.017.

PENG Ya-li1, LIU Shi-gang1. An occlusion recovery method based on rank 1 under orthographic projection model[J]. 2010, 29(10): 39-42. DOI： 10.13873/j.1000-97872010.10.017.

摘要

为了有效地恢复遮挡点

假设相机为正投影模型

提出了一种基于秩1的遮挡点恢复方法。该方法利用所有图像点组成一个秩1的矩阵

并利用该特性构造一个投影矩阵

利用该投影矩阵求到遮挡点

再将求到的遮挡点代替图像中的遮挡点

经过多次迭代

最后求到遮挡点的真实图像位置。模拟实验和真实实验表明:该方法具有鲁棒性好、收敛性好及误差小等优点。

Abstract

Under orthographic projection

an occlusion recovery method based on rank 1 is presented to recovery occlusions.The projective matrix that is from all the image points including all the occlusions that compose a matrix with rank 1 is used to find all the occlusions.At the same time

the obtaining occlusions substitute the occlusions in image.After several times iterations

the real positions of the occlusions can be found.Both simulation and real experiments show that the method is robust and has good property of convergence with small errors.

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Keywords

references

Exploiting distinctive visual landmark maps in pan–tilt–zoom camera networks [J] . A. Del Bimbo,F. Dini,G. Lisanti,F. Pernici. Computer Vision and Image Understanding . 2010 (6)

Estimating 3D shape from degenerate sequences with missing data [J] . Computer Vision and Image Understanding . 2009 (2)

Optimization Algorithms on Subspaces: Revisiting Missing Data Problem in Low-Rank Matrix [J] . Pei Chen. International Journal of Computer Vision . 2008 (1)

Rank Conditions on the Multiple-View Matrix [J] . International Journal of Computer Vision . 2004 (2)

Shape and motion from image streams under orthography: a factorization method [J] . Carlo Tomasi,Takeo Kanade. International Journal of Computer Vision . 1992 (2)

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