[1] Li Zhongke, Yang Xiaohui, Wu Lenan,. A method of automatic plane detection without random search [J]. Journal of Southeast University (English Edition), 2003, 19 (3): 216-220. [doi:10.3969/j.issn.1003-7985.2003.03.003]
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A method of automatic plane detection without random search(
)
一种无需随机搜索策略的自动平面检测方法
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 19
- Issue:
- 2003 3
- Page:
- 216-220
- Research Field:
- Computer Science and Engineering
- Publishing date:
- 2003-09-30
Info
- Title:
- A method of automatic plane detection without random search
- 一种无需随机搜索策略的自动平面检测方法
- Author(s):
- Li Zhongke, Yang Xiaohui, Wu Lenan
- Department of Radio Engineering, Southeast University, Nanjing 210096, China
- Keywords:
- plane detection; feature matching; plane homography; computer vision
- PACS:
- TP391.4
- DOI:
- 10.3969/j.issn.1003-7985.2003.03.003
- Abstract:
- Plane detection is a prerequisite for many computer vision tasks. This paper proposes a new method which can automatically detect planes from two projective images. Firstly, we modify Scott’s feature point matching method by post-processing its result with the concept of similarity, and then get the lines matching according to feature points matching based on the approximate invariance of the features’ distribution between two images. Finally, we group all feature points into subsets in terms of their geometric relations with feature lines as initial sets to estimate homography rather than by a random search strategy(like RANSAC)as in most existing methods. The proposed method is especially suitable to detecting planes in man-made scenes. This method is validated on real images.
- 本文提出一种新的能快速从2幅投影图像中自动检测平面的方法.首先通过引入互相关对Scatt特征点匹配结果作后处理得到可靠的点点匹配, 然后根据2幅图片间特征分布的近似不变性从特征点匹配得到特征线匹配, 继而将特征点按照与特征线的几何关系进行分组, 作为初始集合估计平面单应矩阵, 将特征点按所属平面分组.此方法能有效地避免随机的全局搜索.此方法尤其适于人造场景的平面检测问题.实验表明了此方法的有效性.
References:
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Memo
- Memo:
- Biographies: Li Zhongke(1976—), male, graduate; Wu Lenan(corresponding author), male, doctor, professor, wuln@seu.edu.cn.