计算机工程

• 图形图像处理 • 上一篇    下一篇

一种体视显微图像误匹配消除方法

李 论1,2,王一刚2,范胜利2,白志强1,2,赖建宁1,2   

  1. (1. 太原科技大学电子信息工程学院,太原030024; 2. 浙江大学宁波理工学院信息科学与工程学院,浙江宁波315100)
  • 收稿日期:2014-05-04 出版日期:2015-05-15 发布日期:2015-05-15
  • 作者简介:李 论(1990 - ),男,硕士研究生,主研方向:机器视觉,模式识别;王一刚,副教授、硕士;范胜利,硕士;白志强、赖建宁,硕士 研究生。
  • 基金项目:
    宁波国际合作基金资助项目(2013D10009)。

An Error Match Eliminating Method of Stereo Light Microscopic Images

LI Lun 1,2,WANG Yigang 2,FAN Shengli 2,BAI Zhiqiang 1,2,LAI Jianning 1,2   

  1. (1. School of Electronic Information Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China; 2. School of Information Science and Engineering,Ningbo Institute of Technology,Zhejiang University,Ningbo 315100,China)
  • Received:2014-05-04 Online:2015-05-15 Published:2015-05-15

摘要: 针对尺度不变特征变换(SIFT)匹配中存在的误匹配问题和立体图像特点,提出一种误匹配消除方法。对 体视显微图像进行SIFT 特征匹配初步得到匹配对,结合体视显微镜标定参数,计算三维点云坐标。将三维点云分 别投影到左、右图像中得到新的匹配对,新投影点的图像坐标分别与原来匹配点的图像坐标相减,生成投影向量 集。通过左、右2 个投影向量集幅值和方向的异常值剔除,实现误匹配消除。实验结果表明,实验图像的误匹配消 除率达到100% ,同时不消除正确匹配点,提高了匹配精度。

关键词: 投影向量集, 误匹配消除, 三维点云, 标定, 尺度不变特征变换, 体视显微镜

Abstract: In order to overcome the difficulty of eliminating stereo mismatching pairs on Scale Invariant Feature Transform(SIFT) feature matching,an algorithm aiming to remove error match pairs of Stereo Light Microscope(SLM) images is proposed. SIFT feature matching pairs are roughly found,and then calculate 3D coordinate of point cloud using SLM calibration parameters. New match pairs can be obtained by projecting the point cloud to left,right images and generate projection vector set by subtracting image coordinate of new projection point from one of original point, respectively. Outliers of magnitude and orientation in left,right projection vector sets are eliminated to remove error match pairs. Experimental result presents that the error pairs removal percentage of experimental images in proposed algorithm reaches 100% ,meanwhile it does not eliminate correct matching pairs,improving the precision of stereo match.

Key words: projection vector set, error match eliminating, 3D point cloud, calibration, Scale Invariant Feature Transform(SIFT);Stereo Light Microscope(SLM)

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