Object Location for Binocular Stereo Vision Based on Improved SIFT Feature

Li De-long; Liu Wei

doi:10.12052/gdutxb.150108

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Object Location for Binocular Stereo Vision Based on Improved SIFT Feature

更新时间：2026-05-27

- Object Location for Binocular Stereo Vision Based on Improved SIFT Feature
- Vol. 34, Issue 1, Pages: 90-94(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.150108
  CLC： TP242
- Online First：09 January 2017，
  
  Published：2017
- 稿件说明：
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李德隆, 刘伟. 基于改进的SIFT特征点的双目定位[J]. 广东工业大学学报, 2017, 34(1): 90-94.

Li De-long, Liu Wei. Object Location for Binocular Stereo Vision Based on Improved SIFT Feature[J]. 2017, 34(1): 90-94.
李德隆, 刘伟. 基于改进的SIFT特征点的双目定位[J]. 广东工业大学学报, 2017, 34(1): 90-94. DOI： 10.12052/gdutxb.150108.

Li De-long, Liu Wei. Object Location for Binocular Stereo Vision Based on Improved SIFT Feature[J]. 2017, 34(1): 90-94. DOI： 10.12052/gdutxb.150108.

摘要

立体匹配是双目视觉中最重要的步骤，同时也是最难解决的一个问题.传统的区域立体匹配得到的视差图对无纹理区域难以保留深度不连续的特性，获得目标三维信息速度慢、不准确.本文提出一种改进的SIFT算法，首先通过对采集的左右图像进行极线约束，其次从左图中选择目标区域ROI，再降低特征向量维数来减少耗时，采用基于KD树的BBF算法加快匹配速度，最后用RANSAC算法去除误匹配.实验表明改进的SIFT算法能够迅速准确地得到目标物的特征点，从而利用三角测量法快速地计算出目标三维坐标.

Abstract

Stereo matching is the most important step in binocular vision

and it is also the most difficult problem to solve. The traditional stereo matching to get the regional disparity map is just passable

for the characteristics of the non-texture regions are difficult to retain the depth map discontinuing

so getting the 3D information of the target is slow and inaccurate

Therefore

a new SIFT image matching is proposed

firstly

making the epipolar constraint on the left and right image; secondly

selecting the ROI of target from the left of binocular images

and reducing running time by reducing the dimension of feature vectors and accelerating the matching speed by using BBF algorithm based on KD tree

and finally

removing the false matching by using RANSAC algorithm. The experiment results show that the improved SIFT algorithm can get the target's feature points quickly and accurately

so the 3D coordinates can be calculated by the triangulation method speedy.

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references

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