A Recognition Algorithm of Generic Objects Based on Feature-Level Fusion of 2D and 3D SIFT Descriptors

LI Xin-de; LIU Miao-miao; XU Ye-fan; LUO Chao-min

doi:10.3969/j.issn.0372-2112.2015.11.021

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A Recognition Algorithm of Generic Objects Based on Feature-Level Fusion of 2D and 3D SIFT Descriptors

更新时间：2025-07-16

- A Recognition Algorithm of Generic Objects Based on Feature-Level Fusion of 2D and 3D SIFT Descriptors
- Acta Electronica Sinica Vol. 43, Issue 11, Pages: 2277-2283(2015)
- 作者机构：
  
  1. 东南大学自动化学院复杂工程测量与控制教育部重点实验室,江苏,南京,210096
  2. 底特律大学电子与计算机工程系,底特律,美国,48221
  3. 东南大学自动化学院复杂工程测量与控制教育部重点实验室,江苏,南京,210096
  4. 底特律大学电子与计算机工程系,底特律,美国,48221
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.11.021
  CLC： TP391.4
- Published：2015
- 稿件说明：
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LI Xin-de, LIU Miao-miao, XU Ye-fan, et al. A Recognition Algorithm of Generic Objects Based on Feature-Level Fusion of 2D and 3D SIFT Descriptors[J]. Acta Electronica Sinica, 2015, 43(11): 2277-2283.
DOI：

LI Xin-de, LIU Miao-miao, XU Ye-fan, et al. A Recognition Algorithm of Generic Objects Based on Feature-Level Fusion of 2D and 3D SIFT Descriptors[J]. Acta Electronica Sinica, 2015, 43(11): 2277-2283. DOI： 10.3969/j.issn.0372-2112.2015.11.021.

摘要

如何选择合适的特征表示一般物体类间差异和类内共性至关重要

因此

本文在2D SIFT(Scale Invariant Feature Transform

SIFT)的基础上

提出了基于点云模型的3D SIFT特征描述子

进而提出一种基于2D和3D SIFT特征级融合的一般物体识别算法.分别提取物体2维图像和3维点云的2D和3D SIFT特征描述子

利用词袋(Bag of Words

BoW)模型得到物体特征向量

根据特征级融合将两个特征向量进行融合实现物体描述

运用有监督分类器支持向量机(Support Vector Machine

SVM)实现分类识别

给出最终识别结果.最后

实验验证了本文提出算法的好处.

Abstract

How to choose the appropriate feature to represent differences between classes and the common within class of generic objects is of great importance.So the 3D SIFT(scale invariant feature transform) descriptors of point clouds model based on the 2D SIFT is proposed.Then we propose a new algorithm based on multiple feature fusion of 2D and 3D SIFT descriptors respectively drawn from 2D images and 3D point clouds.The BoW(bag of words) model is used to calculate feature vectors and describe the objects according to the multiple feature fusion.The supervised support vector machine(SVM) classier is used to classify objects.Through several experiments

the advantage of this new algorithm is testified.

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