一种基于2D和3D SIFT特征级融合的一般物体识别算法

A Recognition Algorithm of Generic Objects Based on FeatureLevel Fusion of 2D and 3D SIFT Descriptors

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,Bo W)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.

Howto 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 newalgorithm based on multiple feature fusion of 2D and 3D SIFT descriptors respectively drawn from 2D images and 3D point clouds. The Bo W（ 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 newalgorithm is testified.