Design of the 3D Object Recognition System with Hierarchical Feature Learning


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 1, pp. 13-20, Jan. 2016
10.3745/KTSDE.2016.5.1.13, Full Text:

Abstract

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.


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Cite this article
[IEEE Style]
J. H. Kim, D. H. Kim and I. C. Kim, "Design of the 3D Object Recognition System with Hierarchical Feature Learning," KIPS Transactions on Software and Data Engineering, vol. 5, no. 1, pp. 13-20, 2016. DOI: 10.3745/KTSDE.2016.5.1.13.

[ACM Style]
Joo Hee Kim, Dong Ha Kim, and In Cheol Kim. 2016. Design of the 3D Object Recognition System with Hierarchical Feature Learning. KIPS Transactions on Software and Data Engineering, 5, 1, (2016), 13-20. DOI: 10.3745/KTSDE.2016.5.1.13.