Authors:
Fatima Zahra Ouadiay
;
Nabila Zrira
;
El Houssine Bouyakhf
and
M. Majid Himmi
Affiliation:
Faculty of Sciences and Mohammed V University, Morocco
Keyword(s):
Real 3D Object Recognition, Categorization, Deep Belief Network, PCL, 3D SIFT, SHOT, CSHOT.
Related
Ontology
Subjects/Areas/Topics:
Image Processing
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
3D object recognition and categorization are an important problem in computer vision field. Indeed, this is
an area that allows many applications in diverse real problems as robotics, aerospace, automotive industry
and food industry. Our contribution focuses on real 3D object recognition and categorization using the Deep
Belief Networks method (DBN). We extract descriptors from cloud keypoints, then we train the resulting
vectors with DBN. We evaluate the performance of this contribution on two datasets, Washington RGB-D
object dataset and our own real 3D object dataset. The second one is built from real objects, following the
same acquisition conditions than those used for Washington dataset acquisition. By this proposed approach, a
DBN could be designed to treat the high-level features for real 3D object recognition and categorization. The
experiment results on standard dataset show that our method outperforms the state-of-the-art used in the 3D
object recognition and categorization.