Paper Info
Title
Model-Based Work-Piece Localization With Salient Feature Selection
Abstract
this paper presents a model-based work-piece localization method with salient feature selection. Model-based localization is suitable for work-piece which is one kind of the typical 3D rigid objects with less texture. However, localization based on 3D model will cause high failure rate in heavily cluttered scenes. We propose a new model-based localization method, which is integrated with salient feature selection. Two different models: 3D model and training images are used, and the salient feature selection procedure extracts the regions which may contain the objects potentially. Experiments demonstrate the effectiveness of the proposed method.
Year
Venue
Keywords
2013
2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO)
pose estimation,feature extraction,cad
Field
DocType
Citations 
CAD,Object detection,Computer vision,Pattern recognition,Feature selection,Solid modelling,Failure rate,Pose,Feature extraction,Artificial intelligence,Engineering,Salient
Conference
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Wenjun Zhu1184.98
Zhengke Qin242.10
Peng Wang3318.02
Hong Qiao41147110.95