Bin-Picking based on Harmonic Shape Contexts and Graph-Based Matching

In this work we address the general bin-picking problem where 3D data is available. We apply harmonic shape contexts (HSC) features since these are invariant to translation, scale, and 3D rotation. Each object is divided into a number of sub-models each represented by a number of HSC features. These...

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Main Authors: Kirkegaard, J., Moeslund, T.B.
Format: Conference Proceeding
Language:English
Subjects:
Application software
Computer industry
Computer vision
Data mining
Feature extraction
Noise shaping
Robot sensing systems
Robotics and automation
Service robots
Shape
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Moeslund, T.B.
description In this work we address the general bin-picking problem where 3D data is available. We apply harmonic shape contexts (HSC) features since these are invariant to translation, scale, and 3D rotation. Each object is divided into a number of sub-models each represented by a number of HSC features. These are compared with HSC features extracted in the current data using a graph-based scheme. Results show that the approach is somewhat sensitive to noise, but works in presence of occlusion
doi_str_mv 10.1109/ICPR.2006.325
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subjects Application software
Computer industry
Computer vision
Data mining
Feature extraction
Noise shaping
Robot sensing systems
Robotics and automation
Service robots
Shape
title Bin-Picking based on Harmonic Shape Contexts and Graph-Based Matching
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