research-article

An adaptive evidence structure for Bayesian recognition of 3D objects

Authors:

Ahmed M. Naguib,

Sukhan LeeAuthors Info & Claims

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

Article No.: 75, Pages 1 - 8

https://doi.org/10.1145/2701126.2701160

Published: 08 January 2015 Publication History

Abstract

Classification of an object under various environmental conditions is a challenge for developing a reliable service robot. In this work, we show problems of using simple Naïve Bayesian classifier and propose a Tree-Augmented Naïve (TAN) Bayesian Network -- based classifier. We separate feature space into binary TRUE/FALSE regions which allows us to drive Bayesian inference prior conditional probabilities from statistical database. We go further using TRUE/FALSE regions to estimate expected posterior probabilities of each object under online specific conditions. These expectations are then used to select optimal feature sets under this environment and autonomously reconstruct Bayesian Network. Experimental results, validation and comparison show the performance of the proposed system.

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Recognition System 3.6 Demo: http://youtu.be/VRskOQVUDd0

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Recognition System 4.0 Demo: http://youtu.be/ZH9c_DycnI4

Cited By

Lee SNaguib A(2019)Toward a Sociable and Dependable Elderly Care Robot: Design, Implementation and User StudyJournal of Intelligent & Robotic Systems10.1007/s10846-019-01028-8Online publication date: 11-May-2019
https://doi.org/10.1007/s10846-019-01028-8
Carvalho Lvon Wangenheim A(2019)3D object recognition and classification: a systematic literature reviewPattern Analysis and Applications10.1007/s10044-019-00804-4Online publication date: 27-Feb-2019
https://doi.org/10.1007/s10044-019-00804-4
Lee SNaguib AIslam N(2018)3D Deep Object Recognition and Semantic Understanding for Visually-Guided Robotic Service2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2018.8593985(903-910)Online publication date: Oct-2018
https://doi.org/10.1109/IROS.2018.8593985
Show More Cited By

Index Terms

An adaptive evidence structure for Bayesian recognition of 3D objects
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

January 2015

674 pages

ISBN:9781450333771

DOI:10.1145/2701126

Conference Chairs:
Dongsoo S. Kim
Indiana University
,
Sang-wook Kim
Hanyang University, Korea
,
General Chairs:
Suk-Han Lee
Sungkyunkwan Univerisity, Korea
,
Lajos Hanzo
University of Southampton, UK
,
Roslan Ismail
Universiti Kuala Lumpur, Malaysia

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2015

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SIGAPP

IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication

January 8 - 10, 2015

Bali, Indonesia

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Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Lee SNaguib A(2019)Toward a Sociable and Dependable Elderly Care Robot: Design, Implementation and User StudyJournal of Intelligent & Robotic Systems10.1007/s10846-019-01028-8Online publication date: 11-May-2019
https://doi.org/10.1007/s10846-019-01028-8
Carvalho Lvon Wangenheim A(2019)3D object recognition and classification: a systematic literature reviewPattern Analysis and Applications10.1007/s10044-019-00804-4Online publication date: 27-Feb-2019
https://doi.org/10.1007/s10044-019-00804-4
Lee SNaguib AIslam N(2018)3D Deep Object Recognition and Semantic Understanding for Visually-Guided Robotic Service2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2018.8593985(903-910)Online publication date: Oct-2018
https://doi.org/10.1109/IROS.2018.8593985
Wei LLee S(2016)3D peak based long range rover localization2016 7th International Conference on Mechanical and Aerospace Engineering (ICMAE)10.1109/ICMAE.2016.7549610(600-604)Online publication date: Jul-2016
https://doi.org/10.1109/ICMAE.2016.7549610

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