Objects relationship modeling for improving object detection using bayesian network integration

Youn Suk Song, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Object detection is very important to service robots. Many tasks for service such as delivery, cleaning, and health-care for elderly people are strongly related to objects. Conventional approaches for object detection are mainly based on the geometric models, because they have been applied to static environments. In indoor environments having uncertainty, they have limitation in some situations where interesting objects are occluded by other ones or small in the scene. Context information can be helpful to overcome these uncertain situations. In this paper, we adopt objects as context information to allow for service robots to predict the probability of interesting objects through observed ones. For this, an object relationship model based on Bayesian network (BN) and integration method are proposed. Experimental results confirm that the proposed method predicts the objects very well.

Original languageEnglish
Title of host publicationInternational Conference on Intelligent Computing, ICIC 2006, Proceedings
PublisherSpringer Verlag
Number of pages7
ISBN (Print)3540372717, 9783540372714
Publication statusPublished - 2006
EventInternational Conference on Intelligent Computing, ICIC 2006 - Kunming, China
Duration: 2006 Aug 162006 Aug 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4113 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Conference on Intelligent Computing, ICIC 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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