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Compensating for visually missing features: Scale adaptive recognition of objects using probabilistic voting

  • Hyundai Heavy Industries Co., Ltd
  • Electronics and Telecommunications Research Institute

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work-in-progress paper, we present an efficient methodology for a scale-adaptive recognition of objects. We introduce a new object recognition approach, which detects an object in a scene while probabilistically predicting visually missing features. The idea is to enable a better recognition by considering the fact that object features may not be detected depending on its situation (e.g. distance and occlusion). A probabilistic voting-based methodology is developed.

Original languageEnglish
Title of host publicationURAI 2011 - 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence
Pages287-288
Number of pages2
DOIs
StatePublished - 2011
Event2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2011 - Incheon, Korea, Republic of
Duration: Nov 23 2011Nov 26 2011

Publication series

NameURAI 2011 - 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence

Conference

Conference2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2011
Country/TerritoryKorea, Republic of
CityIncheon
Period11/23/1111/26/11

Keywords

  • Object recognition
  • Visually missing features

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