Affordance-based 3D feature for generic object recognition

M. Iizuka, Shuichi Akizuki, M. Hashimoto

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

Abstract

Techniques for generic object recognition, which targets everyday objects such as cups and spoons, and techniques for approach vector estimation (e.g. estimating grasp position), which are needed for carrying out tasks involving everyday objects, are considered necessary for the perceptual system of service robots. In this research, we design feature for generic object recognition so they can also be applied to approach vector estimation. To carry out tasks involving everyday objects, estimating the function of the target object is critical. Also, as the function of holding liquid is found in all cups, so a function is shared in each type (class) of everyday objects. We thus propose a generic object recognition method that can estimate the approach vector by expressing an object's function as feature. In a test of the generic object recognition of everyday objects, we confirmed that our proposed method had a 92% recognition rate. This rate was 11% higher than the mainstream generic object recognition technique of using convolutional neural network (CNN).

Original languageEnglish
Title of host publicationThirteenth International Conference on Quality Control by Artificial Vision 2017
PublisherSPIE
Volume10338
ISBN (Electronic)9781510611214
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event13th International Conference on Quality Control by Artificial Vision, QCAV 2017 - Tokyo, Japan
Duration: 2017 May 142017 May 16

Other

Other13th International Conference on Quality Control by Artificial Vision, QCAV 2017
CountryJapan
CityTokyo
Period17/5/1417/5/16

Keywords

  • Affordance
  • Generic object recognition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Iizuka, M., Akizuki, S., & Hashimoto, M. (2017). Affordance-based 3D feature for generic object recognition. In Thirteenth International Conference on Quality Control by Artificial Vision 2017 (Vol. 10338). [103380P] SPIE. https://doi.org/10.1117/12.2266917