Towards bendable augmented maps

Sandy Martedi, Hideo Saito

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

2 Citations (Scopus)

Abstract

Three different kinds assumptions are often used for representing the shape of a paper:rigid, foldable and nonrigid. Nonrigid surface detection is intensively explored as challenging topics which addresses two problems: recovering the paper shape and estimating the camera pose. The state-of-the-art researches try to solve both problems robustly for real time purpose. We propose an augmented reality application that use a nonrigid detection method to recover the shape of the bendable paper using dots as keypoints and estimate the camera pose simultaneously. Our approach recovers the multi-planarity of the paper as the initial shape and iteratively approximates the surface shape. The multi-planarity is estimated by using the tracking by descriptor update method that uses the correspondence between captured and reference keypoints. We then optimize the shape using the progressive finite newton optimization method.

Original languageEnglish
Title of host publicationProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
Pages566-569
Number of pages4
Publication statusPublished - 2011 Dec 1
Event12th IAPR Conference on Machine Vision Applications, MVA 2011 - Nara, Japan
Duration: 2011 Jun 132011 Jun 15

Publication series

NameProceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011

Other

Other12th IAPR Conference on Machine Vision Applications, MVA 2011
CountryJapan
CityNara
Period11/6/1311/6/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Towards bendable augmented maps'. Together they form a unique fingerprint.

  • Cite this

    Martedi, S., & Saito, H. (2011). Towards bendable augmented maps. In Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011 (pp. 566-569). (Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011).