A 3D feature model for image matching

Zachary Sun, Nadya Bliss, Karl Ni

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

2 Scopus citations

Abstract

The proposed algorithm identifies whether or not a test photo belongs to a set of co-located training images based on its spatial proximity to the training set. We leverage concepts from Lowe's SIFT and Snavely's Photo Tourism algorithms, and match an image by its 2D features to the 3D features representing the training set. To reduce complexity and increase efficiency, the proposed algorithm implements a compact representation of the image set by merging collections similar features. Test images are then matched with the derived structure. Finally, a decision statistic is determined based on the percentage of features that match. Receiver operating characteristics, computational analysis, and distributions are included in the performance analysis.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2194-2197
Number of pages4
ISBN (Print)9781424442966
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • 3D matching
  • Approximate nearest neighbor
  • SIFT
  • Structure from motion

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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