Dynamic feature ordering for efficient registration

Tat Jen Cham, James M. Rehg

Research output: Contribution to journalConference articlepeer-review


Existing sequential feature-based registration algorithms involving search typically either select features randomly (eg. the RANSAC[8] approach) or assume a predefined, intuitive ordering for the features (eg. based on size or resolution). This paper presents a formal framework for computing an ordering for features which maximizes search efficiency. Features are ranked according to matching ambiguity measure, and an algorithm is proposed which couples the feature selection with the parameter estimation, resulting in a dynamic feature ordering. The analysis is extended to template features where the matching is non-discrete and a sample-refinement process is proposed. The framework is demonstrated effectively on the localization of a person in an image, using a kinematic model with template features. Different priors are used on the model parameters and the results demonstrate nontrivial variations in the optimal feature hierarchy.

Original languageEnglish (US)
Pages (from-to)1084-1091
Number of pages8
JournalProceedings of the IEEE International Conference on Computer Vision
StatePublished - 1999
Externally publishedYes
EventProceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99) - Kerkyra, Greece
Duration: Sep 20 1999Sep 27 1999

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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