Feature selection for position estimation using an omnidirectional camera

Huan N. Do, Mahdi Jadaliha, Jongeun Choi, Chae Young Lim

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


This paper considers visual feature selection to implement position estimation using an omnidirectional camera. The localization is based on a maximum likelihood estimation (MLE) with a map from optimally selected visual features using Gaussian process (GP) regression. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP with unknown hyperparameters. The hyperparameters are identified through the learning process by an MLE, which are used for prediction in an empirical Bayes fashion. To select features, we apply a backward sequential elimination technique in order to improve the quality of the position estimation with compressed features for efficient localization. The excellent results of the proposed algorithm are illustrated by the experimental studies with different visual features under both indoor and outdoor real-world scenarios.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalImage and Vision Computing
Publication statusPublished - 2015 Jul 1

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Vision and Pattern Recognition


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