Egomotion estimation as an appearance-based classification problem

Pedro Sánchez, Cornelio Yáñez, Jonathan Pecero, Apolinar Ramírez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper a probabilistic approach is considered to develop a methodology to solve the problem of estimation of the position of the observer. The base of this methodology is the appearance vision with which an environment map is constructed using Kernel PCA. For the experiments an image set is acquired in unknown locations in the same environment. The performance of Kernel PCA technique was tested according to the optimum dimension of the environment model and the quantity of images correctly classified using a Bayesian algorithm. To validate the results obtained with Kernel PCA the same experiments were performed with PCA and APEX techniques, then the results were compared showing that Kernel PCA has better performance than PCA and APEX.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis and Applications - 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006, Proceedings
PublisherSpringer Verlag
Pages743-752
Number of pages10
ISBN (Print)3540465561, 9783540465560
DOIs
StatePublished - 2006
Event11th Iberoamerican Congress in Pattern Recognition, CIARP 2006 - Cancun, Mexico
Duration: 14 Nov 200617 Nov 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4225 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Iberoamerican Congress in Pattern Recognition, CIARP 2006
Country/TerritoryMexico
CityCancun
Period14/11/0617/11/06

Keywords

  • Egomotion estimation
  • Kernel PCA
  • Probabilistic approach

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