Multiple view feature descriptors from image sequences via kernel principal component analysis

Jason Meltzer, Ming Hsuan Yang, Rakesh Gupta, Stefano Soatto

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

We present a method for learning feature descriptors using multiple images, motivated by the problems of mobile robot navigation and localization. The technique uses the relative simplicity of small baseline tracking in image sequences to develop descriptors suitable for the more challenging task of wide baseline matching across significant viewpoint changes. The variations in the appearance of each feature are learned using kernel principal component analysis (KPCA) over the course of image sequences. An approximate version of KPCA is applied to reduce the computational complexity of the algorithms and yield a compact representation. Our experiments demonstrate robustness to wide appearance variations on non-planar surfaces, including changes in illumination, viewpoint, scale, and geometry of the scene.

Original languageEnglish
Pages (from-to)215-227
Number of pages13
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3021
Publication statusPublished - 2004 Dec 1

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Kernel Principal Component Analysis
Image Sequence
Principal component analysis
Descriptors
Baseline
Robot Navigation
Mobile Robot
Mobile robots
Illumination
Computational complexity
Simplicity
Computational Complexity
Navigation
Lighting
Robustness
Geometry
Demonstrate
Experiment
Experiments
Learning

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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