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Automatic Selection and Detection of Visual Landmarks Using Multiple Segmentations

  • Daniel Langdon
  • Alvaro Soto
  • Domingo Mery
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)

Abstract

Detection of visual landmarks is an important problem in the development of automated, vision-based agents working on unstructured environments. In this paper, we present an unsupervised approach to select and to detect landmarks in images coming from a video stream. Our approach integrates three main visual mechanisms: attention, area segmentation, and landmark characterization. In particular, we demonstrate that an incorrect segmentation of a landmark produces severe problems in the next steps of the analysis, and that by using multiple segmentation algorithms we can greatly increase the robustness of the system. We test our approach with encouraging results in two image sets taken in real world scenarios. We obtained a significant 52% increase in recognition when using the multiple segmentation approach with respect to using single segmentation algorithms.

Keywords

Segmentation Algorithm Scale Invariant Feature Transform Salient Region Visual Landmark Landmark Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel Langdon
    • 1
  • Alvaro Soto
    • 1
  • Domingo Mery
    • 1
  1. 1.Pontificia Universidad Catolica de ChileSantiago 22Chile

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