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Pattern Recognition and Image Analysis

, Volume 21, Issue 2, pp 238–241 | Cite as

Dense statistic versus sparse feature-based approach for 3D object recognition

  • P. DeckerEmail author
  • S. Thierfelder
  • D. Paulus
  • M. Grzegorzek
Representation, Processing, Analysis and Understanding of Images

Abstract

In this article we introduce and compare two approaches towards automatic classification of 3D objects in 2D images. The first one is based on statistical modeling of wavelet features. It estimates probability density functions for all possible object classes considered in a particular recognition task. The second one uses sparse local features. For training, SURF features are extracted from the training images. During the recognition phase, features from the image are matched geometrically, providing the best fitting object for the query image. Experiments were performed for different training sets using more than 40 000 images with different backgrounds. Results show very good classification rates for both systems and point out special characteristics for each approach, which make them more suitable for different applications.

Keywords

Feature Vector Training Image Object Class Object Area Hough Space 
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

© Pleiades Publishing, Ltd. 2011

Authors and Affiliations

  • P. Decker
    • 1
    Email author
  • S. Thierfelder
    • 1
  • D. Paulus
    • 1
  • M. Grzegorzek
    • 2
  1. 1.Research Group for Active VisionUniversity Koblenz-LandauKoblenzGermany
  2. 2.Research Group for Pattern RecognitionUniversity of SiegenSiegenGermany

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