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Analysing False Positives and 3D Structure to Create Intelligent Thresholding and Weighting Functions for SIFT Features

  • Michael May
  • Martin Turner
  • Tim Morris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

Abstract

This paper outlines image processes for object detection and feature match weighting utilising stereoscopic image pairs, the Scale Invariant Feature Transform (SIFT) [13,4] and 3D reconstruction. The process is called FEWER; Feature Extraction and Weighting for Enhanced Recognition. The object detection technique is based on noise subtraction utilising the false positive matches from random features. The feature weighting process utilises a 3D spatial information generated from the stereoscopic pairs and 3D feature clusters. The features are divided into three different types, matched from the target to the scene and weighted based on their 3D data and spatial cluster properties. The weightings are computed by analysing a large number of false positive matches and this gives an estimation of the probability that a feature is matched correctly. The techniques described provide increased accuracy, reduces the occurrence of false positives and can create a reduced set of highly relevant features.

Keywords

Target Image Scale Invariant Feature Transform Feature Match Scene Image Structure From Motion 
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 2011

Authors and Affiliations

  • Michael May
    • 1
  • Martin Turner
    • 1
  • Tim Morris
    • 1
  1. 1.The University of ManchesterUK

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