Template matching for large transformations

  • Authors:
  • Julian Eggert;Chen Zhang;Edgar Körner

  • Affiliations:
  • Honda Research Institute Europe GmbH, Offenbach, Main, Germany;Darmstadt University of Technology, Institute of Automatic Control, Control Theory and Robotics Lab, Darmstadt, Germany;Honda Research Institute Europe GmbH, Offenbach, Main, Germany

  • Venue:
  • ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
  • Year:
  • 2007
  • Tracking with depth-from-size

    ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I

Quantified Score

Hi-index 0.00

Visualization

Abstract

Finding a template image in another larger image is a problem that has applications in many vision research areas such as models for object detection and tracking. The main problem here is that under real-world conditions the searched image usually is a deformed version of the template, so that these deformations have to be taken into account by the matching procedure. A common way to do this is by minimizing the difference between the template and patches of the search image assuming that the template can undergo 2D affine transformations. A popular differential algorithm for achieving this has been proposed by Lucas and Kanade [1], with the disadvantage that it works only for small transformations. Here we investigate the transformation properties of a differential template matching approach by using resolution pyramids in combination with transformation pyramids, and show how we can do template matching under large-scale transformations, with simulation results indicating that the scale and rotation ranges can be doubled using a 3 stage pyramid.