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Implicit Camera Calibration by Using Resilient Neural Networks

  • Pınar Çivicioğlu
  • Erkan Beşdok
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

The accuracy of 3D measurements of objects is highly affected by the errors originated from camera calibration. Therefore, camera calibration has been one of the most challenging research fields in the computer vision and photogrammetry recently. In this paper, an Artificial Neural Network Based Camera Calibration Method, NBM, is proposed. The NBM is especially useful for back-projection in the applications that do not require internal and external camera calibration parameters in addition to the expert knowledge. The NBM offers solutions to various camera calibration problems such as calibrating cameras with automated active lenses that are often encountered in computer vision applications. The difference of the NBM from the other artificial neural network based back-projection algorithms used in intelligent photogrammetry (photogrammetron) is its ability to support the multiple view geometry. In this paper, a comparison of the proposed method has been made with the Bundle Block Adjustment based back-projection algorithm, BBA. The performance of accuracy and validity of the NBM have been tested and verified over real images by extensive simulations.

Keywords

Camera Calibration Computer Vision Application Multiple View Geometry Camera Calibration Parameter Camera Calibration Method 
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

  • Pınar Çivicioğlu
    • 1
  • Erkan Beşdok
    • 2
  1. 1.Civil Aviation School, Avionics Dept.Erciyes UniversityKayseriTurkey
  2. 2.Engineering Faculty, Photogrammetry DevisionErciyes UniversityKayseriTurkey

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