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Efficient Stereo Image Geometrical Reconstruction at Arbitrary Camera Settings from a Single Calibration

  • Songbai Ji
  • Xiaoyao Fan
  • David W. Roberts
  • Keith D. Paulsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Camera calibration is central to obtaining a quantitative image-to-physical-space mapping from stereo images acquired in the operating room (OR). A practical challenge for cameras mounted to the operating microscope is maintenance of image calibration as the surgeon’s field-of-view is repeatedly changed (in terms of zoom and focal settings) throughout a procedure. Here, we present an efficient method for sustaining a quantitative image-to-physical space relationship for arbitrary image acquisition settings (S) without the need for camera re-calibration. Essentially, we warp images acquired at S into the equivalent data acquired at a reference setting, S 0, using deformation fields obtained with optical flow by successively imaging a simple phantom. Closed-form expressions for the distortions were derived from which 3D surface reconstruction was performed based on the single calibration at S 0. The accuracy of the reconstructed surface was 1.05 mm and 0.59 mm along and perpendicular to the optical axis of the operating microscope on average, respectively, for six phantom image pairs, and was 1.26 mm and 0.71 mm for images acquired with a total of 47 arbitrary settings during three clinical cases. The technique is presented in the context of stereovision; however, it may also be applicable to other types of video image acquisitions (e.g., endoscope) because it does not rely on any a priori knowledge about the camera system itself, suggesting the method is likely of considerable significance.

Keywords

Optical Flow Reference Setting Arbitrary Setting Camera Calibration Deformation Field 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Songbai Ji
    • 1
    • 2
  • Xiaoyao Fan
    • 1
  • David W. Roberts
    • 2
    • 3
  • Keith D. Paulsen
    • 1
    • 2
    • 3
  1. 1.Thayer School of EngineeringDartmouth CollegeHanoverUSA
  2. 2.Geisel School of MedicineDartmouth CollegeHanoverUSA
  3. 3.Dartmouth Hitchcock Medical CenterLebanonUSA

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