Wide-Baseline Dense Feature Matching for Endoscopic Images

  • Gustavo A. Puerto-Souza
  • Gian-Luca Mariottini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

Providing a feature-matching strategy to accurately recover tracked features after a fast and large endoscopic-camera motion or a strong organ deformation, is key in many endoscopic-imaging applications, such as augmented reality or soft-tissue shape recovery. Despite recent advances, existing feature-matching algorithms are characterized by limiting assumptions, and have not yet met the necessary levels of accuracy, especially when used to recover features in distorted or poorly-textured tissue areas. In this paper, we present a novel feature-matching algorithm that accurately recovers the position of image features over the entire organ’s surface. Our method is fully automatic, it does not require any explicit assumption about the organ’s 3-D surface, and leverages Gaussian Process Regression to incorporate noisy matches in a probabilistically sound way. We have conducted extensive tests with a large database of more than 100 endoscopic-image pairs, which show the improved accuracy and robustness of our approach when compared to current state-of-the-art methods.

Keywords

Query Image Correct Match Laparoscopic Partial Nephrectomy Reprojection Error Coherent Point Drift 
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 2014

Authors and Affiliations

  • Gustavo A. Puerto-Souza
    • 1
  • Gian-Luca Mariottini
    • 1
  1. 1.Dept. of Computer Science and EngineeringUniv. of Texas at ArlingtonTexasUSA

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