Wide-Baseline Dense Feature Matching for Endoscopic Images

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.