Hierarchical Multi-Affine (HMA) algorithm for fast and accurate feature matching in minimally-invasive surgical images

The ability to find similar features between two distinct views of the same scene (feature matching) is essential in robotics and computer vision. We are here interested in the robotic-assisted minimally-invasive surgical scenario, for which feature matching can be used to recover tracked features after prolonged occlusions, strong illumination changes, image clutter, or fast camera motion. In this paper we introduce the Hierarchical Multi-Affine (HMA) feature-matching algorithm, which improves over the existing methods by recovering a larger number of image correspondences, at an increased speed and with a higher accuracy and robustness. Extensive experimental results are presented that compare HMA against existing methods, over a large surgical-image dataset and over several types of detected features.