Abstract
To reduce the matching ambiguities and explore the matching potential of repetitive features from the scenes with repetitive elements, we present a robust framework of Delaunay triangulation matching based on feature saliency analysis. The feature saliency is computed based on the Gaussian invariance model of the selected reliable features and used as guidance for the triangle-constraint region matching. Due to the seed-sensitive nature of the triangulation matching, the framework integrates the effectiveness validation steps to predict whether the extracted features and the found seeds are proper for matching the specific scenes. By suppressing the surround feature influence, we provide an opportunity for the repetitive features to enhance their feature saliency in the local regions and increase the discriminative power to identify their correspondences. We benchmark the matching performance on the tradeoff between the mean precision and recall. The promising results manifest its effectiveness on the ambiguity reduction.
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