A Data-Driven Approach to Feature Space Selection for Robust Micro-Endoscopic Image Reconstruction
Résumé
In the article we propose a new, on-line feature space selection strategy for displacement field estimation in the context of multi-view reconstruction of biological images acquired by a multi-photon micro-endoscope. While the high variety of targets encountered in clinical endoscopy induce enough texture feature variability to prohibit the use of recent supervised learning or feature matching-based visual tracking methods, we will show how on-line learning combined with a classical method such as Digital Image Correlation (DIC) can contribute to the improvement of convex optimization-based template matching techniques.
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