Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

A. Barclay, H. Kaufmann:
"FT-RANSAC: Towards robust multi-modal homography estimation";
Vortrag: 8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Stockholm; 24.08.2014; in: "8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), 2014", IEEE, (2014), S. 1 - 4.

Kurzfassung englisch:
As the golden standard in robust estimation, the
classic RANSAC approach has undergone extensive research that
contributed to further enhancements in run-time performance,
robustness, and multi-structure support to name a few. Yet, the
accelerating growth of multi-modal co-registered datasets
requires a new adaptation of the RANSAC algorithm. In this
paper, we propose a multi-modal fault-tolerant extension to
RANSAC, termed FT-RANSAC, with a model-independent
tolerance to degenerate configurations. Besides building on stateof-
the-art RANSAC variants, such as PROSAC, our approach
introduces a Hough inspired dimensionality reduction and
consistency voting processes, to enable robust estimation in the
presence of non-homogenous multi-modal correspondence sets.
Through experimental evaluation using homography estimation of
RGB-D data, we demonstrate that our approach outperforms the
classic single-modality RANSAC in robustness and tolerance to
degenerate configurations. Finally, the proposed approach lends
itself to parallel multi-core implementations, and could be adapted
to specialized RANSAC extensions found in the literature.

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