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Article Dans Une Revue Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal Année : 2011

Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs

Résumé

To develop better image change detection algorithms, new models able to capture spatiotemporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semilocal interimage interactions and detecting local or regional changes in an image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections obtained from the neighboring pixels for different patch sizes. We study the statistical properties of the nonparametric detection approach that guarantees small probabilities of false alarms. Experimental results on several applications demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting occlusions and meaningful changes for a variety of illumination conditions and signal-to-noise ratios. The number of control parameters of the algorithm is small, and the adjustment is intuitive in most cases.

Dates et versions

hal-02643196 , version 1 (28-05-2020)

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Charles Kervrann, Jérôme Boulanger, Thierry Pecot, Patrick Perez, Jean Salamero. Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs. Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 2011, 9 (4), pp.1829 - 1865. ⟨10.1137/100791786⟩. ⟨hal-02643196⟩
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