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

Abstract : To develop better image change detection algorithms, new models able to capture spatio-temporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semi-local inter-image 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 non-parametric 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.
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Charles Kervrann, Jérôme Boulanger, Thierry Pecot, Patrick Pérez, Jean Salamero. Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs. 2011. ⟨inria-00487051v2⟩

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