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In this paper, we consider the problem of estimating the spatiotemporal alignment between N unsynchronized video sequences of the same dynamic 3D scene, captured from distinct viewpoints. Unlike most existing methods, which work for N = 2 and rely on a computationally intensive search in the space of temporal alignments, we present a novel approach that reduces the problem for general N to the robust estimation of a single line in IR(N). This line captures all temporal relations between the sequences and can be computed without any prior knowledge of these relations. Considering that the spatial alignment is captured by the parameters of fundamental matrices, an iterative algorithm is used to refine simultaneously the parameters representing the temporal and spatial relations between the sequences. Experimental results with real-world and synthetic sequences show that our method can accurately align the videos even when they have large misalignments (e.g., hundreds of frames), when the problem is seemingly ambiguous (e.g., scenes with roughly periodic motion), and when accurate manual alignment is difficult (e.g., due to slow-moving objects).

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This paper introduces a practical approach to register large-scale GIS imagery to a database of road vectors automatically. The proposed approach breaks the global alignment problem into a set of localized domains (tiles). Within each tile, the displacement between imagery and vectors is approximated by a translation. Finally, a global thin-plate-spline warp based on these local approximations is applied to register the imagery to the vector data. The critical step in this approach is a fully automatic algorithm to compute the best imagery-to-vectors translation within a tile. The proposed algorithm performs vector-guided extraction of road features, aggregates features obtained in the neighborhood of multiple vectors, and then estimates the best translation through a leastsquares optimization applied to a selected subset of the aggregated features. It also computes a confidence value for each processed image tile, so that a human operator can easily find out the places where the automatic approach has encountered difficulties, if necessary. The algorithm has been tested on hundreds of production satellite images of different countries. It has correctly registered over 80 percent of the imagery, and consistently reported low confidence values for the rest.