Frédéric Champagnat

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We study dense optical flow estimation using iterative registration of local window, also known as iterative Lucas-Kanade (LK) [B. Lucas et al, 1981]. We show that the usual iterative-warping scheme encounters divergence problems and propose a modified scheme with better behavior. It yields good results with a much lower cost than the exact dense LK(More)
Iteratively Reweighted Least Squares (IRLS) and Residual Steepest descent (RSD) algorithms of robust statistics arise as special cases of half-quadratic schemes . Here, we adopt a statistical framework and we show that both algorithms are instances of the EM algorithm. The augmented dataset respectively involves a scale and a location mixture of Gaussians.(More)
This paper provides a complete characterization of stationary Markov random fields on a finite rectangular (nontoroidal) lattice in the basic case of a second-order neighborhood system. Equivalently, it characterizes stationary Markov fields on 2 whose restrictions to finite rectangular subsets are still Markovian (i.e., even on the boundaries). Until now,(More)
This paper deals with dense optical flow estimation from the perspective of the trade-off between quality of the estimated flow and computational cost which is required by real-world applications. We propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on GPU. It leads to very high performance even on large image(More)
Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher resolution images. We propose an original observation model devoted to the case of nonisometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main observation models used in(More)
We address the issue of distinguishing point objects from a cluttered background and estimating their position by image processing. We are interested in the specific context in which the object's signature varies significantly relative to its random subpixel location because of aliasing. The conventional matched filter neglects this phenomenon and causes a(More)
We address DSM reconstruction from calibrated limited-angle aerial side-looking image sequences. We use a regularised approach which combines a multi-view pixel-wise similarity criterion and a L1-norm regularisation term. Although it gives quite good results, it has two main drawbacks: occlusions are not dealt with and the reconstruction improvement brought(More)
We present a super-resolution (SR) color freeze frame of small moving objects in video sequences. In the last two decades, all SR methods except one focus on the case of rigid scene. We propose a fast and robust method that performs SR reconstruction on tracked objects. After an affine registration of the regions of interest of objects, a non-uniform(More)