Investigations of multigrid algorithms for the estimation of optical flow fieldsin image sequences
Computer Vision, Graphics, and Image Processing
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust incremental optical flow
Robust incremental optical flow
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Dense Estimation of Fluid Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
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In recent years, interest in motion analysis has increased with advances in processing capabilities. The usual input in a motion analysis system is an image sequence, with a corresponding increase in the amount of processed data. A typical motion problem is to analyze the motion within 2D image data corresponding to a sequence of frames, of a 3D scene. In computer vision a number of techniques are available to estimate the optical flow; the more efficient (in terms of quality) are modulated as the minimization of a global objective function. This cost function includes an observation constraint and a smoothness term. Such models generally assume that the luminance is constant along its trajectory. This assumption is not valid in cases of spatial and temporal distortions as in fluid image sequence. As an extension, a new model is described based on the continuity equation of fluid mechanics and a smoothness function considering the divergence (div) and vorticity (curl) of the motion field. The proposed model is embedded in a multiresolution framework and the minimization is conducted with an efficient multigrid technique. In this paper, the performance of the proposed motion estimation technique is analyzed and compared to similar "standard" methods, using simulated and real satellite data (the last provided by EUMETSAT). Finally, measures as RMSE of the images, number of cuts and number of iterations for the minimization of the energy function are introduced to justify the improvement of the estimation technique.