We present a novel approach to estimate and analyze 3D fluid structure and motion of clouds from multi-spectrum 2D cloud image sequences. Accurate cloud-top structure and motion are very important for a host of meteor...
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We present a novel approach to estimate and analyze 3D fluid structure and motion of clouds from multi-spectrum 2D cloud image sequences. Accurate cloud-top structure and motion are very important for a host of meteorological and climate applications. However, due to the extremely complex nature of cloud fluid motion, classical nonrigid motion analysis methods are insufficient for solving this particular problem. In this paper, two spectra of satellite cloud images are utilized. The high-resolution visible channel is first used to perform cloud tracking by using a recursive algorithm which integrates local motion analysis with a set of global fluid constraints, defined according to the physical fluid dynamics. Then, the infrared channel (thermodynamic information) is incorporated to post-process the cloud tracking results in order to capture the cloud density variations and small details of cloud fluidity. Experimental results on GOES (Geostationary Operational Environmental Satellite) cloud image sequences are presented in order to validate and evaluate both the effectiveness and robustness of our algorithm.
We propose a novel method for continuous 3D depth recovery and tracking using calibrated stereo. The method integrates stereo correspondence, surface reconstruction and tracking by using a new single deformable dual m...
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We propose a novel method for continuous 3D depth recovery and tracking using calibrated stereo. The method integrates stereo correspondence, surface reconstruction and tracking by using a new single deformable dual mesh optimization, resulting in simplicity, robustness and efficiency. In order to combine stereo correspondence and structure recovery, the method introduces an external energy function defined for a 3D volume based on cross-correlation between the stereo pairs. The internal energy functional of the deformable dual mesh imposes smoothness on the surfaces and it serves as a communication tool between the two meshes. Under the forces produced by the energy terms, the dual mesh deforms to recover and track the 3D surface. The newly introduced dual-mesh model, which is one of the main contributions of this paper, makes the system robust against local minima and yet it is efficient. A coarse-to-fine minimization approach makes the system even more efficient. Tracking is achieved by using the recovered surface as an initial position for the next time frame. Although the system can effectively utilize initial surface positions and disparity data, they are not needed for a successful operation, which makes this system applicable to a wide range of areas. We present the results of a number of experiments on stereo human face and cloud images, which proves that our new method is very effective.
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