KEYWORDS: 3D modeling, Video, 3D image processing, Data modeling, Optimization (mathematics), 3D vision, Optical flow, Visual process modeling, Magnetorheological finishing, Lithium
Mutiview plus associated depth information form a typical 3D video representation. Depth map of each frame in a video sequence is usually estimated by stereo matching approaches separately. As a result it has weak temporal consistency. In this paper, we propose a novel framework based on spatio-temporal Markov Random Fields. It enforces temporal correlation by employing additional state in the graphical model. Improved belief propagation-sequential algorithm is exploited as an efficient optimization scheme to minimize the energy function. The experimental results demonstrate that the proposed method produces dependable depth maps in both spatial and temporal domain.
To detect the wavefront of a long-focus lens, a new method is proposed in this paper. It is based on two dimensional
sub-aperture scanning and model method reconstruction. The number of stripes moved from one sub-aperture to another
has a relationship with the wavefront slope. In most cases it can be approached to linear relation. Through scanning the
initial wavefront slope data are achieved. Then with the slope data, the wavefront is reconstructed by model method with
Zernike polynomials. Singular value decomposition method is used in the process of solving the matrix equation.
Stripe-counting is one of the most important contents, which plays a decisive role in getting an accurate experimental
result. The method's precision is validated after comparing it with laser interferometer. It also works in some situations
that interferometer will not be suitable to detect a long-focus and large-diameter lens.
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