When using traditional phase-shift profilometry for 3D measurement, it is necessary to keep the measured object static during the shooting process. When the measured object is moving, errors will occur if the projection and capture of the fringe image is not fast enough. This paper proposes a new method to reconstruct the moving object by double sampling. A trigger control device is applied to the camera and projector, which ensures that after each projection, two consecutive images are captured before the next projection. Then, the phase information is retrieved by analyzing the relationship between the motion and fringe patterns. Finally, the moving object is retrieved successfully. The proposed method increased the frame rate of the moving object reconstruction.
Recently, curvature filter (CF) has been developed to implicitly minimize curvature for image processing problems such as smoothing and denoising. In this paper, we propose a parallel curvature filter (PCF) that performs on GPU which is much faster than the original CF on CPU. Inspired by Convolution Neural Networks processed by GPU, the convolution operations in curvature filter computation can be similarly paralleled by GPU so that the PCF on a single GPU can process 33.2 Giga pixels per second. Such performance allows it to work in the real-time applications such as video processing and biomedical image processing, where high performance is required. Our experiments confirm the efficiency and effectiveness of the PCF.
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