Structural light measurement as a non-contact measurement method is commonly used in 3D shape detection, which can quickly acquire large-scale points cloud data of 3D surface with high precision. In the development of triangulation structural light sensors, the extraction of the light stripe centerline is the most important research point. Aiming at the problems of large error, high computational complexity and low data processing efficiency in the traditional maximum value based centerline extraction methods, a novel centerline extraction method based on actual light intensity distribution is proposed. Compared with the center line extraction method based on normal direction of light stripe, the discussed method is more suitable to describe the spatial characteristics of light stripe energy structure. It can greatly reduce the amount of calculation, improve processing speed and accuracy. The effectiveness of the proposed method is verified by a practical case of structural light sensor development.
Since the ideal image is difficult to be stripped from the actual sampling, this study aims to address and test a shared noise which as the knowledge and is included in the signal applying to the sampled projection to generate high qualified X-ray imaging by reducing the artifacts in computed tomography (CT). Combined with the randomness of the noise, the prereconstruction of the original projection is performed first, and then the forward projection which contains the equivalent noise in the image is obtained. Based on the slice updating, the forward and reconstruction processing is employed again. By means of threshold setting, multiple forward projections are accumulated, whereas the noise upon them will be reduced by averaging process. The noise is suppressed, and the expected information emerges, simultaneously. Study results show effective results, and the proposed method is practical and attractive as a preferred solution to CT artifacts suppression. It provides reliable guarantee for the CT inspection of internal and external dimensions.
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