A geological assessment method of floor water inrush risk and its application

Laser stripe center extraction is a key step for the profile measurement of line structured light sensors (LSLS). To accurately obtain the center coordinates at sub-pixel level, an improved gray-gravity method (IGGM) was proposed. Firstly, the center points of the stripe were computed using the gray-gravity method (GGM) for all columns of the image. By fitting these points using the moving least squares algorithm, the tangential vector, the normal vector and the radius of curvature can be robustly obtained. One rectangular region could be defined around each of the center points. Its two sides that are parallel to the tangential vector could alter their lengths according to the radius of the curvature. After that, the coordinate for each center point was recalculated within the rectangular region and in the direction of the normal vector. The center uncertainty was also analyzed based on the Monte Carlo method. The obtained experimental results indicate that the IGGM is suitable for both the smooth stripes and the ones with sharp corners. The high accuracy center points can be obtained at a relatively low computation cost. The measured results of the stairs and the screw surface further demonstrate the effectiveness of the method.

Sub-Pixel Extraction of Laser Stripe Center Using an Improved Gray-Gravity Method †

An adaptive moving total least squares method for curve fitting

Visual detection method based on line lasers for the detection of longitudinal tears in conveyor belts

The moving least squares (MLS) method has been developed for the fitting of measured data contaminated with random error. The local approximants of MLS method only take the error of dependent variable into account, whereas the independent variable of measured data always contains random error. Considering the errors of all variables, this paper presents an improved moving least squares (IMLS) method to generate curve and surface for the measured data. In IMLS method, total least squares (TLS) with a parameter based on singular value decomposition is introduced to the local approximants. A procedure is developed to determine the parameter . Numerical examples for curve and surface fitting are given to prove the performance of IMLS method.

/pdf/an-improved-moving-least-squares-method-for-curve-and-4hb7e4m31c.pdf

An Improved Moving Least Squares Method for Curve and Surface Fitting

Calculation of stratum surface principal curvature based on a moving least square method

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Calculation of stratum surface principal curvature based on a moving least square method