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2024年4月16日发(作者：vlookup函数的具体实例)

opencv中mls曲线拟合

English Answer:

MLS Curve Fitting in OpenCV.

Multi-level B-splines (MLS) is a powerful technique for

curve fitting that has gained popularity in OpenCV due to

its versatility and efficiency. MLS uses a weighted sum of

B-spline basis functions to approximate a curve, allowing

for smooth and accurate curve fitting even with complex

data.

Implementation in OpenCV.

OpenCV provides a comprehensive set of functions for

MLS curve fitting. The primary function for fitting an MLS

curve is `cv::ml::EMLS`. This function takes as input a set

of data points and a set of parameters specifying the B-

spline basis functions. It returns a fitted MLS curve that

can be evaluated at any point within the specified domain.

Parameters for MLS Curve Fitting.

The parameters for MLS curve fitting include:

Number of levels: The number of levels in the B-spline

basis functions.

Knots: The knots that define the intervals of

interpolation.

Weights: The weights of the basis functions at each

data point.

Regularization: A parameter that controls the

smoothness of the fitted curve.

Applications of MLS Curve Fitting.

MLS curve fitting finds applications in various

computer vision and image processing tasks, including:


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