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Description: 自己做课题时编写的一个可以拟合45个数据点的曲面拟合程序-own topics can be prepared in a fit 45 data points the surface fitting procedure
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Size: 879054 |
Author: 小刚 |
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Description: ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model - matrix with model points,
data - matrix with data points,
OUTPUT:
R - rotation matrix and
T - translation vector accordingly
so
newdata = R*data + T .
newdata are transformed data points to fit model
see help icp for more information
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Size: 5385 |
Author: 阳关 |
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Description: ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly
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Size: 5524 |
Author: zxphistory@163.com |
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Description: 一个对点列进行线性拟合的例子-Points out a linear fitting example
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Size: 9216 |
Author: 站长 |
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Description: 自己做课题时编写的一个可以拟合45个数据点的曲面拟合程序-own topics can be prepared in a fit 45 data points the surface fitting procedure
Platform: |
Size: 4862976 |
Author: 小刚 |
Hits:
Description: ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model - matrix with model points,
data - matrix with data points,
OUTPUT:
R - rotation matrix and
T - translation vector accordingly
so
newdata = R*data + T .
newdata are transformed data points to fit model
see help icp for more information
-ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model- matrix with model points,
data- matrix with data points,
OUTPUT:
R- rotation matrix and
T- translation vector accordingly
so
newdata = R*data+ T .
newdata are transformed data points to fit model
see help icp for more information
Platform: |
Size: 5120 |
Author: 阳关 |
Hits:
Description: 最小二乘拟合算法C程序，N为拟合点数，T为拟合阶数。-Least-squares fitting algorithm C procedures, N for the fitting points, T for the fitting order.
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Size: 1024 |
Author: 唐荣 |
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Description: fit_ellipse - finds the best fit to an ellipse for the given set of points. a matalb realization-fit_ellipse- finds the best fit to an ellipse for the given set of points. a matalb realization
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Size: 3072 |
Author: Deng Fu Qin |
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Description: 对于空间两个点云，利用经典的icp算法进行匹配-ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
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Size: 5120 |
Author: wangwei |
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Description: Two matlab files to demonstrate how to fit an ellipsoid directly to a set of 3D points. -Two matlab files to demonstrate how to fit an ellipsoid directly to a set of 3D points.
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Size: 1024 |
Author: guigui |
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Description: This is a fast non-iterative ellipse fit, and among fast non-iterative ellipse fits this is the most accurate and robust.
It takes the xy-coordinates of data points, and returns the coefficients of the equation of the ellipse:
ax^2 + bxy + cy^2 + dx + ey + f = 0,
i.e. it returns the vector A=(a,b,c,d,e,f). To convert this vector to the geometric parameters -This is a fast non-iterative ellipse fit, and among fast non-iterative ellipse fits this is the most accurate and robust.
It takes the xy-coordinates of data points, and returns the coefficients of the equation of the ellipse:
ax^2+ bxy+ cy^2+ dx+ ey+ f = 0,
i.e. it returns the vector A=(a,b,c,d,e,f). To convert this vector to the geometric parameters
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Size: 2048 |
Author: ragada |
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Description: fit_ellipse - finds the best fit to an ellipse for the given set of points.-fit_ellipse- finds the best fit to an ellipse for the given set of points.
Platform: |
Size: 5120 |
Author: yar |
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Description: Least squares fit of a line to a set of points
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Size: 1024 |
Author: hazem tarek |
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Description: 通过一系列凸显预处理，实现人脸边缘检测。然后提取出眼、鼻、口三处特征点、并标记出来。-Highlighted by a series of pre realize the face edge detection. Then extract the eyes, nose, mouth three feature points, and marked.
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Size: 11420672 |
Author: ql |
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Description: Input: XY(n,2) is the array of coordinates of n points x(i)=XY(i,1), y(i)=XY(i,2)
Output: Par = [a b R] is the fitting circle:
center (a,b) and radius R
Note: this fit does not use built-in matrix functions (except "mean"),
so it can be easily programmed in any programming language-Input: XY (n, 2) is the array of coordinates of n points x (i) = XY (i, 1), y (i) = XY (i, 2) Output: Par = [ab R] is the fitting circle: center (a, b) and radius R Note: this fit does not use built-in matrix functions (except " mean" ), so it can be easily programmed in any programming language
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Size: 1024 |
Author: 田雨 |
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Description: 最小二乘是由方程式近似的数据点的一组的方式，让你预测中间值，或计算的数据的一些措施。您可能已经通过绘制通过数目的数据点图上绘制的直线近似的趋势线。最小二乘这不只是“装修”，但在数学的方式，最大限度地减少错误，从而导致。我的单元延伸最小二乘从仅仅是一种直线拟合到高阶多项式为更复杂的数据（其中可能有一平方律拟合的物理原因，例如），或更好的匹配的数据。-Least-squares is a way of approximating a set of data points by an equation, allowing you to predict intermediate values or calculate some measure of the data. You may have approximated a trend-line by drawing a straight line through a number of data points plotted on a graph. Least-squares does just this "fitting" but in a mathematical way that minimises the errors that result. My unit extends least-squares from just a straight-line fit into higher order polynomials for more complex data (where there may be a physical reason for a square-law fit, for example) or for a better match to the data.
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Size: 222208 |
Author: liu |
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Description: 四点拟合一个平面，并且判断其他点到面的距离以及方向-Using four points to fit a plane, and tell you the distance and direction from other points to the plane.
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Size: 7373824 |
Author: liujie |
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Description: 对于空间两个点云，利用经典的icp算法进行匹配-ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
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Size: 5120 |
Author: orecast |
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Description: best fit line with given data points
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Size: 2048 |
Author: rupam |
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Description: The following Matlab project contains the source code and Matlab examples used for fitting an ellipse to a given set of points. The most accurate and robust fit minimizes geometric (orthogonal) distances the observed points to the fitting curve.-The following Matlab project contains the source code and Matlab examples used for fitting an ellipse to a given set of points. The most accurate and robust fit minimizes geometric (orthogonal) distances the observed points to the fitting curve.
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Size: 2048 |
Author: and111 |
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