转载:Cubic interpolation
https://www.paulinternet.nl/?page=bicubic
Cubic interpolation
If the values of a function f(x) and its derivative are known at x=0 and x=1, then the function can be interpolated on the interval [0,1] using a third degree polynomial. This is called cubic interpolation. The formula of this polynomial can be easily derived.
A third degree polynomial and its derivative:


The values of the polynomial and its derivative at x=0 and x=1:




The four equations above can be rewritten to this:




And there we have our cubic interpolation formula.
Interpolation is often used to interpolate between a list of values. In that case we don't know the derivative of the function. We could simply use derivative 0 at every point, but we obtain smoother curves when we use the slope of a line between the previous and the next point as the derivative at a point. In that case the resulting polynomial is called a Catmull-Rom spline. Suppose you have the values p0, p1, p2and p3 at respectively x=-1, x=0, x=1, and x=2. Then we can assign the values of f(0), f(1), f'(0) and f'(1) using the formulas below to interpolate between p1 and p2.




Combining the last four formulas and the preceding four, we get:




So our cubic interpolation formula becomes:

For example:

For the green curve:





The first and the last interval
We used the two points left of the interval and the two points right of the inverval as inputs for the interpolation function. But what if we want to interpolate between the first two or last two elements of a list? Then we have no p0 or no p3. The solution is to imagine an extra point at each end of the list. In other words, we have to make up a value for p0 and p3 when interpolating the leftmost and rightmost interval respectively. Two ways to do this are:
- Repeat the first and the last point.
Left: p0 = p1
Right: p3 = p2 - Let the end point be in the middle of a line between the imaginary point and the point next to the end point.
Left: p0 = 2p1 - p2
Right: p3 = 2p2 - p1
转载:Cubic interpolation的更多相关文章
- 【转载】interpolation(插值)和 extrapolation(外推)的区别
根据已有数据以及模型(函数)预测未知区域的函数值,预测的点在已有数据范围内就是interpolation(插值), 范围外就是extrapolation(外推). The Difference Bet ...
- Interpolation in MATLAB
Mathematics One-Dimensional Interpolation There are two kinds of one-dimensional interpolation i ...
- MATLAB曲面插值及交叉验证
在离散数据的基础上补插连续函数,使得这条连续曲线通过全部给定的离散数据点.插值是离散函数逼近的重要方法,利用它可通过函数在有限个点处的取值状况,估算出函数在其他点处的近似值.曲面插值是对三维数据进行离 ...
- OpenCV基于傅里叶变换进行文本的旋转校正
傅里叶变换可以用于将图像从时域转换到频域,对于分行的文本,其频率谱上一定会有一定的特征,当图像旋转时,其频谱也会同步旋转,因此找出这个特征的倾角,就可以将图像旋转校正回去. 先来对原始图像进行一下傅里 ...
- 通过python将图片生成字符画
基础知识: 1.python基础知识 快速学习链接:https://www.shiyanlou.com/courses/214 2.linux命令行操作 快速学习链接:https://www. ...
- Deep Learning 16:用自编码器对数据进行降维_读论文“Reducing the Dimensionality of Data with Neural Networks”的笔记
前言 论文“Reducing the Dimensionality of Data with Neural Networks”是深度学习鼻祖hinton于2006年发表于<SCIENCE > ...
- Line Search and Quasi-Newton Methods 线性搜索与拟牛顿法
Gradient Descent 机器学习中很多模型的参数估计都要用到优化算法,梯度下降是其中最简单也用得最多的优化算法之一.梯度下降(Gradient Descent)[3]也被称之为最快梯度(St ...
- Line Search and Quasi-Newton Methods
Gradient Descent 机器学习中很多模型的参数估计都要用到优化算法,梯度下降是其中最简单也用得最多的优化算法之一.梯度下降(Gradient Descent)[3]也被称之为最快梯度(St ...
- 非刚性图像配准 matlab简单示例 demons算法
2011-05-25 17:21 非刚性图像配准 matlab简单示例 demons算法, % Clean clc; clear all; close all; % Compile the mex f ...
随机推荐
- MySql优化之主从复制
第一步: 配置节点信息(配置完毕重启mysql) 找到my.cnf配置文件,这个文件在etc目录下使用命令修改my.cnf文件 vi /etc/my.cnf 主节点配置: server-id =55 ...
- python文件操作汇总day7
Python处理文件 文件操作分为读.写.修改,我们先从读开始学习 读文件 示例1: f = open(file='D:/工作日常/兼职白领学生空姐模特护士联系方式.txt',mode='r',enc ...
- MATLAB实例:二元高斯分布图
MATLAB实例:二元高斯分布图 作者:凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/ 1. MATLAB程序 %% demo Multivariate No ...
- sql 根据查询的记录生成序号的几种方式
row_number() order() 函数会为查询出来的每一行记录生成一个序号,依次排序且不会重复,注意使用row_number函数时必须要用over子句选择对某一列进行排序才能生成序号. ra ...
- Uva1639(概率期望/对数处理避免丢失精度)
Uva1639 题意: 有两个盒子各有n个糖果(n<=200000),每天随机选择一个:选第一个盒子的概率是p(0 ≤ p ≤ 1),第二个盒子的概率为1-p,然后吃掉其中的一颗.直到有一天,随 ...
- c++多线程编程互斥锁初步
上一次讲述了多线程编程,但是由于线程是共享内存空间和资源的,这就导致:在使用多线程的时候,对于共享资源的控制要做的很好.先上程序: #include <iostream> #include ...
- JS表单验证源码(带错误提示及密码等级)
先晒图 index.html <!DOCTYPE html> <html lang="en"> <head> <meta charset= ...
- nginx配置location与rewrite规则教程
location 教程 location 教程 示例: location = / { # 精确匹配 / ,主机名后面不能带任何字符串 [ configuration A ] }location / { ...
- idea 代码没有被svn控制
背景 开发从svn上拉下来的代码,上传时发现idea的快捷键(ctrl+T)没反应以及菜单栏中没有相关按钮. 原因 发现项目当前文件夹里没有 .svn 隐藏文件夹,所以当前文件夹就没有被idea识别继 ...
- PAT (Basic Level) Practice (中文)1023 组个最小数 (20 分) (排序)
给定数字 0-9 各若干个.你可以以任意顺序排列这些数字,但必须全部使用.目标是使得最后得到的数尽可能小(注意 0 不能做首位).例如:给定两个 0,两个 1,三个 5,一个 8,我们得到的最小的数就 ...