代码搜索：Gradient
找到约 2,951 项符合「Gradient」的源代码
代码结果 2,951
www.eeworm.com/read/251128/12363018
m p0309.m
[I,map]=imread('3-22.jpg');
imshow(I,map);
I=double(I);
[Gx,Gy]=gradient(I); % 计算梯度
G=sqrt(Gx.*Gx+Gy.*Gy); % 注意是矩阵点乘
J1=G;
figure,imshow(J1,map); % 第一种图像增强
J2=I;
www.eeworm.com/read/336217/12463432
m ex234.m
%********************************************************
%程序：EX234.M
%功能：工作空间直接做图法使用实例
%********************************************************
[x,y,z]=peaks(30); %定义图形peaks
contour(x,y,z
www.eeworm.com/read/227861/14408221
m exm044_3.m
%exm044_3.m
F=[1,2,3;4,5,6;7,8,9]
Dx=diff(F) %相邻行差分
Dx_2=diff(F,1,2) %相邻列差分。第三输入宗量2表示"列"差分。
[FX,FY]=gradient(F) %数据点步长默认为1
[FX_2,FY_2]=gradient(F,0.5) %数据点步长为0.5
www.eeworm.com/read/221376/14742360
dfm sinternalskins.dfm
object FormInternalSkins: TFormInternalSkins
Left = 183
Top = 116
AutoScroll = False
BorderIcons = []
Caption = 'Internal skins'
ClientHeight = 223
ClientWidth = 332
Color = cl
www.eeworm.com/read/220380/14802159
m annsfront.m
clear all;
p=[-1:0.05:1];
t0=sin(2*pi*p);
t=sin(2*pi*p)+0.1*randn(size(p));
>> val.P=[-0.975:0.05:0.975];
>> val.T=sin(2*pi*val.P)+0.1*randn(size(val.P));
>> net=newff([-1 1],[20 1],{'tansig','p
www.eeworm.com/read/114552/15048172
txt mch04-32.txt
叠加到轮廓图上的二维箭头图
n = -2.0:.22:2.0;
[X,Y,Z] = peaks(n);
[U,V] = gradient(Z,.2);
hold on
quiver(X,Y,U,V)
hold off
www.eeworm.com/read/167562/5455391
cpp bubble.cpp
/****************************************************************************
**
** Copyright (C) 2006-2006 Trolltech ASA. All rights reserved.
**
** This file is part of the example classes of the Qt
www.eeworm.com/read/167562/5457452
cpp qbrush.cpp
/****************************************************************************
**
** Copyright (C) 1992-2006 Trolltech ASA. All rights reserved.
**
** This file is part of the QtGui module of the Qt To
www.eeworm.com/read/473520/6845253
m liti20.m
[x,y,z]=peaks(20);
[dx,dy]=gradient(z,.5,.5);
contour(x,y,z,10)
hold on
quiver(x,y,dx,dy)
hold off
www.eeworm.com/read/392361/8348469
m exm044_3.m
%exm044_3.m
F=[1,2,3;4,5,6;7,8,9]
Dx=diff(F) %相邻行差分
Dx_2=diff(F,1,2) %相邻列差分。第三输入宗量2表示"列"差分。
[FX,FY]=gradient(F) %数据点步长默认为1
[FX_2,FY_2]=gradient(F,0.5) %数据点步长为0.5