Gradient using first order derivative of Gaussian

Output the gradient image of a grayscale image

Description

[gx,gy]=gaussgradient(IM,sigma) outputs the gradient image gx and gy of
image IM using a 2-D Gaussian kernel. Sigma is the standard deviation of
this kernel along both directions.

• Required Products: Image Processing Toolbox
• MATLAB release: MATLAB 7 (R14)

[H N]

Image processing toolbox is not required if you use conv2 instead of imfilt.

[Michal Kujalowicz]

Theare is a problem with fim=mat2gray(im); instruction,in "testgaussgradient.m" (matlab R14)I got "matrix im should be a double error".
It can be solved by adding im=double(im);
after reading image (3 line)

[renuka patnaik]

there was a problem using the function imshow(fim(1:50,1:50),'InitialMagnification','fit'); in 'fit'

[masha yusrina]

there was a problem using the function imshow(fim(1:50,1:50),'InitialMagnification','fit'); in 'fit'

i can't solve it

[Andrea Tagliasacchi]

Works perfectly it seems, Thanks

[Prakash Manandhar]

 

[Yaroslav Bulatov]

 

[hbu ]

good

[samur piere]

can you mention the mathematical basis for kernel size and normalization steps. thx.

epsilon = 1e-2;
halfsize = ceil( sigma * sqrt(-2*log(sqrt(2*pi)*sigma*epsilon)) );

hx = hx/sqrt(sum(sum( abs(hx).*abs(hx) )));

[Gustaf Kylberg]

Seems like you are calculating too small kernel sizes when higher values of sigma are used, e.g., when sigma>10 the kernels are clearly truncated. With sigma>40 your kernel size becomes imaginary.

[Karthik Muthuswamy]

Real nice work!!!

[Karthik Muthuswamy]

Can you add the paper/text you had referred to calculate the kernels in your code. Thanks!

[Ernesto]

thanks, it show clearly gx and gy

[Yad prab]

Hey, nice job man! I can easily visulaize the variations in my image.
But what if i m in 3D. With image in 2D and a time factor that is adds to the third dimenstion ?