Maximum recursion limit of 500 reached. Use set(0,'Rec​ursionLimi​t',N) to change the limit. Be aware that exceeding your available stack space can crash MATLAB and/or your computer.

it's fine working on another laptop but in my laptop will getting above error.........? can any one solve this....?

clear all;
close all;
a=imread('indus.jpg');
b=rgb2gray(a);
[m,n]=size(b);

r=im2double(b);
% r=im2bw(b,0.75);
% ^r=b;
out=r;
for i=2:m-1
    for j=2:n-1
   h=[1*r(i-1,j-1) 1*r(i-1,j) 1*r(i-1,j+1);...
      1*r(i,j-1) 1*r(i,j) 1*r(i,j+1);...
     1*r(i+1,j-1) 1*r(i+1,j) 1*r(i+1,j+1)];
 out(i,j)=(median(median(h)));
    end
end
r=out;
sobel=r;
for i=2:m-1
    for j=2:n-1
   h=[-1*r(i-1,j-1) 0*r(i-1,j) 1*r(i-1,j+1);...
      -2*r(i,j-1) 0*r(i,j) 2*r(i,j+1);...
     -1*r(i+1,j-1) 0*r(i+1,j) 1*r(i+1,j+1)];
 sobel(i,j)=(mean(mean(h)));
    end
end
sobel1=sobel;
for i=2:m-1
    for j=2:n-1
   h=[-1*sobel(i-1,j-1) -2*sobel(i-1,j) -1*sobel(i-1,j+1);...
      0*sobel(i,j-1) 0*sobel(i,j) 0*sobel(i,j+1);...
     1*sobel(i+1,j-1) 2*sobel(i+1,j) 1*sobel(i+1,j+1)];
 sobel1(i,j)=(mean(mean(h)));
    end
end
% a=min(r,sobel1);
% figure,imshow(a);

res=max(sobel1,r);
figure,imshow(res);

subplot(221);imshow(r,[]);
subplot(222);imshow(res,[]);
subplot(223);imshow(sobel,[]);
subplot(224);imshow(sobel1,[]);

Answer 1

[Image Analyst]

First, read this link to fix the formatting.

If you picked a name of your script that is the same as some function in your script, it will be called recursively, just as your partial error message states.

It's likely you called your script mean.m, sobel.m, or median.m.

I'm not sure because you didn't attach it, nor did you include the complete error message ( ALL the red text including the parts that identify the line of code throwing the error), nor did you tell us the name of your script.

[DB]

im posted all the error message in my question ..?

[Image Analyst]

DB, First, you didn't read the link I gave, did you? Because your code is still messed up.

Second, I saved your code as test3.m and ran your code and it finished with no errors. This is because I have a unique name. Did you read the part where I said the name is likely a problem? You didn't tell me the name of your script, so I think you might have overlooked that part.

Third, you are NOT including the error message. You're copying just a small snippet out of it. See Jan's answer. The full error message will also have things like the line number(s) and the lines of source code, none of which you posted. You need to include ALL the red text including the parts that identify the line of code throwing the error, and the name of your script, like I originally asked for.

[Jan]

@DB talari: Image Analyst's suggestions are valuable. Please consider them, because this will help readers to answer your questions efficiently in the future.

[Jan]

I've unaccepted my answer and accepted this one.

Answer 2

[Jan]

Start with omitting the brute clearing header: "clear all; close all". This wastes time and is not useful in productive code.

An improtant detail is missing in the error message: Which line causes the error? This line will contain the name of the function, which is called recursively. I assume this will be the name of the script file you have posted.

[SUSHMA MB]

Maximum recursion limit of 500 reached. Use set(0,'RecursionLimit',N) to change the limit. Be aware that exceeding your available stack space can crash MATLAB and/or your computer.

Error in reducem>dpalg

How to resolve this error?

[Walter Roberson]

SUSHMA MB: We need to see your code, and we need to know the file name you are using for your code.

Answer 3

[Jay shankar Nanda]

function [x,P]=ekf(fstate,x,P,hmeas,z,Q,R)
% EKF   Extended Kalman Filter for nonlinear dynamic systems
% [x, P] = ekf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P 
% for nonlinear dynamic system:
%           x_k+1 = f(x_k) + w_k
%           z_k   = h(x_k) + v_k
% where w ~ N(0,Q) meaning w is gaussian noise with covariance Q
%       v ~ N(0,R) meaning v is gaussian noise with covariance R
% Inputs:   f: function handle for f(x)
%           x: "a priori" state estimate
%           P: "a priori" estimated state covariance
%           h: fanction handle for h(x)
%           z: current measurement
%           Q: process noise covariance 
%           R: measurement noise covariance
% Output:   x: "a posteriori" state estimate
%           P: "a posteriori" state covariance
%
% Example:

n=3;      %number of state
q=0.1;    %std of process 
r=0.1;    %std of measurement
Q=q^2*eye(n); % covariance of process
R=r^2;        % covariance of measurement  
f=@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))];  % nonlinear state equations
h=@(x)x(1);                               % measurement equation
s=[0;0;1];                                % initial state
x=s+q*randn(3,1); %initial state          % initial state with noise
P = eye(n);                               % initial state covraiance
N=20;                                     % total dynamic steps
xV = zeros(n,N);          %estmate        % allocate memory
sV = zeros(n,N);          %actual
zV = zeros(1,N);
for k=1:N
  z = h(s) + r*randn;                     % measurments
  sV(:,k)= s;                             % save actual state
  zV(k)  = z;                             % save measurment
  [x, P] = ekf(f,x,P,h,z,Q,R);            % ekf 
  xV(:,k) = x;                            % save estimate
  s = f(s) + q*randn(3,1);                % update process 
end
for k=1:3                                 % plot results
  subplot(3,1,k)
  plot(1:N, sV(k,:), '-', 1:N, xV(k,:), '--')
end
%}
% By Yi Cao at Cranfield University, 02/01/2008
%

[x1,A]=jaccsd(fstate,x);    %nonlinear update and linearization at current state
P=A*P*A'+Q;                 %partial update
[z1,H]=jaccsd(hmeas,x1);    %nonlinear measurement and linearization
P12=P*H';                   %cross covariance
% K=P12*inv(H*P12+R);       %Kalman filter gain
% x=x1+K*(z-z1);            %state estimate
% P=P-K*P12';               %state covariance matrix
R=chol(H*P12+R);            %Cholesky factorization
U=P12/R;                    %K=U/R'; Faster because of back substitution
x=x1+U*(R'\(z-z1));         %Back substitution to get state update
P=P-U*U';                   %Covariance update, U*U'=P12/R/R'*P12'=K*P12.

function [z,A]=jaccsd(fun,x)
% JACCSD Jacobian through complex step differentiation
% [z J] = jaccsd(f,x)
% z = f(x)
% J = f'(x)
%
z=fun(x);
n=numel(x);
m=numel(z);
A=zeros(m,n);
h=n*eps;
for k=1:n
    x1=x;
    x1(k)=x1(k)+h*i;
    A(:,k)=imag(fun(x1))/h;
end

The same error is showing to me kindly tell how to fix it

[Stephen23]

@Jay shankar Nanda: Note that inside ekf you call ekf. So when you first call ekf, it will run until the line where it calls ekf. Then it will call ekf, and run until it reaches the line where it calls ekf. Then it will call ekf, and run until it reaches the line where it calls ekf. Then it will call ekf, and run until it reaches the line where it calls ekf. Then it will call ekf, and run until it reaches the line where it calls ekf. Then it will call ekf, and run until it reaches the line where it calls ekf... etc., until your code reaches the recursion limit.

Is that your intent?

Note that your main functions inputs are all/mostly ignored within the function. It looks as if the main function should be renamed or perhaps converted to a script.