image error

 x=imread('pp.jpg');
>> [m,n]=size(x);
>> x=[[1:m]' x];
??? Error using ==> horzcat
CAT arguments dimensions are not consistent.

why this error occured and how to remove this error?

[Image Analyst]

What are you trying to do? Put up a ramp from 1 to m along the first row or first column of x? Be aware that if the number of rows is more than 255 your ramp will get clipped to 255.

Answer 1

[Andreas Goser]

Your code would work with some images, but in your case, x likely is 3 dimensional. E.g this examples image is

imdata = imread('ngc6543a.jpg');

Depending on what you need, you may want to convert x or extract the data relevant for you.

[pdp rpa]

Thnks for your kind response sir, actually i have a code of dbscan where , x=[[1:m]' x]; is used.

But the main problem is that i want to use this code for image segmentation and i am not getting how to do this. plz help me, this is my project work. The code is given as:

% -------------------------------------------------------------------------

% Function: [class,type]=dbscan(x,k,Eps)

% -------------------------------------------------------------------------

% Aim:

% Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN)

% -------------------------------------------------------------------------

% Input:

% x - data set (m,n); m-objects, n-variables

% k - number of objects in a neighborhood of an object

% (minimal number of objects considered as a cluster)

% Eps - neighborhood radius, if not known avoid this parameter or put []

% -------------------------------------------------------------------------

% Output:

% class - vector specifying assignment of the i-th object to certain

% cluster (m,1)

% type - vector specifying type of the i-th object

% (core: 1, border: 0, outlier: -1)

% -------------------------------------------------------------------------

function [class,type]=dbscan(x,k,Eps)

[m,n]=size(x);

if nargin<3 | isempty(Eps)

[Eps]=epsilon(x,k);

end

x=[[1:m]' x];

[m,n]=size(x);

type=zeros(1,m);

no=1;

touched=zeros(m,1);

for i=1:m

if touched(i)==0;

ob=x(i,:);

D=dist(ob(2:n),x(:,2:n));

ind=find(D<=Eps);

if length(ind)>1 & length(ind)<k+1

type(i)=0;

class(i)=0;

end

if length(ind)==1

type(i)=-1;

class(i)=-1;

touched(i)=1;

end

if length(ind)>=k+1;

type(i)=1;

class(ind)=ones(length(ind),1)*max(no);

while ~isempty(ind)

ob=x(ind(1),:);

touched(ind(1))=1;

ind(1)=[];

D=dist(ob(2:n),x(:,2:n));

i1=find(D<=Eps);

if length(i1)>1

class(i1)=no;

if length(i1)>=k+1;

type(ob(1))=1;

else

type(ob(1))=0;

end

for i=1:length(i1)

if touched(i1(i))==0

touched(i1(i))=1;

ind=[ind i1(i)];

class(i1(i))=no;

end

end

end

end

no=no+1;

end

end

end

i1=find(class==0);

class(i1)=-1;

type(i1)=-1;

%...........................................

function [Eps]=epsilon(x,k)

% Function: [Eps]=epsilon(x,k)

%

% Aim:

% Analytical way of estimating neighborhood radius for DBSCAN

%

% Input:

% x - data matrix (m,n); m-objects, n-variables

% k - number of objects in a neighborhood of an object

% (minimal number of objects considered as a cluster)

[m,n]=size(x);

Eps=((prod(max(x)-min(x))*k*gamma(.5*n+1))/(m*sqrt(pi.^n))).^(1/n);

%............................................

function [D]=dist(i,x)

% function: [D]=dist(i,x)

%

% Aim:

% Calculates the Euclidean distances between the i-th object and all objects in x

%

% Input:

% i - an object (1,n)

% x - data matrix (m,n); m-objects, n-variables

%

% Output:

% D - Euclidean distance (m,1)

[m,n]=size(x);

D=sqrt(sum((((ones(m,1)*i)-x).^2)'));

if n==1

D=abs((ones(m,1)*i-x))';

end

[Andreas Goser]

That sounds like a good reason for you to contact Technical Support. Alone or with your professor.

[pdp rpa]

thnks .i am doing this project alone. plz help me