Find the closest co-ordinates (between to uneven list of cooardinates)

I want to select the coordinates in PointinCh1 list which have a coordinates in PointinCh2 list close to them. The code that I am suing at the moment is creating a list called 'closestForPin2toPin1', which is not helpful in finding the indexes in PointinCh1 .

Your help will be appreciated

 PointinCh1 = 
    20   482
    19   359
    45   438
    61   248
    90   403
   104    95
   149   335
   148   392
   161    73
   186    29
   188   236
   189   319
   200   162
   208    70
   204   198
   203   343
   214   250
   225   307
   233   171
   238   205
   237   245
   253   148
   264   362
   281    34
   300   341
   306    88
   305   203
   328   234
   326   164
   330    20
   364   199
   424   241
   433   314
   491   187
PointinCh2 = 
    99   399
   104    95
   149   335
   148   392
   158    82
   184   238
   190   320
   202   343
   236   246
   263   361
   299   342
   330    20
   493   193
%compute Euclidean distances:
for idis=1: length(PointinCh1)
distances = sqrt(sum(bsxfun(@minus, PointinCh2, PointinCh1(idis,:)).^2,2));
%find the smallest distance and use that as an index into B:
closestForPin2toPin1(idis,:)= PointinCh2(find(distances==min(distances)),:);
end

Answer 1

[KSSV]

Read about inbuilt function knnsearch .

http://in.mathworks.com/help/stats/knnsearch.html

[Peyman Obeidy]

[n,d]=knnsearch(PointinCh1,PointinCh2,'k',10,'distance','minkowski','p',5); what is next? :)

[KSSV]

You read the documentation part. knnsearch gives you indices of the points closer to the given points, that's what you wanted.

Answer 2

[Image Analyst]

Try pdist2():

PointinCh1 = [
    20   482
    19   359
    45   438
    61   248
    90   403
   104    95
   149   335
   148   392
   161    73
   186    29
   188   236
   189   319
   200   162
   208    70
   204   198
   203   343
   214   250
   225   307
   233   171
   238   205
   237   245
   253   148
   264   362
   281    34
   300   341
   306    88
   305   203
   328   234
   326   164
   330    20
   364   199
   424   241
   433   314
   491   187]
PointinCh2 = [...
    99   399
   104    95
   149   335
   148   392
   158    82
   184   238
   190   320
   202   343
   236   246
   263   361
   299   342
   330    20
   493   193]
d = pdist2(PointinCh1, PointinCh2)
minDistance = min(d(:))
[row, col] = find(d == minDistance)

Answer 3

[Peyman Obeidy]

Thank you both, I think knnsearch works better, as you see I have 13 points in PointinCh2, so I expect to find them in PointinCh1

[Image Analyst]

Knowing both, I can't definitively say one is better than the other. They're both just a few lines of code and both will handle finding the indexes closest to each point. They both check the distance of every point to every other point. And they both require the Statistics and Machine Learning Toolbox. So in end they seem pretty equivalent, but I'm glad KSSV mentioned knnsearch() because I did not think of that, and it's a good answer.