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copied!<p>Following the discussion in the comments, I am adding another solution. For a given point, we compute the neighboring points within a specified number of steps (radius if you will). This is shown for both the 2D and the 3D case.</p> <h1>2D matrix</h1> <pre><code>siz = [10 15]; %# matrix size p = [5 10]; %# 2D point location %# neighboring points k = 2; %# radius size [sx,sy] = ndgrid(-k:k,-k:k); %# steps to get to neighbors xy = bsxfun(@plus, p, [sx(:) sy(:)]); %# add shift xy = bsxfun(@min, max(xy,1), siz); %# clamp coordinates within range xy = unique(xy,'rows'); %# remove duplicates xy(ismember(xy,p,'rows'),:) = []; %# remove point itself %# show solution figure line(p(1), p(2), 'Color','r', ... 'LineStyle','none', 'Marker','.', 'MarkerSize',50) line(xy(:,1), xy(:,2), 'Color','b', ... 'LineStyle','none', 'Marker','.', 'MarkerSize',20) grid on, box on, axis equal axis([1 siz(1) 1 siz(2)]) xlabel x, ylabel y </code></pre> <p><img src="https://i.stack.imgur.com/xrroH.png" alt="screenshot_2D_matrix"></p> <hr> <h1>3D matrix</h1> <pre><code>siz = [10 15 8]; %# matrix size p = [5 10 4]; %# 3D point location %# neighboring points k = 2; %# radius size [sx,sy,sz] = ndgrid(-k:k,-k:k,-k:k); %# steps to get to neighbors xyz = bsxfun(@plus, p, [sx(:) sy(:) sz(:)]); %# add shift xyz = bsxfun(@min, max(xyz,1), siz); %# clamp coordinates within range xyz = unique(xyz,'rows'); %# remove duplicates xyz(ismember(xyz,p,'rows'),:) = []; %# remove point itself %# show solution figure line(p(1), p(2), p(3), 'Color','r', ... 'LineStyle','none', 'Marker','.', 'MarkerSize',50) line(xyz(:,1), xyz(:,2), xyz(:,3), 'Color','b', ... 'LineStyle','none', 'Marker','.', 'MarkerSize',20) view(3), grid on, box on, axis equal axis([1 siz(1) 1 siz(2) 1 siz(3)]) xlabel x, ylabel y, zlabel z </code></pre> <p><img src="https://i.stack.imgur.com/l7cAu.png" alt="screenshot_3D_matrix"></p> <p>HTH</p>

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