nearest-neighbor

My Plain Code without interpolation: im1 = imread('lena.jpg');imshow(im1); [m,n,p]=size(im1); thet = rand(1); m1=m*cos(thet)+n*sin(thet); n1=m*sin(thet)+n*cos(thet); for i=1:m for j=1:n t = uint16((i-m/2)*cos(thet)-(j-n/2)*sin(thet)+m1/2); s = uint16((i-m/2)*sin(thet)+(j-n/2)*cos(thet)+n1/2); if t~=0 && s~=0 im2(t,s,:)=im1(i,j,:); end end end figure; imshow(im2); This code creates black spot, the problem is how to do interpolation? Thank you all for any illumination. P.S. Not asking for build-in function: imrotate(im1,1/thet,'nearest'); To rotate the image without the black spots, you need to

I have a dataset of approximately 100,000 (X, Y) pairs representing points in 2D space. For each point, I want to find its k-nearest neighbors. So, my question is - what data-structure / algorithm would be a suitable choice, assuming I want to absolutely minimise the overall running time? I'm not looking for code - just a pointer towards a suitable approach. I'm a bit daunted by the range of choices that seem relevent - quad-trees, R-trees, kd-trees, etc. I'm thinking the best approach is to build a data structure, then run some kind of k-Nearest Neighbor search for each point. However, since

I need to find for each point of the data set all its nearest neighbors. The data set contains approx. 10 million 2D points. The data are close to the grid, but do not form a precise grid... This option excludes (in my opinion) the use of KD Trees, where the basic assumption is no points have same x coordinate and y coordinate. I need a fast algorithm O(n) or better (but not too difficult for implementation :-)) ) to solve this problem ... Due to the fact that boost is not standardized, I do not want to use it ... Thanks for your answers or code samples... I would do the following: Create a

I have a numpy array like this: x = np.array([[1,2,3],[4,5,6],[7,8,9]]) I need to create a function let's call it "neighbors" with the following input parameter: x: a numpy 2d array (i,j): the index of an element in a 2d array d: neighborhood radius As output I want to get the neighbors of the cell i,j with a given distance d . So if I run neighbors(im, i, j, d=1) with i = 1 and j = 1 (element value = 5) I should get the indices of the following values: [1,2,3,4,6,7,8,9] . I hope I make it clear. Is there any library like scipy which deal with this? I've done something working but it's a rough

Given n points in d-dimensional space, there are several data structures, such as Kd-Trees, Quadtrees, etc. to index the points. On these data structures it is possible to implement straight-forward algorithm for nearest neighbor queries around a given input point. Is there a book, paper, survey, ... that compares the theoretical (mostly expected) runtime of the nearest neighbor query on different data structures? The data I am looking at is composed of fairly small point clouds, so it can all be processed in main memory. For the sake of simplicity, I assume the data to be uniformly