kd-tree | An implementation of k-d tree | Dataset library

I know this is a super basic question, but I'm stuck.

I am attempting to create a data frame. I want to create a data frame that contains the results of the for loop.

I am creating a small euclidean clustering node for the point cloud, utilizing PCL's implementation of the KD tree.

Currently, my subscriber definition looks like this,

Currently, I'm working on image stitching of aerial footage. I'm using the dataset, get from OrchardDataset. First of all, thanks to some great answers on stackoverflow, especially the answer from @alkasm (Here and Here). But I having an issue, as you can see below at Gap within the stitched image section.

I used the H21, H31, H41, etc to wrap the images. The stitched image using H21 is excellent, but when wrap the img3 to current stitched image using H31, result shown terrible alignment between img3 and current stitched image. As the more images I wrap, the gap gets bigger and the images totally not well aligned.

Does the brillant stackoverflow community have an ideas on how can I solve this problem?

These are the steps I use to stitch the images:

1. Extract the frame every second from the footage and undistort the image to get rid of fish-eye effect using the provided camera calibration matrix.
2. Compute the SIFT feature descriptors. Set up macther using FLANN kd-tree and find matches between the images. Find the Homography (H21, H32, H43 and etc, where H21 refer to the homography which warps imag2 into coordinates of img1)
3. Compose the homography with the previous homographies to get net homography using the method suggested in Here. (Compute H31, H41, H51, etc)
4. Wrap the images using the answer provided in Here.

Gap within the stitched image:

I'm using the first 10 images get from OrchardDataSet.

Stitched Image with Gaps

Here's portion of my script:

main.py

ref_img is the first frame (img1). AdjHomoSet contain the images to be wraped (img2, img3, img4, etc). AccHomoSet contain the net homography (H31, H41, H51, etc)

I have a Set of n points and a more or less arbitrary circle. Now I want to check if the circle contains at most k of the points.

Right now I'm just brute force testing all of the points. Since I need to answer this question for a lot of circles, a preprocessing time of up to n²log(n) would be fine.

The optimal data structure would most likely be the order k-Voronoi Diagram (not the one for dimension k), but I would need to implement it myself and therefore I would like to know if I have other (simpler) options.

Another Idea for some speed up would be using a KD-Tree.

I would like to know, if I'm missing another way to do it.

I am trying to create a kd-tree through scipy's KD_tree class built by objects rather than pure coordinates. The objects has a (x,y) tuple, and the tree is based upon this, but i would like to include the object itself as the node/in the node.

Is there some "easy" approach to this? Had a look on scipy kdtree with meta data, which says to use a third dimension as a object pointer(?). Wouldn't the tree then apply this value to the comparison of neighbors? I am also in the same boat as this gentleman, where creating my own kd-tree would be nice to skip for now.

PS. This is my first post, so be gentle with me ;)

I know I can use a data structure such as the kd-tree to look for the k nearest points to each point in the plane (using the Euclidean distance). But I've already constructed a Delaunay triagulation in my program, so I'd like to find the k nearest neighbors more efficiently by taking adavntage of the Delaunay triangulation. I'm using Matlab, and the delaunayTriangulation class allows to query only the first nearest neighbor to a given point. How can I find the k nearest neighbors in MATLAB using the Delaunay triangulation? Or otherwise, if I have to implement myself the k nearest neighbors search algorithm from the Delaunay triangulation, can you point me out to such an algorithm?

Given a square grid with side N. Also given a set of input points on the grid. Need to find another set of points on the grid (can be multiple) that have the maximal Manhattan distance from the given input points.

For example with N = 4 and input [p1:{0, 0}, p2:{3, 3}] the output should be [{0, 3}, {1, 2}, {2, 1}, {3, 0}] with the distance equal to 3.

In these slides (13) the deletion of a point in a kd-tree is described: It states that the left subtree can be swapped to be the right subtree, if the deleted node has only a left subtree. Then the minimum can be found and recursively be deleted (just as with a right subtree).

This is because kd-trees with equal keys for the current dimensions should be on the right.

My question: Why does the equal key point have to be the right children of the parent point? Also, what happens if my kd-tree algorithm returns a tree with an equal key point on the left?

For example: Assume the dataset (7,2), (7,4), (9,6) The resulting kd-tree would be (sorted with respect to one axis):

I am trying to use Kdtree data structure to remove closest points from an array preferablly without for loops.