When spatial data is represented using Hilbert numbers, a rectangular range is mapped to multiple ranges of Hilbert numbers. Is there any quick way to find this mapping without going through every cell the given rectangle, R, overlaps with? I can think of considering only the border cells of R and then sorting them... Is there any better way?
Thanks.
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Given a 2D space inhabited by circle shapes (grey), is there an easy way to find the largest empty circular area (blue)? I guess I have to look at space partioning, maybe quadtrees, but I'm curious as to whether there might be existing solutions, especially for circular shapes?
My idea (neither thought through or implemented, and computationally expensive):
Divide the space into a grid, the finer, the more precise
At every grid point, calculate D, the smallest distance to any of the neighbouring circles C_i (= distance to center of C_i - radius of C_i)
Choose the grid point that maximizes D
The result for certain calculations when drawn using python matshow
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Is there any way to draw lines
like a border between the various colors like in this figure?
This is a grid n X n that looks like this initially.
Each square has 4 small squares(lattices) within it. i.e in the case of the first square, it has (0,0), (1,0), (0,1) and (1,1) indices occupied by the same number say 0 and the next square has (0,3), (0,3), (1,3) and (1,3) indices occupied by a different number say 1.
This is resulting in a different color. After subjecting it to some mathematical operations I got the first image. Now I want to distinguish between these colors as in figure 2. Is there a way to do this? Any leads would be appreciated!
You could iterate over each cell in the final grid to compare its color with its N, S, E, W neighbors, and draw a line segment corresponding to the border, when the condition is met, and not when the cells share the same color.
I would have provided a small sample code, but you did not explain how your cells and grids are represented; however, this is rather straightforward code that you can probably write.
I have to find the nearest color. For example, I have two colors colorA1, colorA2 which are nearly same color. And also I have other color colorB1.
And I need such a method:
Color getNearestColor(colorA1, colorA2, colorB1). This method should give me the colorB2 which is calculated by using the difference of colorA1 and colorA2, then using their distance it should give me colorB2 which has the same distance as in colorA1 and colorA2.
Can you give some ideas how to implement it?
To find the nearest colour, you need a definition of "near", so a metric.
In Wikipedia you will find different metrics of color differences.
Personally I would use the 2*R*R + 4*G*G + 3*B*B. (no need for square roots, you will just compare same metrics). Easy to calculate, you can use just integers (if you use 32 bit integers, you will have no overflow).
Then find which colour has the smallest differences between your target colour.
The other methods are more precise, but in that case "RGB" is not enough. You need to know which colour space are using (probably you are in sRGB).
wondering if anyone has any insight as to how to ascertain the order of differently sized rectangles from left to right and from top to bottom when they are not already aligned to any grid, and they are differently sized and/or rotated. Some might also be missing.
As anyone can see from the illustration, the objects should be numbered as shown. But how, mathematically or programmatically, can I determine this? What is the logic? I don't even know what words to use to describe the problem.
This looks like a rather complex problem; maybe some algorithm already exists, IDK.
Approach 1: grid positioning.
One approach could start with trying to position the rectangles on a grid whose mesh size will have to be calculated; maybe a best fit to the size of the rectangles (H & W, or surface, maybe?)
Once a reasonable grid has been determined, it must be appropriately placed over the rectangles; maybe in such a way that minimizes row overlap and column overlap of the rectangles?
The last step would consist of traversing the grid row by row, and assigning a label to each rectangle; maybe based on the max common surface shared by a grid cell and a rectangle?
There will be many edge cases to identify and resolve.
Approach 2: sweep line.
Alternatively, a sweep line numbering of the rectangles from N, S, E, and W, and an appropriate weighting/averaging of the numbering of the rectangles from each direction, might give good results?
It may require several passes, after identifying what could be rows and columns, in order to find a "best fit".
This second approach is likely easier to implement.
I have an image and I am picking colors by RGB (data sampling). I select N points from a specific region in the image which has the "same" color. By "same" I mean, that part of the image belongs to an object, (let's say a yellow object). Each picked point in the RGB case has three values [R,G,B]. For example: [120,150,225]. And the maximum and minimum for each field are 255 and 0 respectively.
Let's assume that I picked N points from the region of the object in the image. The points obviously have different RGB values but from the same family (a gradient of the specific color).
Question:
I want to find a range for each RGB field that when I apply a color filter on the image the pixels related to that specific object remain (to be considered as inliers). Is it correct to find the maximum and minimum from the sampled points and consider them as the filter range? For example if the max and min of the field R are 120 ,170 respectively, can it be used as a the range that should be kept.
In my opinion, the idea is not true. Because when choosing the max and min of a set of sampled data some points will be out of that range and also there will be some point on the object that doesn't fit in this range.
What is a better solution to include more points as inliers?
If anybody needs to see collected data samples, please let me know.
I am not sure I fully grasp what you are asking for, but in my opinion filtering in RGB is not the way to go. You should use a different color space than RGB if you want to compare pixels of similar color. RGB is good for representing colors on a screen, but you actually want to look at the hue, saturation and intensity (lightness, or luminance) for analysing visible similarities in colors.
For example, you should convert your pixels to HSI or HSL color space first, then compare the different parameters you get. At that point, it is more natural to compare the resulting hue in a hue range, saturation in a saturation range, and so on.
Go here for further information on how to convert to and from RGB.
What happens here is that you implicitly try to reinvent either color indexing or histogram back-projection. You call it color filter but it is better to focus on probabilities than on colors and color spaces. Colors of course not super reliable and change with lighting (though hue tends to stay the same given non-colored illumination) that's why some color spaces are better than others. You can handle this separately but it seems that you are more interested in the principles of calculating "filtering operation" that will do segmentation of the foreground object from background. Hopefully.
In short, a histogram back-projection works by first creating a histogram for R, G, B within object area and then back-projecting them into the image in the following way. For each pixel in the image find its bin in the histogram, calculate its relative weight (probability) given overall sum of the bins and put this probability into the image. In such a way each pixel would have probability that it belongs to the object. You can improve it by dividing with probability of background if you want to model background too.
The result will be messy but somewhat resemble an object segment plus some background noise. It has to be cleaned and then reconnected into object using separate methods such as connected components, grab cut, morphological operation, blur, etc.