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All,

I'm looking for some ideas/code that will help me build 5 dimensional bezier patches to 'wrap' 5d data.

The data is actually a colour image and is therefore in (x,y,r,g,b) space. I could take it down to grey-scale (x,y,k) but I'd rather stick with r,g,b if possible.

I want to build Bezier patches to fit the data with a defined tolerance.

Anyone got ideas/links to:

1. Making Bezier patches to fit data.

2. Taking Bezier patches beyond the standard 3 dimensions

Paul

I'm looking for some ideas/code that will help me build 5 dimensional bezier patches to 'wrap' 5d data.

The data is actually a colour image and is therefore in (x,y,r,g,b) space. I could take it down to grey-scale (x,y,k) but I'd rather stick with r,g,b if possible.

I want to build Bezier patches to fit the data with a defined tolerance.

Anyone got ideas/links to:

1. Making Bezier patches to fit data.

2. Taking Bezier patches beyond the standard 3 dimensions

Paul

Not sure if this is helpful, but I thought it might assist.

Regards

P.S. Would it be 'sextic' after qubic, quartic and quintic?

Paul

r=R(x,y)

b=B(x,y)

g=G(x,y)

ie 3 2-D functions, where (r,g,b) = (R(x,y), B(x,y), G(x,y)) is then your 3D point in your colour space

r(x,y) = sum {r(i)r(j)Bin(s)Bjn(t)} where r(i) is the red value at the ith control point s ,t are the parameters for the curve, and Bin(s) is the ith Bezier function in the nth order set of Bezier functions you are using

I see where you are coming from. I could build 3 patches, one for each colour and combine them using a quite simple function when needed. This would probably work well but it feels like an iunnecessary optimisation. Making the data fit the algorithm.

Consider two adjacent pixels. The colour difference between them would be equivalent to the distance between their two 3d coordinates. This figure would bear some resemblance to what you are suggesting but it just feels wrong. After all, if I switched my data to c,m,y perhaps, this would change the three patches you are recommending but if the patch is over the real 3d colour-space then the single patch would remain unchanged.

I like your simplification but I would like, firstly, to treat it as a possible future change to my goal and to investigate the 5d idea first to see if it is even possible.

Paul

All color interepoloation I am aware of intepolates the primaries independently

I want to be sure that the reason behind this is a real mathematical reason, not just another data-optimisation.

Its like saying that the distance between two points in 3-dimensions is 'proportional' to the average of the three distances in each orthogonal coordinate plane and therefore can be treated as equivalent. To me, an r,g,b point in colour-space is exactly the same point as its c,m,y equivalent but the 'c' and 'm' and 'y' are different because they come from a rotated (and incidentally reflected) coordinate system. Curve-fitting in each coordinate separately may well generate a perfectly usable solution but I want to investigate the possibilities of 5d splines.

Its like saying that that because the presence of the smaller factors of a polynomial are relatively insignificant they can be ignored. I'm the kind of person who says 'how small' and 'how insignificant'?

Paul

Just for curiosity: Do you whant to create a compress algorithm or a magnification one?

Its funny how you get your best insights overnight. I apologise for being dumb and unable to see what you so patiently tried to explain. It is all much clearer now.

Essentially, I still have a 2d plane to interpolate, the only difference between this and the classic qubic spline problem is the return values of the function.

Classic qubic spline:

z = f(x,y)

my requirement:

(r,g,b) = f(x,y)

And, as you quite correctly point out, (r,g,b) = f(x,y) can be easily and without error be treated as:

(r = fr(x,y), g = fg(x,y), b = fb(x,y))

where fr, fg, fb are functions that are directly derived from 'f' that deliver each component individually.

Right ... now I am out of the woods, where next?

Does anyone have any references that could help me answer the following questions:

1. What techniques can be used to determine a good way of dividing up an image so that not too many patches must be used. I'm not asking for minimal here, just a good way away from maximal.

1.1 Is there a technique for blending two adjacent patches into one?

2. Are square patches the best or would triangular be worth studying.

Paul

Neither, I want to map the colour image so that I can then analyse it.

Paul

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