Recent content by daviddoria

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    Normalized correlation with a constant vector

    Yes, that is exactly what I am observing. You got it :)
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    Normalized correlation with a constant vector

    I am confused how to interpret the result of preforming a normalized correlation with a constant vector. Since you have to divide by the standard devation of both vectors (reference: http://en.wikipedia.org/wiki/Cross-correlation#Normalized_cross-correlation ) , if one of them is constant (say a...
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    Vector difference metric that considers the variance of the components

    @chiro - what kind of statistical techniques? Are you suggesting just computing a Euclidean style distance between local FFT components or something like that? Doesn't this ONLY contain frequency information, and no information in the spatial domain (i.e. the colors don't have to match at all)...
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    Vector difference metric that considers the variance of the components

    Yep, that's what I want :) Of course there isn't a perfect one, but I thought someone here might have an idea of a better one than a simple Euclidean distance. Assume everything is quite low resolution. For instance, this picture of the entire house/grass/road is ~500x500. Yes, only...
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    Vector difference metric that considers the variance of the components

    Hi Stephen, The values are just the RGB pixel values. It does very well in most cases, but when it fails (like the case I described), it fails miserably. Yes, simple and fast is definitely a requirement. I would like to avoid this at all costs :) I hope this clarifies some...
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    Vector difference metric that considers the variance of the components

    I am trying to match little square patches in an image. You can imagine that these patches have been "vectorized" in that the values are reordered consistently into a 1D array. At first glance, it seems reasonable to simply do a Euclidean distance style comparison of two of these arrays to get a...
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    Weighted least squares best fit plane

    I know that the plane through the center of mass whose normal is the eigenvector corresponding to the smallest eigenvalue of the scatter matrix of a set of points is the best fit plane. I now want to do a "weighted least squares" - would I simply multiply the...
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    Distance difference problem

    I think it works if you just use the law of cosines. See attached.
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    Distance difference problem

    Gah, you are right. However, this requires I have p. What if I don't have p?
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    Distance difference problem

    I guess another way to say it is: "I need a difference function which will produce 'x' for f(p,d1) and also produce 'x' for f(d2,d1)"
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    Distance difference problem

    I guess even better would be some transform of each distance, so that: f(d1)-f(p)=f(d2)-f(d1) of course f() may not be exactly the same function, it may depending on the position (i.e. it could be f(d1)-f(p)=g(d2)-g(d1) or something like that).
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    Distance difference problem

    See the image in the attached document. I am looking for a function which will make f(d1-p)=f(d2-d1)=f(d3-d2) (see the very last part of the document) I thought it would be as simple as dividing by the angle between the lines, but that doesn't seem to work. Is it reasonable to do this...
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    Why do we have to learn that?

    This is for mathematics students. Certainly if you study mathematics as your "field" then you should know these things. But engineers need not know them, and certainly people studying/practicing non-technical disciplines need not study them in high school.
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    Why do we have to learn that?

    Haha Manheis, as I was reading this thread I was thinking "how has no one posted the Worlfram TED talk??". I don't work for Wolfram either, but he is definitely right. Frankly, I am quite shocked that people here do not support this concept. The people who will build the next-gen Maple style...
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    Interpretation of power rule for integration applied to 1/x

    We all know \int \frac{1}{x} dx = ln(x) + c but if you try to apply the power rule for integration: \int x^n dx = \frac{x^{n+1}}{n+1} + c you get \int x^{-1} dx = \frac{x^0}{0} What can you learn from this/what does this mean? David
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