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Simply equation in docstring of match_docstring.

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Tony S Yu
2012-02-26 10:22:54 -05:00
parent a87bcb2d73
commit 2e8fcef89b
1 changed files with 11 additions and 8 deletions
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skimage/feature/template.py
+11 -8
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@@ -21,17 +21,20 @@ def match_template(image, template, method='norm-coeff', pad_output=True):
method : str
The correlation method used in scanning.
T represents the template, I the image and R the result.
The summation is done over X = 0..w-1 and Y = 0..h-1 of the template.
All sums are done over X = 0..w-1 and Y = 0..h-1 of the template.
'norm-coeff':
R(x, y) = Sum(X,Y)[T(X, Y) * I(x + X, y + Y)] / N
N = sqrt(Sum(X,Y)[T(X, Y)**2] * Sum(X,Y)[I(x + X, y + Y)**2])
R(x, y) = Sum[T(X, Y) * I(x + X, y + Y)] / N
N = sqrt(Sum[T(X, Y)**2] * Sum[I(x + X, y + Y)**2])
'norm-corr':
R(x,y) = Sum(X,y)[T'(X, Y) * I'(x + X, y + Y)] / N
N = sqrt(Sum(X,y)[T'(X, Y)**2] * Sum(X,Y)[I'(x + X, y + Y)**2])
R(x,y) = Sum[T'(X, Y) * I'(x + X, y + Y)] / N
N = sqrt(Sum[T'(X, Y)**2] * Sum[I'(x + X, y + Y)**2])
where:
T'(x, y) = T(X, Y) - 1/(w * h) * Sum(X',Y')[T(X', Y')]
I'(x + X, y + Y) = I(x + X, y + Y)
- 1/(w * h) * Sum(X',Y')[I(x + X', y + Y')]
T'(x, y) = T(X, Y) - mean(T)
I'(x + X, y + Y) = I(x + X, y + Y) - mean[I(X', Y')]
mean[I(X', Y')] = mean of image region under the template.
pad_output : bool
If True, pad output array to be the same size as the input image.
Otherwise, the output is an array with shape `(M - m + 1, N - n + 1)`