Commit 5504a64c authored by André Anjos's avatar André Anjos 💬

More documentation

parent 0dff0b04
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......@@ -12,24 +12,27 @@ class MiuraMatch (Algorithm):
The method is based on "cross-correlation" between a model and a probe image.
It convolves the binary image(s) representing the model with the binary image
representing the probe (rotated by 180 degrees), to evaluate how they
representing the probe (rotated by 180 degrees), and evaluates how they
cross-correlate. If the model and probe are very similar, the output of the
correlation corresponds to a single scalar and approaches a maximum. The
correlation corresponds to a single scalar and approaches a maximum. The
value is then normalized by the sum of the pixels lit in both binary images.
Therefore, the output of this method is a floating-point number in the range
:math:`[0, 0.5]`. The higher, the better match.
In case model and probe represent images from the same vein structure, but
are misaligned, the output is not guaranteed to be accurate. To mitigate this
aspect, Miura et al. proposed to add a *small** erosion factor to the model
aspect, Miura et al. proposed to add a *small* cropping factor to the model
image, assuming not much information is available on the borders (``ch``, for
the vertical direction and ``cw``, for the horizontal direction). This allows
the convolution to yield searches for different areas in the probe image. The
maximum value is then taken from the resulting operation. The convolution
result is normalized by the pixels lit in both the eroded model image and the
matching pixels on the probe that yield the maximum on the resulting
result is normalized by the pixels lit in both the cropped model image and
the matching pixels on the probe that yield the maximum on the resulting
convolution.
For this to work properly, input images are supposed to be binary in nature,
with zeros and ones.
Based on N. Miura, A. Nagasaka, and T. Miyatake. Feature extraction of finger
vein patterns based on repeated line tracking and its application to personal
identification. Machine Vision and Applications, Vol. 15, Num. 4, pp.
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