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bob/bio/vein/algorithm/MiuraMatch.py

@@ -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.