title: Uniform LBP with scikit-image local_binary_pattern function
body: <p>I'm using the local_binary_pattern from skimage.feature with uniform mode like this:</p>
  
  <pre><code>&gt;&gt;&gt; from skimage.feature import local_binary_pattern
  &gt;&gt;&gt; lbp_image=local_binary_pattern(some_grayscale_image,8,2,method='uniform')
  &gt;&gt;&gt; histogram=scipy.stats.itemfreq(lbp_image)
  &gt;&gt;&gt; print histogram
  [[  0.00000000e+00   1.57210000e+04]
   [  1.00000000e+00   1.86520000e+04]
   [  2.00000000e+00   2.38530000e+04]
   [  3.00000000e+00   3.23200000e+04]
   [  4.00000000e+00   3.93960000e+04]
   [  5.00000000e+00   3.13570000e+04]
   [  6.00000000e+00   2.19800000e+04]
   [  7.00000000e+00   2.46530000e+04]
   [  8.00000000e+00   2.76230000e+04]
   [  9.00000000e+00   4.88030000e+04]]
  </code></pre>
  
  <p>As I'm taking 8 pixels in the neighborhood, it's expected to obtain 59 different LBP codes (because the uniform method), but instead it gives me only 9 different LBP codes. More generally, always return P+1 labels (where P is the number of neighbors).</p>
  
  <p>It's another kind of uniform method, or I'm misunderstanding something?</p>