and `OP` for $L = 64, 128, 256, 512, 1024$ on four of the largest data sets (the best values are [underlined]{}). It can be seen that the performances of both methods are relatively insensitive to the value of $L$. Therefore, in the subsequent experiments we report the results for $L = 256$.

  ID   Criterion           $L=64$     $L=128$      $L=256$     $L=512$    $L=1024$
  ---- ------------- ------------ ----------- ------------ ----------- -----------
  9    Initial SSE     [143859]{}      144651       144658      144637      144638
       Final SSE           118267      119127   [118033]{}      118033      118034
  10   Initial SSE           1987        1920     [1919]{}        1920        1920
       Final SSE         [1742]{}    [1742]{}     [1742]{}    [1742]{}    [1742]{}
  11   Initial SSE           3242    [3192]{}         3231        3202        3202
       Final SSE             2742    [2734]{}     [2734]{}    [2734]{}    [2734]{}
  15   Initial SSE      [17448]{}       17504        17504       17504       17504
       Final SSE        [14581]{}   [14581]{}    [14581]{}   [14581]{}   [14581]{}

  : \[tab\_num\_bins\_var\] Influence of $L$ on the effectiveness of Otsu Var-Part ($L$: \# histogram bins)

  ID   Criterion          $L=64$     $L=128$      $L=256$      $L=512$    $L=1024$
  ---- ------------- ----------- ----------- ------------ ------------ -----------
  9    Initial SSE        123527      123095   [122528]{}       123129      123342
       Final SSE          118575      118577       119326   [118298]{}      118616
  10   Initial SSE          1855    [1807]{}         1835         1849        1848
       Final SSE        [1742]{}    [1742]{}     [1742]{}     [1742]{}    [1742]{}
  11   Initial SSE          2994        2997         2995         2995    [2991]{}
       Final SSE        [2747]{}    [2747]{}     [2747]{}     [2747]{}    