Added links and removed some pre-filled code

Author: jo-mueller

05_feature_extraction/03_Otsu_threshold.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Otsu's threshold method (optional)\n",
     "\n",
-    "In this notebook you will learn how to write your own implementation of Otsu's threshold method (Otsu et al., IEEE, 1979). It will allow you to apply many of the previously introduced concepts of Python programing for image data.\n",
+    "In this notebook you will learn how to write your own implementation of Otsu's threshold method ([Otsu et al., IEEE, 1979)](https://ieeexplore.ieee.org/document/4310076). It will allow you to apply many of the previously introduced concepts of Python programing for image data.\n",
     "\n",
     "## The algorithm\n",
     "Otsu's threshold method selects a threshold value to which all pixels in the image are compared. The pixels with gray values below and above the threshold are denoted $A$ and $B$. For a given threshold $t$ between the image's minimal and maximal gray value $t \\in ]min(image), max(image)[$, the method calculates the weighted variances $\\eta_w$ of pixels in $A$ and $B$:\n",
@@ -155,7 +155,7 @@
    "source": [
     "Next, calculate the variances and their weights for $A$ and $B$:\n",
     "\n",
-    "*Tip: Search online whether numpy may have a convenient function for this!*"
+    "*Tip: Search online whether numpy may have a [convenient function](https://numpy.org/doc/stable/reference/routines.statistics.html) for this!*"
    ]
   },
   {
@@ -234,8 +234,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "variances_below_array = np.asarray(variances_below)\n",
-    "variances_above_array = np.asarray(variances_above)"
+    "variances_below_array = \n",
+    "variances_above_array = "
    ]
   },
   {