diff --git a/docs/examples/image_segmentation/fast_scanning.jl b/docs/examples/image_segmentation/fast_scanning.jl
new file mode 100644
index 00000000..b29218c1
--- /dev/null
+++ b/docs/examples/image_segmentation/fast_scanning.jl
@@ -0,0 +1,50 @@
+# ---
+# cover: assets/fastscanning.gif
+# title: Fast Scanning Segmentation Algorithm
+# description: This demo shows how to use the meanshift algorithm to segment an image.
+# author: Ashwani Rathee
+# date: 2021-09-12
+# ---
+
+# Fast scanning algorithm segments the image by scanning it once and comparing each
+# pixel to its upper and left neighbor. The algorithm starts from the first pixel
+# and assigns it to a new segment ``A_1``. Label count `lc` is assigned 1. Then it starts a column-wise traversal
+# of the image and for every pixel, it computes the difference measure `diff_fn`
+# between the pixel and its left neighbor, say ``\delta_{l}`` and between the pixel and
+# its top neighbor, say ``\delta_{t}``. Four cases arise:
+# 1) ``\delta_{l} \ge`` `threshold` and ``\delta_{t} <``   `threshold` : 
+#    We can say that the point has similar intensity to that its top neighbor.
+#    Hence, we assign the point to the segment that contains its top neighbor.
+# 2) ``\delta_{l} <``   `threshold` and ``\delta_{t} \ge`` `threshold` : 
+#    Similar to case 1, we assign the point to the segment that contains its left neighbor.
+# 3) ``\delta_{l} \ge`` `threshold` and ``\delta_{t} \ge`` `threshold` : 
+#    Point is significantly different from its top and left neighbors and is
+#    assigned a new label ``A_{\text{lc}+1}`` and `lc` is incremented.
+# 4) ``\delta_{l} <``   `threshold` and ``\delta_{t} <``   `threshold` : 
+#    In this case, we merge the top and left segments together and assign 
+#    the point under consideration to this merged segment.
+
+# This algorithm segments the image in just two passes (one for segmenting and other for
+# merging), hence it is very fast and can be used in real time applications.
+
+# **Time Complexity:** ``O(n)`` where ``n`` is the number of pixels
+
+using Images
+using ImageSegmentation, TestImages
+using IndirectArrays
+
+img = testimage("blobs")
+img = imresize(img, (128, 128));
+
+# Now we will segment the image using the fast scanning algorithm.
+
+segments = fast_scanning(img, 0.1)  # threshold = 0.1
+segments = prune_segments(segments, i->(segment_pixel_count(segments,i)<50), (i,j)->(-segment_pixel_count(segments,j)))
+labels = labels_map(segments)
+colored_labels = IndirectArray(labels, distinguishable_colors(maximum(labels)))
+masked_colored_labels = colored_labels .* (1 .- Gray.(img))
+mosaic(img, colored_labels, masked_colored_labels; nrow=1)
+
+# ## References:
+# - http://djj.ee.ntu.edu.tw/FastSegment.pdf
+save("assets/fastscanning.gif", cat(img, colored_labels, masked_colored_labels; dims=3); fps=1) #src