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+<h2>DESCRIPTION</h2>
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+Image segmentation or object recognition is the process of grouping
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+similar pixels into unique segments, also refered to as objects.
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+Boundary and region based algorithms are described in the literature,
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+currently a region growing and merging algorithm is implemented. Each
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+object found during the segmentation process is given a unique ID and
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+is a collection of contiguous pixels meeting some criteria. Note the
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+contrast with image classification where all pixels similar to each
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+other are assigned to the same class and do not need to be contiguous.
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+The image segmentation results can be useful on their own, or used as a
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+preprocessing step for image classification. The segmentation
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+preprocessing step can reduce noise and speed up the classification.
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+
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+<H2>NOTES</h2>
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+
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+<h3>Region Growing and Merging</h3>
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+This segmentation algorithm sequentially examines all current
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+segments in the map. The similarity between the current segment and
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+each of its neighbors is calculated according to the given distance
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+formula. Segments will be merged if they meet a number of criteria,
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+including: 1. The pair is mutually most similar to each other (the
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+similarity distance will be smaller than to any other neighbor), and
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+2. The similarity must be lower than the input threshold. The process
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+is repeated until no merges are made during a complete pass.
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+
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+<h3>Similarity and Threshold</h3>
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+The similarity between segments and unmerged objects is used to
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+determine which objects are merged. Smaller distance values indicate a
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+closer match, with a similarity score of zero for identical pixels.
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+<p>
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+During normal processing, merges are only allowed when the
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+similarity between two segments is lower than the givem
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+threshold value. During the final pass, however, if a minimum
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+segment size of 2 or larger is given with the <em>minsize</em>
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+parameter, segments with a smaller pixel count will be merged with
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+their most similar neighbor even if the similarity is greater than
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+the threshold.
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+<p>
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+The threshold must be larger than 0.0 and smaller than 1.0. A threshold
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+of 0 would allow only identical valued pixels to be merged, while a
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+threshold of 1 would allow everything to be merged. Initial empirical
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+tests indicate threshold values of 0.01 to 0.05 are reasonable values
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+to start. It is recommended to start with a low value, e.g. 0.01, and
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+then perform hierachical segmentation by using the output of the last
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+run as seeds for the next run.
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+
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+<h4>Calculation Formulas</h4>
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+Both Euclidean and Manhattan distances use the normal definition,
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+considering each raster in the image group as a dimension.
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+
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+In future , the distance calculation will also take into account the
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+shape characteristics of the segments. The normal distances are then
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+multiplied by the input radiometric weight. Next an additional
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+contribution is added: (1-radioweight) * {smoothness * smoothness
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+weight + compactness * (1-smoothness weight)}, where compactness =
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+the Perimeter Length / sqrt( Area ) and smoothness = Perimeter
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+Length / the Bounding Box. The perimeter length is estimated as the
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+number of pixel sides the segment has.
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+
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+<h3>Seeds</h3>
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+The seeds map can be used to provide either seed pixels (random or
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+selected points from which to start the segmentation process) or
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+seed segments (results of previous segmentations or
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+classifications). The different approaches are automatically
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+detected by the program: any pixels that have identical seed values
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+and are contiguous will be assigned a unique segment ID.
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+<p>
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+It is expected that the <em>minsize</em> will be set to 1 if a seed
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+map is used, but the program will allow other values to be used. If
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+both options are used, the final iteration that ignores the
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+threshold also will ignore the seed map and force merges for all
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+pixels (not just segments that have grown/merged from the seeds).
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+
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+<h3>Maximum number of starting segments</h3>
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+For the region growing algorithm without starting seeds, each pixel
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+is sequentially numbered. The current limit with CELL storage is 2
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+billion starting segment IDs. If the initial map has a larger
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+number of non-null pixels, there are two workarounds:
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+<p>
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+1. Use starting seed pixels. (Maximum 2 billion pixels can be
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+labeled with positive integers.)
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+<p>
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+2. Use starting seed segments. (By initial classification or other
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+methods.)
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+
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+<h3>Boundary Constraints</h3>
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+Boundary constraints limit the adjacency of pixels and segments.
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+Each unique value present in the <em>bounds</em> raster are
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+considered as a MASK. Thus no segments in the final segmentated map
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+will cross a boundary, even if their spectral data is very similar.
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+
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+<h3>Minimum Segment Size</h3>
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+To reduce the salt and pepper affect, a <em>minsize</em> greater
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+than 1 will add one additional pass to the processing. During the
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+final pass, the threshold is ignored for any segments smaller then
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+the set size, thus forcing very small segments to merge with their
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+most similar neighbor.
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+
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+<h2>EXAMPLES</h2>
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+This example uses the ortho photograph included in the NC Sample
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+Dataset. Set up an imagery group:<br>
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+<div class="code"><pre>
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+i.group group=ortho_group input=ortho_2001_t792_1m@PERMANENT
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+</pre></div>
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+
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+<p>Set the region to a smaller test region. <br>
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+
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+<div class="code"><pre>
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+g.region -p n=220446 s=220075 e=639151 w=638592
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+</pre></div>
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+
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+Try out a low threshold and check the results.<br>
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+<div class="code"><pre>
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+i.segment group=ortho_group output=ortho_segs_l1 threshold=0.02
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+</pre></div>
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+<p></p>
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+From a visual inspection, it seems this results in too many segments.
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+Increasing the threshold, using the previous results as seeds,
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+and setting a minimum size of 2: <br>
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+<div class="code"><pre>
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+i.segment group=ortho_group output=ortho_segs_l2 threshold=0.05 \
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+ seeds=ortho_segs_l1 min=2
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+
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+i.segment group=ortho_group output=ortho_segs_l3 threshold=0.1 \
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+ seeds=ortho_segs_l2
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+
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+i.segment group=ortho_group output=ortho_segs_l4 threshold=0.2 \
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+ seeds=ortho_segs_l3
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+
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+i.segment group=ortho_group output=ortho_segs_l5 threshold=0.3 \
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+ seeds=ortho_segs_l4
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+</pre></div>
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+<p>
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+The output ortho_segs_l4 with threshold=0.2 looks best. There is some
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+noise in the image, lets next force all segments smaller than 5 pixels
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+to be merged into their most similar neighbor (even if they are less
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+similar then required by our threshold):<br>
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+
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+<p>Set the region to match the entire map(s) in the group. <br>
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+<div class="code"><pre>
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+g.region -p rast=ortho_2001_t792_1m@PERMANENT
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+</pre></div>
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+
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+Run i.segment on the full map:
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+
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+<div class="code"><pre>
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+i.segment group=ortho_group output=ortho_segs_final \
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+ threshold=0.2 min=5
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+</pre></div>
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+<p></p>
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+Processing the entire ortho image with nearly 10 million pixels took about
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+15 minutes for the first run.
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+
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+<h2>TODO</h2>
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+<h3>Functionality</h3>
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+<ul>
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+<li>Further testing of the shape characteristics (smoothness,
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+compactness), if it looks good it should be added.
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+<b>in progress</b></li>
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+<li>Malahanobis distance for the similarity calculation.</li>
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+</ul>
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+<h3>Use of Segmentation Results</h3>
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+<ul>
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+<li>Improve the optional output from this module, or better yet, add a
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+module for <em>i.segment.metrics</em>.</li>
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+<li>Providing updates to i.maxlik to ensure the segmentation output can
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+be used as input for the existing classification functionality.</li>
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+<li>Integration/workflow for <em>r.fuzzy</em>.</li>
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+</ul>
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+<h3>Speed</h3>
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+<ul>
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+<li>See create_isegs.c</li>
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+</ul>
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+<H2>REFERENCES</h2>
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+This project was first developed during GSoC 2012. Project documentation,
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+Image Segmentation references, and other information is at the
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+<a href="http://grass.osgeo.org/wiki/GRASS_GSoC_2012_Image_Segmentation">project wiki</a>.
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+<p>
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+Information about
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+<a href="http://grass.osgeo.org/wiki/Image_classification">classification in GRASS</a>
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+is at available on the wiki.
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+</p>
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+<h2>SEE ALSO</h2>
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+<em>
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+<a href="i.group.html">i.group</a>,
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+<a href="i.maxlik.html">i.maxlik</a>,
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+<a href="r.fuzzy">r.fuzzy</a>,
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+<a href="i.smap.html">i.smap</a>,
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+<a href="r.seg.html">r.seg</a> (Addon)
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+</em>
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+
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+<h2>AUTHORS</h2>
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+Eric Momsen - North Dakota State University<br>
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+Markus Metz (GSoC Mentor)
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+
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Markus Metz