[GRASS-SVN] r55008 - grass/trunk/imagery/i.segment
svn_grass at osgeo.org
svn_grass at osgeo.org
Tue Feb 12 02:28:21 PST 2013
Author: martinl
Date: 2013-02-12 02:28:21 -0800 (Tue, 12 Feb 2013)
New Revision: 55008
Modified:
grass/trunk/imagery/i.segment/i.segment.xl.html
Log:
x.segment.xl: manual cosmetics (formatting issues)
Modified: grass/trunk/imagery/i.segment/i.segment.xl.html
===================================================================
--- grass/trunk/imagery/i.segment/i.segment.xl.html 2013-02-12 10:18:50 UTC (rev 55007)
+++ grass/trunk/imagery/i.segment/i.segment.xl.html 2013-02-12 10:28:21 UTC (rev 55008)
@@ -1,169 +1,195 @@
<h2>DESCRIPTION</h2>
-Image segmentation or object recognition is the process of grouping
-similar pixels into unique segments, also refered to as objects.
-Boundary and region based algorithms are described in the literature,
-currently a region growing and merging algorithm is implemented. Each
-object found during the segmentation process is given a unique ID and
-is a collection of contiguous pixels meeting some criteria. Note the
-contrast with image classification where all pixels similar to each
-other are assigned to the same class and do not need to be contiguous.
-The image segmentation results can be useful on their own, or used as a
-preprocessing step for image classification. The segmentation
-preprocessing step can reduce noise and speed up the classification.
-<H2>NOTES</h2>
+Image segmentation or object recognition is the process of grouping
+similar pixels into unique segments, also refered to as objects.
+Boundary and region based algorithms are described in the literature,
+currently a <em>region growing</em> and <em>merging algorithm</em> is
+implemented. Each object found during the segmentation process is
+given a unique ID and is a collection of contiguous pixels meeting
+some criteria. Note the contrast with image classification where all
+pixels similar to each other are assigned to the same class and do not
+need to be contiguous. The image segmentation results can be useful
+on their own, or used as a preprocessing step for image
+classification. The segmentation preprocessing step can reduce noise
+and speed up the classification.
+<h2>NOTES</h2>
+
<h3>Region Growing and Merging</h3>
-This segmentation algorithm sequentially examines all current
-segments in the map. The similarity between the current segment and
-each of its neighbors is calculated according to the given distance
-formula. Segments will be merged if they meet a number of criteria,
-including: 1. The pair is mutually most similar to each other (the
-similarity distance will be smaller than to any other neighbor), and
-2. The similarity must be lower than the input threshold. The process
-is repeated until no merges are made during a complete pass.
+This segmentation algorithm sequentially examines all current segments
+in the raster map. The similarity between the current segment and each
+of its neighbors is calculated according to the given distance
+formula. Segments will be merged if they meet a number of criteria,
+including:
+
+<ol>
+ <li>The pair is mutually most similar to each other (the similarity
+distance will be smaller than to any other neighbor), and</li>
+ <li>The similarity must be lower than the input threshold. The
+process is repeated until no merges are made during a complete pass.</li>
+</ol>
+
<h3>Similarity and Threshold</h3>
-The similarity between segments and unmerged objects is used to
-determine which objects are merged. Smaller distance values indicate a
+
+The similarity between segments and unmerged objects is used to
+determine which objects are merged. Smaller distance values indicate a
closer match, with a similarity score of zero for identical pixels.
<p>
-During normal processing, merges are only allowed when the
-similarity between two segments is lower than the givem
-threshold value. During the final pass, however, if a minimum
-segment size of 2 or larger is given with the <em>minsize</em>
-parameter, segments with a smaller pixel count will be merged with
-their most similar neighbor even if the similarity is greater than
-the threshold.
+During normal processing, merges are only allowed when the similarity
+between two segments is lower than the givem threshold value. During
+the final pass, however, if a minimum segment size of 2 or larger is
+given with the <b>minsize</b> parameter, segments with a smaller pixel
+count will be merged with their most similar neighbor even if the
+similarity is greater than the threshold.
<p>
-The threshold should be set by the user between 0 and 1.0. A threshold
-of 0 would allow only identical valued pixels to be merged, while a
-threshold of 1 would allow everything to be merged. Initial empirical
-tests indicate threshold values of 0.01 to 0.05 are reasonable values
+The threshold should be set by the user between 0 and 1.0. A threshold
+of 0 would allow only identical valued pixels to be merged, while a
+threshold of 1 would allow everything to be merged. Initial empirical
+tests indicate threshold values of 0.01 to 0.05 are reasonable values
to start.
<h4>Calculation Formulas</h4>
-Both Euclidean and Manhattan distances use the normal definition,
+
+Both Euclidean and Manhattan distances use the normal definition,
considering each raster in the image group as a dimension.
-In future , the distance calculation will also take into account the
+In future, the distance calculation will also take into account the
shape characteristics of the segments. The normal distances are then
-multiplied by the input radiometric weight. Next an additional
-contribution is added: (1-radioweight) * {smoothness * smoothness
-weight + compactness * (1-smoothness weight)}, where compactness =
-the Perimeter Length / sqrt( Area ) and smoothness = Perimeter
-Length / the Bounding Box. The perimeter length is estimated as the
-number of pixel sides the segment has.
+multiplied by the input radiometric weight. Next an additional
+contribution is added: <tt>(1-radioweight) * {smoothness * smoothness
+weight + compactness * (1-smoothness weight)}, where compactness = the
+Perimeter Length / sqrt( Area ) and smoothness = Perimeter Length /
+the Bounding Box</tt>. The perimeter length is estimated as the number
+of pixel sides the segment has.
<h3>Seeds</h3>
-The seeds map can be used to provide either seed pixels (random or
-selected points from which to start the segmentation process) or
-seed segments (results of previous segmentations or
-classifications). The different approaches are automatically
-detected by the program: any pixels that have identical seed values
-and are contiguous will be assigned a unique segment ID.
+
+The seeds map can be used to provide either seed pixels (random or
+selected points from which to start the segmentation process) or seed
+segments (results of previous segmentations or classifications). The
+different approaches are automatically detected by the program: any
+pixels that have identical seed values and are contiguous will be
+assigned a unique segment ID.
+
<p>
-It is expected that the <em>minsize</em> will be set to 1 if a seed
-map is used, but the program will allow other values to be used. If
-both options are used, the final iteration that ignores the
-threshold also will ignore the seed map and force merges for all
-pixels (not just segments that have grown/merged from the seeds).
+It is expected that the <b>minsize</b> will be set to 1 if a seed
+map is used, but the program will allow other values to be used. If
+both options are used, the final iteration that ignores the threshold
+also will ignore the seed map and force merges for all pixels (not
+just segments that have grown/merged from the seeds).
<h3>Maximum number of starting segments</h3>
-For the region growing algorithm without starting seeds, each pixel
-is sequentially numbered. The current limit with CELL storage is 2
-billion starting segment IDs. If the initial map has a larger
-number of non-null pixels, there are two workarounds:
-<p>
-1. Use starting seed pixels. (Maximum 2 billion pixels can be
-labeled with positive integers.)
-<p>
-2. Use starting seed segments. (By initial classification or other
-methods.)
+For the region growing algorithm without starting seeds, each pixel is
+sequentially numbered. The current limit with CELL storage is 2
+billion starting segment IDs. If the initial map has a larger number
+of non-null pixels, there are two workarounds:
+<ol>
+ <li>Use starting seed pixels. (Maximum 2 billion pixels can be
+labeled with positive integers.)</li>
+ <li>Use starting seed segments. (By initial classification or other
+methods.)</li>
+</ol>
+
<h3>Boundary Constraints</h3>
-Boundary constraints limit the adjacency of pixels and segments.
-Each unique value present in the <em>bounds</em> raster are
-considered as a MASK. Thus no segments in the final segmentated map
-will cross a boundary, even if their spectral data is very similar.
+Boundary constraints limit the adjacency of pixels and segments. Each
+unique value present in the <b>bounds</b> raster are considered as a
+MASK. Thus no segments in the final segmentated map will cross a
+boundary, even if their spectral data is very similar.
+
<h3>Minimum Segment Size</h3>
-To reduce the salt and pepper affect, a <em>minsize</em> greater
-than 1 will add one additional pass to the processing. During the
-final pass, the threshold is ignored for any segments smaller then
-the set size, thus forcing very small segments to merge with their
-most similar neighbor.
+To reduce the salt and pepper affect, a <b>minsize</b> greater than
+1 will add one additional pass to the processing. During the final
+pass, the threshold is ignored for any segments smaller then the set
+size, thus forcing very small segments to merge with their most
+similar neighbor.
+
<h2>EXAMPLES</h2>
+
This example uses the ortho photograph included in the NC Sample
-Dataset. Set up an imagery group:<br>
+Dataset. Set up an imagery group:
+
<div class="code"><pre>
i.group group=ortho_group input=ortho_2001_t792_1m at PERMANENT
</pre></div>
-<p>Because the segmentation process is computationally expensive,
-start with a small processing area to confirm if the segmentation
-results meet your requirements. Some manual adjustment of the
-threshold may be required. <br>
+<p>Because the segmentation process is computationally expensive,
+start with a small processing area to confirm if the segmentation
+results meet your requirements. Some manual adjustment of the
+threshold may be required.
<div class="code"><pre>
g.region rast=ortho_2001_t792_1m at PERMANENT
</pre></div>
-Try out a first threshold and check the results.<br>
+Try out a first threshold and check the results.
+
<div class="code"><pre>
i.segment -w group=ortho_group output=ortho_segs threshold=0.04 \
method=region_growing
</pre></div>
-<p></p>
-From a visual inspection, it seems this results in oversegmentation.
-Increasing the threshold: <br>
+
+<p>From a visual inspection, it seems this results in oversegmentation.
+Increasing the threshold:
+
<div class="code"><pre>
i.segment -w group=ortho_group output=ortho_segs \
threshold=0.1 method=region_growing
</pre></div>
-<p></p>
-This looks better. There is some noise in the image, lets next force
+
+<p>This looks better. There is some noise in the image, lets next force
all segments smaller than 5 pixels to be merged into their most similar
neighbor (even if they are less similar then required by our
-threshold):<br>
+threshold):
+
<div class="code"><pre>
i.segment -w --overwrite group=ortho_group output=ortho_segs \
threshold=0.1 method=region_growing minsize=5
</pre></div>
-<p></p>
-Processing the entire ortho image with nearly 10 million pixels took about
-15 minutes.
+<p>Processing the entire ortho image with nearly 10 million pixels
+took about 15 minutes.
+
<h2>TODO</h2>
+
<h3>Functionality</h3>
+
<ul>
<li>Further testing of the shape characteristics (smoothness,
compactness), if it looks good it should be added.
<b>in progress</b></li>
<li>Malahanobis distance for the similarity calculation.</li>
</ul>
+
<h3>Use of Segmentation Results</h3>
+
<ul>
<li>Improve the optional output from this module, or better yet, add a
module for <em>i.segment.metrics</em>.</li>
-<li>Providing updates to i.maxlik to ensure the segmentation output can
-be used as input for the existing classification functionality.</li>
-<li>Integration/workflow for <em>r.fuzzy</em>.</li>
+<li>Providing updates to <em><a href="i.maxlik.html">i.maxlik</a></em>
+to ensure the segmentation output can be used as input for the
+existing classification functionality.</li>
+<li>Integration/workflow for <em>r.fuzzy</em> (Addon).</li>
</ul>
+
<h3>Speed</h3>
+
<ul>
<li>See create_isegs.c</li>
</ul>
+
<H2>REFERENCES</h2>
+
This project was first developed during GSoC 2012. Project documentation,
Image Segmentation references, and other information is at the
<a href="http://grass.osgeo.org/wiki/GRASS_GSoC_2012_Image_Segmentation">project wiki</a>.
-<p>
-Information about
+
+<p>Information about
<a href="http://grass.osgeo.org/wiki/Image_classification">classification in GRASS</a>
is at available on the wiki.
-</p>
<h2>SEE ALSO</h2>
<em>
@@ -178,4 +204,3 @@
Eric Momsen - North Dakota State University (Google Summer of Code 2012, mentor: Markus Metz)<br>
Various improvements by Markus Metz
-
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