[GRASS-SVN] r55034 - grass/trunk/imagery/i.segment
svn_grass at osgeo.org
svn_grass at osgeo.org
Wed Feb 13 03:09:31 PST 2013
Author: mmetz
Date: 2013-02-13 03:09:31 -0800 (Wed, 13 Feb 2013)
New Revision: 55034
Removed:
grass/trunk/imagery/i.segment/i.segment.xl.html
Modified:
grass/trunk/imagery/i.segment/i.segment.html
grass/trunk/imagery/i.segment/parse_args.c
Log:
i.segment manual and option cosmetics
Modified: grass/trunk/imagery/i.segment/i.segment.html
===================================================================
--- grass/trunk/imagery/i.segment/i.segment.html 2013-02-13 10:57:30 UTC (rev 55033)
+++ grass/trunk/imagery/i.segment/i.segment.html 2013-02-13 11:09:31 UTC (rev 55034)
@@ -11,27 +11,32 @@
preprocessing step for image classification. The segmentation
preprocessing step can reduce noise and speed up the classification.
-<H2>NOTES</h2>
+<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
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
+similarity between two segments is lower than the given
threshold value. During the final pass, however, if a minimum
-segment size of 2 or larger is given with the <em>minsize</em>
+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.
@@ -48,13 +53,13 @@
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
+Perimeter Length / sqrt( Area ) and smoothness = Perimeter
+Length / Bounding Box. The perimeter length is estimated as the
number of pixel sides the segment has.
<h3>Seeds</h3>
@@ -66,32 +71,33 @@
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
+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
+threshold will also 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
+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
+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
@@ -99,25 +105,25 @@
<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>Set the region to a smaller test region. <br>
+<p>Set the region to a smaller test region.
<div class="code"><pre>
g.region -p n=220446 s=220075 e=639151 w=638592
</pre></div>
-Try out a low threshold and check the results.<br>
+Try out a low threshold and check the results.
<div class="code"><pre>
i.segment group=ortho_group output=ortho_segs_l1 threshold=0.02
</pre></div>
<p></p>
From a visual inspection, it seems this results in too many segments.
Increasing the threshold, using the previous results as seeds,
-and setting a minimum size of 2: <br>
+and setting a minimum size of 2:
<div class="code"><pre>
i.segment group=ortho_group output=ortho_segs_l2 threshold=0.05 \
seeds=ortho_segs_l1 min=2
@@ -137,20 +143,21 @@
value of 0.25 seems to be a good choice. There is also some noise in
the image, lets next force all segments smaller than 10 pixels to be
merged into their most similar neighbor (even if they are less similar
-than required by our threshold):<br>
+than required by our threshold):
-<p>Set the region to match the entire map(s) in the group. <br>
+<p>Set the region to match the entire map(s) in the group.
<div class="code"><pre>
g.region -p rast=ortho_2001_t792_1m at PERMANENT
</pre></div>
+<p>
Run i.segment on the full map:
<div class="code"><pre>
i.segment group=ortho_group output=ortho_segs_final \
threshold=0.25 min=10
</pre></div>
-<p></p>
+<p>
Processing the entire ortho image with nearly 10 million pixels took
about 20 minutes for the final run.
@@ -166,15 +173,18 @@
<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>
+
+<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>.
@@ -182,7 +192,7 @@
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>
<a href="i.group.html">i.group</a>,
Deleted: grass/trunk/imagery/i.segment/i.segment.xl.html
===================================================================
--- grass/trunk/imagery/i.segment/i.segment.xl.html 2013-02-13 10:57:30 UTC (rev 55033)
+++ grass/trunk/imagery/i.segment/i.segment.xl.html 2013-02-13 11:09:31 UTC (rev 55034)
@@ -1,206 +0,0 @@
-<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 <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 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
-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 <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
-to start.
-
-<h4>Calculation Formulas</h4>
-
-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
-shape characteristics of the segments. The normal distances are then
-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.
-
-<p>
-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:
-<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 <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 <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:
-
-<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.
-
-<div class="code"><pre>
-g.region rast=ortho_2001_t792_1m at PERMANENT
-</pre></div>
-
-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>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>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):
-
-<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>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 <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
-<a href="http://grass.osgeo.org/wiki/Image_classification">classification in GRASS</a>
-is at available on the wiki.
-
-<h2>SEE ALSO</h2>
-<em>
-<a href="i.group.html">i.group</a>,
-<a href="i.maxlik.html">i.maxlik</a>,
-<a href="r.fuzzy">r.fuzzy</a>,
-<a href="i.smap.html">i.smap</a>,
-<a href="r.seg.html">r.seg</a> (Addon)
-</em>
-
-<h2>AUTHORS</h2>
-
-Eric Momsen - North Dakota State University (Google Summer of Code 2012, mentor: Markus Metz)<br>
-Various improvements by Markus Metz
Modified: grass/trunk/imagery/i.segment/parse_args.c
===================================================================
--- grass/trunk/imagery/i.segment/parse_args.c 2013-02-13 10:57:30 UTC (rev 55033)
+++ grass/trunk/imagery/i.segment/parse_args.c 2013-02-13 11:09:31 UTC (rev 55034)
@@ -51,7 +51,7 @@
similarity->guisection = _("Settings");
min_segment_size = G_define_option();
- min_segment_size->key = "min";
+ min_segment_size->key = "minsize";
min_segment_size->type = TYPE_INTEGER;
min_segment_size->required = NO;
min_segment_size->answer = "1";
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