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Commit bb8a903e authored by Davis King's avatar Davis King
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updated the docs

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docs/docs/imaging.xml
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This object is a tool for detecting the positions of objects in
an image. In particular, it is a simple container to aggregate
an instance of the <a href="#scan_image_pyramid">scan_image_pyramid</a>
class, the weight vector needed by scan_image_pyramid, and finally
class, the weight vector needed by scan_image_pyramid, and
an instance of <a href="#test_box_overlap">test_box_overlap</a>. The test_box_overlap
object is used to perform non-max suppression on the output of the
scan_image_pyramid object.
<p>
Note that you can use the
<a href="ml.html#structural_object_detection_trainer">structural_object_detection_trainer</a>
to learn the parameters of an object_detector. See the example programs for an introduction.
</p>
</description>
<examples>
<example>object_detector_ex.cpp.html</example>
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docs/docs/index.xml
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</ul>
</li>
<li><b>Bayesian Network Inference Algorithms</b>
<li><b>Graphical Model Inference Algorithms</b>
<ul>
<li><a href="bayes.html#bayesian_network_join_tree">join tree</a> algorithm for exact inference</li>
<li><a href="bayes.html#bayesian_network_gibbs_sampler">gibbs sampler</a> markov chain monte carlo algorithm</li>
<li><a href="bayes.html#bayesian_network_join_tree">Join tree</a> algorithm for exact inference in
a Bayesian network.</li>
<li><a href="bayes.html#bayesian_network_gibbs_sampler">Gibbs sampler</a> markov chain monte
carlo algorithm for approximate inference in a Bayesian network.</li>
<li>Routines for performing MAP inference in
<a href="optimization.html#find_max_factor_graph_viterbi">chain-structured</a> or
<a href="optimization.html#find_max_factor_graph_nmplp">general</a> factor graphs.</li>
</ul>
</li>
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<li>Common image operations such as edge finding and morphological operations</li>
<li>Implementations of the <a href="imaging.html#get_surf_points">SURF</a>
and <a href="imaging.html#hog_image">HOG</a> feature extraction algorithms.</li>
<li>Tools for <a href="imaging.html#object_detector">detecting objects</a> in images.</li>
</ul>
</li>
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docs/docs/release_notes.xml
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<?xml version="1.0" encoding="ISO-8859-1"?>
<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="stylesheet.xsl"?>
<doc>
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<current>
New Stuff:
- Added a file_exists() function.
- Added separable_3x3_filter_block_grayscale
- Added separable_3x3_filter_block_rgb
- Added pyramid_down_5_4, pyramid_down_4_3, and pyramid_down_3_2
- Added fill_rect() for images.
- Two new routines for performing MAP inference in factor graphs:
- For chain-structured graphs: find_max_factor_graph_viterbi()
- For general graphs: find_max_factor_graph_nmplp()
- Image Processing
- Added more tools for creating image pyramids. See pyramid_down_5_4,
pyramid_down_4_3, and pyramid_down_3_2.
- Added more image filtering functions.
- Added a set of tools for creating sliding window classifiers:
- Added the scan_image() routine. It is a tool for sliding a set of
rectangles over an image space and finding the locations where the sum
of pixels in the rectangles exceeds a threshold. Also added
scan_image_pyramid, which is a tool for running scan_image() over an
image pyramid.
- Added the structural_object_detection_trainer. This is a tool which
formulates the sliding window classifier learning problem as an
instance of structural SVM learning.
- Added a variety of supporting tools and two object detection example
programs.
- Added the following functions for computing statistics on vectors:
mean_sign_agreement(), correlation(), covariance(), r_squared(),
and mean_squared_error()
- Added the find_max_factor_graph_nmplp() function for performing approximate
MAP inference.
- Added the scan_image() routine. It is a tool for sliding a set of rectangles
over an image space and finding the locations where the sum of pixels in
the rectangles exceeds a threshold.
- Added the hashed_feature_image object.
- Added the scan_image_pyramid object.
- Added the object_detector object.
- Added the structural_svm_object_detection_problem object.
- Added spatially_filter_image_separable()
- Added structural_object_detection_trainer
- Added the cross_validate_object_detection_trainer() and
test_object_detection_function() routines.
- Added the find_max_factor_graph_viterbi() routine for performing MAP
inference in chain-structured factor graphs.
Non-Backwards Compatible Changes:
- Changed the interface to the ridge regression trainer objects so that
they report the entire set of LOO prediction values rather than a
summary statistic like mean squared error.
- Changed the serialization routine for bgr_pixels to store the pixels
in BGR order rather than RGB.
- Changed the interface for the spatially_filter_image() routines to take the filter
as a matrix rather than C-array. I also fixed a bug which showed up when using
non-square filters. The bug would cause the edges of the output image to be incorrect.
Changed the behavior of spatially_filter_image(). Now it won't truncate signed
pixel values to 0 if they go negative.
- Changed the interface to the ridge regression trainer objects so that they
report the entire set of LOO prediction values rather than a summary statistic
like mean squared error.
- Changed the serialization routine for bgr_pixels to store the pixels in BGR
order rather than RGB.
- Changed the interface for the spatially_filter_image() routine to take the
filter as a matrix rather than C-array. Also, now it won't force signed pixel
values to 0 if they go negative.
- Changed the test_regression_function() and cross_validate_regression_trainer()
routines so they return both the MSE and R-squared values rather than just the
MSE.
......@@ -59,18 +55,20 @@ Bug fixes:
to be part of a rectangle are drawn as being inside the overlay rectangle.
- Fixed a bug pointed out by Martin Müllenhaupt which caused the windows socket
code to not compile when used with the mingw-cross-env project.
- Fixed a bug in the png_loader. If you loaded an image with an
alpha channel into something without an alpha channel there were
uninitialized values being alpha blended into the image.
- Fixed a bug in the cpp_tokenizer that only shows up on newer versions of
gcc. It wasn't tokenizing double quoted strings right.
Other:
- Added a more complete set of functions for converting between image space
and the downsampled hog grid. So now you can convert from image to hog
instead of just hog to image.
- Made the integral_image more general by making it templated on the
type of scalar used to store the sums.
- Fixed a bug in the png_loader. If you loaded an image with an alpha channel
into something without an alpha channel there were uninitialized values being
alpha blended into the image.
- Fixed a bug in the cpp_tokenizer that only shows up on newer versions of gcc.
It wasn't tokenizing double quoted strings right.
- Fixed a bug in spatially_filter_image() which showed up when using non-square
filters. The bug would cause the edges of the output image to be incorrect.
Other:
- Added a more complete set of methods for converting between image space and
the downsampled hog grid used by hog_image. Now you can convert from image
to hog in addition to hog to image.
- Made the integral_image more general by making it templated on the type of
scalar used to store the sums.
</current>
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