updated the docs

docs/docs/imaging.xml

@@ -1162,10 +1162,15 @@
                 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>

docs/docs/index.xml

@@ -144,10 +144,15 @@
                </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>
 
@@ -158,6 +163,7 @@
                   <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>
 

docs/docs/release_notes.xml

-<?xml version="1.0" encoding="ISO-8859-1"?>
+<?xml version="1.0" encoding="UTF-8"?>
 <?xml-stylesheet type="text/xsl" href="stylesheet.xsl"?>
 
 <doc>
@@ -12,41 +12,37 @@
 
 <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>