Added sections to gallery of examples

Modified travis_script.sh to account for the new structure of the gallery

Added README.txt files in directories of gallery examples

Fixed references to gallery images in user guide pages

Fixed broken links

doc/examples/color_exposure/README.txt

@@ -0,0 +1,2 @@
+Manipulating exposure and color channels
+----------------------------------------

doc/examples/edges/README.txt

@@ -0,0 +1,2 @@
+Edges and lines
+---------------

doc/examples/features_detection/README.txt

@@ -0,0 +1,2 @@
+Detection of features and objects
+---------------------------------

doc/examples/filters/README.txt

@@ -0,0 +1,2 @@
+Filtering and restoration
+-------------------------

doc/examples/numpy_operations/README.txt

@@ -0,0 +1,2 @@
+Operations on NumPy arrays
+--------------------------

doc/examples/segmentation/README.txt

@@ -0,0 +1,2 @@
+Segmentation of objects
+-----------------------

doc/examples/transform/README.txt

@@ -0,0 +1,2 @@
+Geometrical transformations and registration
+--------------------------------------------

doc/source/user_guide/numpy_images.txt

@@ -81,9 +81,9 @@ disk: ::
     ...                    (nrows / 2)**2)
     >>> camera[outer_disk_mask] = 0
 
-.. image:: ../auto_examples/images/plot_camera_numpy_1.png
+.. image:: ../auto_examples/numpy_operations/images/plot_camera_numpy_1.png
     :width: 45%
-    :target: ../auto_examples/plot_camera_numpy.html
+    :target: ../auto_examples/numpy_operations/plot_camera_numpy.html
 
 Boolean arithmetic can be used to define more complex masks: ::
 

doc/source/user_guide/transforming_image_data.txt

@@ -78,8 +78,8 @@ using an array of labels to encode the regions to be represented with the
 same color. 
 
 
-.. image: ../auto_examples/images/plot_join_segmentations_1.png
-   :target: ../auto_examples/plot_join_segmentations.html
+.. image: ../auto_examples/segmentation/images/plot_join_segmentations_1.png
+   :target: ../auto_examples/segmentation/plot_join_segmentations.html
    :align: center
    :width: 80%
 
@@ -87,9 +87,9 @@ same color.
 
 .. topic:: Examples:
 
-  * :ref:`example_plot_tinting_grayscale_images.py`
-  * :ref:`example_plot_join_segmentations.py`
-  * :ref:`example_plot_rag_mean_color.py`
+  * :ref:`example_color_exposure_plot_tinting_grayscale_images.py`
+  * :ref:`example_segmentation_plot_join_segmentations.py`
+  * :ref:`example_segmentation_plot_rag_mean_color.py`
 
 
 Contrast and exposure
@@ -157,16 +157,16 @@ details are enhanced in large regions with poor contrast. As a further
 refinement, histogram equalization can be performed in subregions of the
 image with :func:`equalize_adapthist`, in order to correct for exposure
 gradients across the image. See the example
-:ref:`example_plot_equalize.py`.
+:ref:`example_color_exposure_plot_equalize.py`.
 
-.. image:: ../auto_examples/images/plot_equalize_1.png
-   :target: ../auto_examples/plot_equalize.html
+.. image:: ../auto_examples/color_exposure/images/plot_equalize_1.png
+   :target: ../auto_examples/color_exposure/plot_equalize.html
    :align: center
    :width: 90%
 
 
 .. topic:: Examples:
 
-  * :ref:`example_plot_equalize.py`
+  * :ref:`example_color_exposure_plot_equalize.py`
 
 

doc/source/user_guide/tutorial_segmentation.txt

@@ -11,8 +11,8 @@ the coins cannot be done directly from the histogram of grey values,
 because the background shares enough grey levels with the coins that a
 thresholding segmentation is not sufficient.
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_1.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_1.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 ::
@@ -26,8 +26,8 @@ Simply thresholding the image leads either to missing significant parts
 of the coins, or to merging parts of the background with the
 coins. This is due to the inhomogeneous lighting of the image. 
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_2.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_2.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 A first idea is to take advantage of the local contrast, that is, to
@@ -53,8 +53,8 @@ boundary of the coins, or inside the coins.
     >>> from scipy import ndimage as ndi
     >>> fill_coins = ndi.binary_fill_holes(edges)
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_3.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_3.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 Now that we have contours that delineate the outer boundary of the coins,
@@ -62,8 +62,8 @@ we fill the inner part of the coins using the
 ``ndi.binary_fill_holes`` function, which uses mathematical morphology
 to fill the holes.
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_4.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_4.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 Most coins are well segmented out of the background. Small objects from
@@ -83,8 +83,8 @@ has not been segmented correctly at all. The reason is that the contour
 that we got from the Canny detector was not completely closed, therefore
 the filling function did not fill the inner part of the coin. 
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_5.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_5.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 Therefore, this segmentation method is not very robust: if we miss a
@@ -128,8 +128,8 @@ separate the coins from the background.
 
 and here is the corresponding 2-D plot:
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_6.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_6.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 The next step is to find markers of the background and the coins based on the
@@ -139,8 +139,8 @@ extreme parts of the histogram of grey values::
     >>> markers[coins < 30] = 1
     >>> markers[coins > 150] = 2
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_7.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_7.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 Let us now compute the watershed transform::
@@ -148,8 +148,8 @@ Let us now compute the watershed transform::
     >>> from skimage.morphology import watershed
     >>> segmentation = watershed(elevation_map, markers)
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_8.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_8.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center
 
 With this method, the result is satisfying for all coins. Even if the
@@ -165,7 +165,7 @@ We can now label all the coins one by one using ``ndi.label``::
 
     >>> labeled_coins, _ = ndi.label(segmentation)
 
-.. image:: ../auto_examples/applications/images/plot_coins_segmentation_9.png
-   :target: ../auto_examples/applications/plot_coins_segmentation.html
+.. image:: ../auto_examples/xx_applications/images/plot_coins_segmentation_9.png
+   :target: ../auto_examples/xx_applications/plot_coins_segmentation.html
    :align: center