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/xx_applications/plot_coins_segmentation.py

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+"""
+===============================================================
+Comparing edge-based segmentation and region-based segmentation
+===============================================================
+
+In this example, we will see how to segment objects from a background. We use
+the ``coins`` image from ``skimage.data``, which shows several coins outlined
+against a darker background.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data
+
+coins = data.coins()
+hist = np.histogram(coins, bins=np.arange(0, 256))
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
+ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
+ax1.axis('off')
+ax2.plot(hist[1][:-1], hist[0], lw=2)
+ax2.set_title('histogram of grey values')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Thresholding
+============
+
+A simple way to segment the coins is to choose a threshold based on the
+histogram of grey values. Unfortunately, thresholding this image gives a binary
+image that either misses significant parts of the coins or merges parts of the
+background with the coins:
+
+"""
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
+ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
+ax1.set_title('coins > 100')
+ax1.axis('off')
+ax1.set_adjustable('box-forced')
+ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
+ax2.set_title('coins > 150')
+ax2.axis('off')
+ax2.set_adjustable('box-forced')
+margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
+fig.subplots_adjust(**margins)
+
+"""
+.. image:: PLOT2RST.current_figure
+
+
+Edge-based segmentation
+=======================
+
+Next, we try to delineate the contours of the coins using edge-based
+segmentation. To do this, we first get the edges of features using the Canny
+edge-detector.
+"""
+
+from skimage.feature import canny
+edges = canny(coins/255.)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
+ax.axis('off')
+ax.set_title('Canny detector')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+These contours are then filled using mathematical morphology.
+"""
+
+from scipy import ndimage as ndi
+
+fill_coins = ndi.binary_fill_holes(edges)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
+ax.axis('off')
+ax.set_title('Filling the holes')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Small spurious objects are easily removed by setting a minimum size for valid
+objects.
+"""
+from skimage import morphology
+coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
+ax.axis('off')
+ax.set_title('Removing small objects')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+However, this method is not very robust, since contours that are not perfectly
+closed are not filled correctly, as is the case for one unfilled coin above.
+
+
+Region-based segmentation
+=========================
+
+We therefore try a region-based method using the watershed transform. First, we
+find an elevation map using the Sobel gradient of the image.
+
+"""
+
+from skimage.filters import sobel
+
+elevation_map = sobel(coins)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
+ax.axis('off')
+ax.set_title('elevation_map')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Next we find markers of the background and the coins based on the extreme parts
+of the histogram of grey values.
+"""
+
+markers = np.zeros_like(coins)
+markers[coins < 30] = 1
+markers[coins > 150] = 2
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
+ax.axis('off')
+ax.set_title('markers')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+Finally, we use the watershed transform to fill regions of the elevation map
+starting from the markers determined above:
+
+"""
+segmentation = morphology.watershed(elevation_map, markers)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
+ax.axis('off')
+ax.set_title('segmentation')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+This last method works even better, and the coins can be segmented and labeled
+individually.
+
+"""
+
+from skimage.color import label2rgb
+
+segmentation = ndi.binary_fill_holes(segmentation - 1)
+labeled_coins, _ = ndi.label(segmentation)
+image_label_overlay = label2rgb(labeled_coins, image=coins)
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
+ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
+ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
+ax1.axis('off')
+ax1.set_adjustable('box-forced')
+ax2.imshow(image_label_overlay, interpolation='nearest')
+ax2.axis('off')
+ax2.set_adjustable('box-forced')
+
+fig.subplots_adjust(**margins)
+
+"""
+.. image:: PLOT2RST.current_figure
+
+"""
+
+plt.show()