diff --git a/.travis.yml b/.travis.yml
index 69534244..a47dae84 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -26,11 +26,13 @@ install:
     - sudo $PYTHON setup.py install
 script:
     # Change into an innocuous directory and find tests from installation
+    - mkdir $HOME/.matplotlib
+    - "echo 'backend : Agg' > $HOME/.matplotlib/matplotlibrc"
+    - "echo 'backend.qt4 : PyQt4' >> $HOME/.matplotlib/matplotlibrc"
     - mkdir for_test
     - cd for_test
     - nosetests-$PYVER --exe -v --cover-package=skimage skimage
     # Change back to repository root directory and run all doc examples
     - cd ..
-    - "echo 'backend : Agg' > matplotlibrc"
-    - for f in doc/examples/*.py; do $PYTHON "$f"; if [ $? -ne 0 ]; then exit $?; fi done
+    - for f in doc/examples/*.py; do $PYTHON "$f"; if [ $? -ne 0 ]; then exit 1; fi done
     - flake8 --exit-zero skimage doc/examples viewer_examples
diff --git a/doc/examples/plot_matching.py b/doc/examples/plot_matching.py
index 43833d7c..bae6d6e2 100644
--- a/doc/examples/plot_matching.py
+++ b/doc/examples/plot_matching.py
@@ -20,10 +20,13 @@ sufficient. Therefore, the RANSAC algorithm is used on top of the normal model
 to robustly estimate the parameter set by detecting outliers.
 
 """
+from __future__ import print_function
+
 import numpy as np
 from matplotlib import pyplot as plt
 
 from skimage import data
+from skimage.util import img_as_float
 from skimage.feature import corner_harris, corner_subpix, corner_peaks
 from skimage.transform import warp, AffineTransform
 from skimage.exposure import rescale_intensity
@@ -32,10 +35,11 @@ from skimage.measure import ransac
 
 
 # generate synthetic checkerboard image and add gradient for the later matching
-checkerboard = data.checkerboard()
+checkerboard = img_as_float(data.checkerboard())
 img_orig = np.zeros(list(checkerboard.shape) + [3])
 img_orig[..., 0] = checkerboard
-gradient_r, gradient_c = np.mgrid[0:img_orig.shape[0], 0:img_orig.shape[1]]
+gradient_r, gradient_c = np.mgrid[0:img_orig.shape[0],
+                                  0:img_orig.shape[1]] / float(img_orig.shape[0])
 img_orig[..., 1] = gradient_r
 img_orig[..., 2] = gradient_c
 img_orig = rescale_intensity(img_orig)
@@ -53,9 +57,9 @@ coords_warped = corner_peaks(corner_harris(img_warped_gray),
                              threshold_rel=0.001, min_distance=5)
 
 # determine sub-pixel corner position
-coords_orig_subpix = corner_subpix(img_orig_gray, coords_orig, window_size=10)
+coords_orig_subpix = corner_subpix(img_orig_gray, coords_orig, window_size=9)
 coords_warped_subpix = corner_subpix(img_warped_gray, coords_warped,
-                                     window_size=10)
+                                     window_size=9)
 
 
 def gaussian_weights(window_ext, sigma=1):
@@ -109,9 +113,9 @@ outliers = inliers == False
 
 
 # compare "true" and estimated transform parameters
-print tform.scale, tform.translation, tform.rotation
-print model.scale, model.translation, model.rotation
-print model_robust.scale, model_robust.translation, model_robust.rotation
+print(tform.scale, tform.translation, tform.rotation)
+print(model.scale, model.translation, model.rotation)
+print(model_robust.scale, model_robust.translation, model_robust.rotation)
 
 
 # visualize correspondences
diff --git a/skimage/viewer/qt/__init__.py b/skimage/viewer/qt/__init__.py
index 55cafcfe..8e7ab939 100644
--- a/skimage/viewer/qt/__init__.py
+++ b/skimage/viewer/qt/__init__.py
@@ -12,8 +12,11 @@ if qt_api is None:
             import PyQt4
             qt_api = 'pyqt'
         except ImportError:
-            qt_api = 'none'
+            qt_api = None
             # Note that we don't want to raise an error because that would
             # cause the TravisCI build to fail.
             warnings.warn("Could not import PyQt4: ImageViewer not available!")
+
+
+if qt_api is not None:
     os.environ['QT_API'] = qt_api
diff --git a/skimage/viewer/utils/core.py b/skimage/viewer/utils/core.py
index 0dcd4176..0b97d8c3 100644
--- a/skimage/viewer/utils/core.py
+++ b/skimage/viewer/utils/core.py
@@ -2,13 +2,18 @@ import warnings
 
 import numpy as np
 
+from ..qt import qt_api
+
 try:
     import matplotlib as mpl
     from matplotlib.figure import Figure
     from matplotlib import _pylab_helpers
     from matplotlib.colors import LinearSegmentedColormap
-    from matplotlib.backends.backend_qt4 import FigureManagerQT
-    from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
+    if qt_api is None:
+        raise ImportError
+    else:
+        from matplotlib.backends.backend_qt4 import FigureManagerQT
+        from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
 except ImportError:
     FigureCanvasQTAgg = object  # hack to prevent nosetest and autodoc errors
     LinearSegmentedColormap = object