Merge remote-tracking branch 'upstream/3.4' into merge-3.4

doc/opencv.bib

@@ -209,7 +209,21 @@
   hal_id = {inria-00350283},
   hal_version = {v1},
 }
-
+@article{Collins14
+  year = {2014},
+  issn = {0920-5691},
+  journal = {International Journal of Computer Vision},
+  volume = {109},
+  number = {3},
+  doi = {10.1007/s11263-014-0725-5},
+  title = {Infinitesimal Plane-Based Pose Estimation},
+  url = {http://dx.doi.org/10.1007/s11263-014-0725-5},
+  publisher = {Springer US},
+  keywords = {Plane; Pose; SfM; PnP; Homography},
+  author = {Collins, Toby and Bartoli, Adrien},
+  pages = {252-286},
+  language = {English}
+}
 @article{Daniilidis98,
   author = {Konstantinos Daniilidis},
   title = {Hand-Eye Calibration Using Dual Quaternions},

doc/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.markdown

@@ -21,7 +21,6 @@ train_data, and next 250 samples as test_data. So let's prepare them first.
 @code{.py}
 import numpy as np
 import cv2 as cv
-from matplotlib import pyplot as plt
 
 img = cv.imread('digits.png')
 gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
@@ -89,7 +88,6 @@ alphabets directly.
 @code{.py}
 import cv2 as cv
 import numpy as np
-import matplotlib.pyplot as plt
 
 # Load the data, converters convert the letter to a number
 data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',