Added variational refinement as a separate class (based on implementation
inside DeepFlow, but significantly accelerated, about 4-6 times faster),
accelerated the main dense inverse search algorithm. Added several new
features including patch mean normalization for increased robustness to
illumination changes and spatial propagation, which often helps to recover
from errors introduced by the coarse-to-fine scheme. Expanded the
documentation, added new accuracy and perf tests. Refactored some of
the already existing optical flow accuracy tests.

[Aruco] Fixed the spelling of Length in Samples

replaced images to 6x6_250

Updated README

[Aruco] Updated Charuco Diamond Detection Tutorial Readme

[Aruco] Updated Charuco Board  Detection Tutorial Readme

[Aruco] Updated Aruco Tutorials  Readme

[Aruco] Updated Aruco Tutorials  Readme

Added a new line at the end of file.

Update aruco_board_detection.markdown

Update aruco_calibration.markdown

Update aruco_detection.markdown

Update charuco_detection.markdown

Update charuco_diamond_detection.markdown

Update README.md

Update charuco_detection.markdown

- with big images the int multiplication can overflow

Basic interfaces and a partial implementation of the Dense Inverse
Search (DIS) optical flow algorithm without variational refinement. Also
added a python benchmarking script that can evaluate different optical
flow algorithms on the MPI Sintel and Middlebury datasets and build
overall comparative charts.

Extensive changes to README.md opencv_contrib file to bring it up to date.

`lenght` --> `length`

structured_light:  python / java bindings

Update 2