This simple detector runs up to two times faster than LSD while achieving
competitive results. Please evaluate the algorithm with samples/fld_lines.cpp

added thinning sample
added thinning tests

* Add bilateral texture filter

* Change tab to space

Paillou Filter

comment : english tab and assert  Mat(instead of new)

InputArray and ptr<Type>()

Outputarrays

OutputArray as argument

Update doc

- no for-loops
- support all thresholding types
- support any input image depth
- add Doxygen comments

* Sparse match interpolator interface and EdgeAwareInterpolator were
added to the ximgproc module
* New optical flow algorithm, based on PyrLK sparse OF and sparse match
interpolation, is added to the optflow module

Now the filter natively supports StereoBM and StereoSGBM with no
parameter tuning required. Also, now user won't need to set the ROI and
the right matcher parameters manually, it is all done in the respective
convenience factory method based on the left matcher instance. Tutorial
was added to clarify the provided example of use.

All Niblack related code now resides inside ximgproc and not as a
separate new module.

DisparityWLSFilter demonstrated the best results, so I removed all the other
filters. Quality was significantly improved by adding confidence support
to the filter (left-right consistency + penalty for areas near depth discontinuities).
Filter was optimized using parallel_for_ and HAL intrinsics. Demo application was
rewritten for better compliance with OpenCV standards. Added accuracy and
performance tests. Documentation was added, as well as references to the
original papers.

modules/ximgproc/include/opencv2/ximgproc/fast_hough_transform.hpp moved from modules/ximgproc/include/opencv2/ximgproc.hpp
HoughPoint2Line returned Vec4i instead of OutputArray

Added basic interface and demo for disparity filtering, added unoptimized fast weighted least
squares filter implementation. Current demo tests domain transform, guided and
weighted least squares filters on a dataset, measures speed and quality.