* Improve Canny by using _mm_movemask_epi8 to find next pixel magnitude greater than lower threshold. Added parallelized finalPass to Canny with variable gradients. Little changes in finalPass.

* Some things fixed

  * use universal intrinsic for accumulate series using float/double
  * accumulate, accumulateSquare, accumulateProduct and accumulateWeighted
  * add v_cvt_f64_high in both SSE/NEON
  * add test for conversion v_cvt_f64_high in test_intrin.cpp
  * improve some existing universal intrinsic by using new instructions in Aarch64
  * add workaround for Android build in intrin_neon.hpp

* Add Grana's connected components algorithm for 8-way connectivity. That algorithm is faster than Wu's one (currently implemented in opencv). For more details see https://github.com/prittt/YACCLAB.

* New functions signature and distance transform compatibility

* Add tests to imgproc/test/test_connectedcomponents.cpp

* Change of test_connectedcomponents.cpp for c++98 support

      * accumulate, accumulateSquare, accumulateProduct and accumulateWeighted

  * The difference becomes too large when multiply is done in int16
  * To reproduce the test failure, IPP has to be switched off

"declare.in(img, WARMUP_RNG)" overrides input image with random values

Also added support for different input sizes (via cv::resize)

There is an issue with processing of abs(short) function for
negative argument.

Affected OpenCL devices:
- iGPU: Intel(R) HD Graphics 520 (OpenCL 2.0 )
- CPU: Intel(R) Core(TM) i5-6300U CPU @ 2.40GHz (OpenCL 2.0 (Build 10094))

* Common Canny parallelization added. TBB and single thread code removed. Final pass vectorized with SSE2 intrinsics.

* wrong #ifdef replaced with #if

* Merged to actual Canny version

* Merged common parallelized Canny with actual Canny implementation

* Remove 'Mutex *mutex' and pass 'Mutex mutex' from outside to parallelCanny

* Replaced extern Mutex with intern mutable Mutex.

Problem is observed with test_loop_times=100
in corner cases like 93x1 or similar

When using OCL, the results of goodFeaturesToTrack() vary slightly from
run to run. This appears to be because the order of the results from
the findCorners kernel depends on thread execution and the sorting
function that is used at the end to rank the features only enforces are
partial sort order.

This does not materially impact the quality of the results, but it
makes it hard to build regression tests and generally introduces noise
into the system that should be avoided.

An easy fix is to change the sort function to enforce a total sort on
the features, even in cases where the match quality is exactly the same
for two features.

  * indent line

  * clean up some lines

  * use SSE and/or AVX based on configuration
  * revise the test to verify the implementation

  * use NEON under aarch64 only
  * check 64F version correctly