https://sourceforge.net/p/predef/wiki/OperatingSystems

Updated integrations for:
cv::split
cv::merge
cv::insertChannel
cv::extractChannel
cv::Mat::convertTo - now with scaled conversions support
cv::LUT - disabled due to performance issues
Mat::copyTo
Mat::setTo
cv::flip
cv::copyMakeBorder - currently disabled
cv::polarToCart
cv::pow - ipp pow function was removed due to performance issues
cv::hal::magnitude32f/64f - disabled for <= SSE42, poor performance
cv::countNonZero
cv::minMaxIdx
cv::norm
cv::canny - new integration. Disabled for threaded;
cv::cornerHarris
cv::boxFilter
cv::bilateralFilter
cv::integral

Warping a matrix with more than 4 channels using BORDER_CONSTANT and
INTER_NEAREST, INTER_CUBIC or INTER_LANCZOS4 interpolation led to
undefined behaviour. This commit changes the behavior of these methods
to be similar to that of INTER_LINEAR. Changed the scope of some of the
variables to more local. Modified some tests to be able to detect the
error described.

Add new 5x5 gaussian blur kernel for CV_8UC1 format,
it is 50% ~ 70% faster than current ocl kernel in the perf test.
Signed-off-by: Li Peng <peng.li@intel.com>

Add new ocl kernel for image pyramids upsampling,
It is 35% faster than current OCL kernel in perf test.
Signed-off-by: Li Peng <peng.li@intel.com>

Add new OpenCL kernels for bicubic interploation, it is 20% faster
than current warp image kernel with bicubic interploation.
Signed-off-by: Li Peng <peng.li@intel.com>

Add new ocl kernels for warpAffine and warpPerspective,
The average performance improvemnt is about 30%. The new
ocl kernels require CV_8UC1 format and support nearest
neighbor and bilinear interpolation.
Signed-off-by: Li Peng <peng.li@intel.com>

This ocl kernel is 46%~171% faster than current laplacian 3x3
ocl kernel in the perf test, with image format "CV_8UC1".
Signed-off-by: Li Peng <peng.li@intel.com>

Change contour test images to be very wide (#7464)

* Change contour test images to be very wide (#7409, #7458)

Unfortunately, slows down the tests.

* Decrease the number of contour test cases, in order to (at least partially) offset the test run duration increase caused by making the test images wider

* Don't test with very wide images on 32-bit architectures

It improves 108%~230% performance in the perf test
with image format "CV_8UC1" and kernel size 3.
Signed-off-by: Li Peng <peng.li@intel.com>

This ocl kernel is for 3x3 kernel size and CV_8UC1 format
It is 115% ~ 300% faster than current ocl path in perf test

python ./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_GaussianBlurFixture*
Signed-off-by: Li Peng <peng.li@intel.com>

This kernel is for CV_8UC1 format and 3x3 kernel size,
It is about 33% ~ 55% faster than current ocl kernel with below perf test

python ./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_ErodeFixture*
python ./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_DilateFixture*

Also add accuracy test cases for this kernel, the test command is

./bin/opencv_test_imgproc --gtest_filter=OCL_Filter/MorphFilter3x3*
Signed-off-by: Li Peng <peng.li@intel.com>

The optimization is for CV_8UC1 format and 3x3 box filter,
it is 15%~87% faster than current ocl kernel with below perf test

./modules/ts/misc/run.py -t imgproc --gtest_filter=OCL_BlurFixture*

Also add test cases for this ocl kernel.
Signed-off-by: Li Peng <peng.li@intel.com>

OpenCL Device:
- Intel(R) HD Graphics (OpenCL 2.0: r2.0.54425)

* 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

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