- broken bindings generator
- broken conversion functions (uses non-supported cv::Mat types)
- other problems like missing convertion functions for Moments and other structs

* kcf use float data type rather than double.

In our practice, float is good enough and could get better performance.
With this patch, one of my benchmark could get about 20% performance gain.
Signed-off-by: Zhigang Gong <zhigang.gong@intel.com>

* Offload transpose matrix multiplication to ocl.

The matrix multiplication in updateProjectMatrix is one of the
hotspot. And because of the matrix shape is special, say the
m is very short but the n is very large. The GEMM implementation
in neither the clBLAS nor the in trunk implementation are very
inefficient, I implement an standalone transpose matrix mulplication
kernel here. It can get about 10% performance gain on Intel
desktop platform or 20% performance gain on a braswell platform.
And in the mean time, the CPU utilization will be lower.
Signed-off-by: Zhigang Gong <zhigang.gong@intel.com>

* Add verification code for kcf ocl transpose mm kernel.
Signed-off-by: Zhigang Gong <zhigang.gong@linux.intel.com>

* tracking: show FPS in traker sample

* tracking: fix MSVC warnings in KCF

* tracking: move OCL kernel initialization to constructor in KCF

Squashed commits:
[802ce32] surface_matching loadPLYSimple() reports only ascii ply is supported (+1 squashed commit)
Squashed commits:
[e240207] surface_matching loadPLYSimple() reports only ascii ply is supported

noise control implementation ended

noise control errors fixed (part I)

noise control errors fixed (ended)

Perf Test for staticSaliencySpectralResidual and objectnessBING added

improvements and bugs fixed

saliency perf test added

matrices optimization

Where possible, CV_8U matrices have been used in place of CV_32F

new sorting algorithm implemented (first part)

new sorting algorithm implemented (second part)

new sorting algorithm implemented (finished)

Updated module license text.

How to install tesseract on windows (#1361)

rvecs and tvecs should be of type std::vector<cv::Vec3d> as used in the detect_markers.cpp. Otherwise an exception is thrown.

should use UMat