fixed dependency of stitching module on xfeatures2d module as optional
fixed the initModule_xfeatures2d function that was called in module stitching since it is in another namespace than the standard cv one.

removed optim module; moved its functionality to core and photo modules; moved drawing functions from core to imgproc. Removed FilterEngine etc. from public API

Porting the [PR 2970](https://github.com/Itseez/opencv/pull/2970) To the new InputOutputArray type

put the detection-based tracker back (to objdetect module), so that the Android face detection sample builds fine. The patch by Konstantin Matskevich that adds C++11 threads support has been merged in too. Also, fixed compile error in the doc builder.

I propose forEach method for cv::Mat and cv::Mat_.
This is solution for the overhead of MatIterator_<_Tp>.

I runs a test that micro opecode runs all over the pixel of cv::Mat_<cv::Point3_<uint8_t>>.
And this implementation 40% faster than the simple pointer, 80% faster than iterator.
With OpenMP, 70% faster than simple pointer, 95% faster than iterator (Core i7 920).

Above all, code is more readable.

My test code is here.
    https://gist.github.com/kazuki-ma/8285876

Thanks.

made the example consistent with the fuction definition and improved doc

hopefully, fixed compile errors on Win & Linux; fixed getMatVector() so core & imgproc tests now pass; fixed doc builder errors

Signed-off-by: Han Qiang <a0byte@gmail.com>

Added extra information for fitEllipse, minAreaRect and addapted ellipse &
ellipse2Poly parameter description.

Conflicts:
	modules/core/include/opencv2/core/cuda.hpp
	modules/cudacodec/src/thread.cpp
	modules/cudacodec/src/thread.hpp
	modules/superres/perf/perf_superres.cpp
	modules/superres/src/btv_l1_cuda.cpp
	modules/superres/src/optical_flow.cpp
	modules/videostab/src/global_motion.cpp
	modules/videostab/src/inpainting.cpp
	samples/cpp/stitching_detailed.cpp
	samples/cpp/videostab.cpp
	samples/gpu/stereo_multi.cpp

Usage:

import numpy as np
import cv2
img = cv2.imread('messi5.jpg',0)
sift = cv2.SIFT()
kp = sift.detect(img)

pts = cv2.KeyPoint_convert(kp)

kps = cv2.KeyPoint_convert(pts.reshape(-1,1,2))

overlap = cv2.KeyPoint_overlap(kp[0],kp[1])

Changed the brief description of function Mat::colRange from "Create a matrix header for the specified row span." to "Create a matrix header for the specified column span."