Add patches required for SunOS support.
Update to 2.4.5:

2.4.5

April, 2013

    Experimental WinRT support (build for WindowsRT guide)

    the new video super-resolution module has been added that
    implements the following papers:

    - S. Farsiu, D. Robinson, M. Elad, P. Milanfar. Fast and robust
    Super-Resolution. Proc 2003 IEEE Int Conf on Image Process,
    pp. 291â294, 2003.
    - D. Mitzel, T. Pock, T. Schoenemann, D. Cremers. Video super
    resolution using duality based TV-L1 optical flow. DAGM, 2009.

    CLAHE (adaptive histogram equalization) algorithm has been
    implemented, both CPU and GPU-accelerated versions (in imgproc
    and gpu modules, respectively)

    there are further improvements and extensions in ocl module:
    - 2 stereo correspondence algorithms: stereobm (block matching)
    and stereobp (belief propagation) have been added
    - many bugs fixed, including some crashes on Intel HD4000

    The tutorial on displaying cv::Mat inside Visual Studio 2012
    debugger has been contributed by Wolf Kienzle from Microsoft
    Research. See
    http://opencv.org/image-debugger-plug-in-for-visual-studio.html

    78 pull requests have been merged. Big thanks to everybody who
    contributed!

    At least 25 bugs have been fixed since 2.4.4 (see
    http://code.opencv.org/projects/opencv/issues select closed
    issues with target version set to "2.4.5").

2.4.4

March, 2013

    This is the biggest news in 2.4.4 - we've got full-featured
    OpenCV Java bindings on a desktop, not only Android! In fact
    you can use any JVM language, including functional Java or
    handy Groovy. Big thanks to Eric Christiansen for the contribution!
    Check the tutorial for details and code samples.

    Android application framework, samples, tutorials, OpenCV
    Manager are updated, see Android Release Notes for details.

    Numerous improvements in gpu module and the following new
    functionality & optimizations:
	Optimizations for the NVIDIA Kepler architecture
	NVIDIA CARMA platform support
	HoughLinesP for line segments detection
	Lab/Luv <-> RGB conversions

    Let us be more verbose here. The openCL-based hardware acceleration
    (ocl) module is now mature, and, with numerous bug fixes, it
    is largely bug-free. Correct operation has been verified on
    all tested platforms, including discrete GPUs (tested on NVIDIA
    and AMD boards), as well as integrated GPUs (AMD APUs as well
    as Intel Ivy Bridge iGPUs). On the host side, there has been
    exhaustive testing on 32/64 bit, Windows/Linux systems, making
    the ocl module a very serious and robust cross-platform GPU
    hardware acceleration solution. While we currently do not test
    on other devices that implement OpenCL (e.g. FPGA, ARM or other
    processors), it is expected that the ocl module will work well
    on such devices as well (provided the minimum requirements
    explained in the user guide are met).
    Here are specific highlights of the 2.4.4 release:
	The ocl::Mat can now use âspecialâ memory (e.g. pinned
	memory, host-local or device-local).
	The ocl module can detect if the underlying hardware supports
	âintegrated memory,â and if so use âdevice-localâ memory
	by default for all operations.
	New arithmetic operations for ocl::Mat, providing significant
	ease of use for simple numerical manipulations.
	Interop with OpenCL enables very easy integration of OpenCV
	in existing OpenCL applications, and vice versa.
	New algorithms include Hough circles, more color conversions
	(including YUV, YCrCb), and Hu Moments.
	Numerous bug fixes, and optimizations, including in:
	blendLinear, square samples, erode/dilate, Canny, convolution
	fixes with AMD FFT library, mean shift filtering, Stereo
	BM.
	Platform specific bug fixes: PyrLK, bruteForceMatcher,
	faceDetect now works also on Intel Ivy Bridge chips (as
	well as on AMD APUs/GPUs and NVIDIA GPUs); erode/dilate
	also works on NVIDIA GPUs (as well as AMD APUs/GPUs and
	Intel iGPUs).

    Many people contributed their code in the form of pull requests.
    Here are some of the most interesting contributions, that were
    included into 2.4 branch:
	>100 reported problems have been resolved since 2.4.3
	Oscar Deniz submitted smile detector and sample.
	Alexander Smorkalov created a tutorial on cross-compilation
	of OpenCV for Linux on ARM platforms.