Kaze

This pull request adds two new feature algorithms: [KAZE][kaze] and much faster one [AKAZE][akaze].
Both algorithms were included in features 2d unit testing framework and registered in algorithms factory.
In synthetic tests, AKAZE demonstrate much better matching precision than other features (SURF, FREAK, BRISK and ORB). See my post with the [results][http://computer-vision-talks.com/articles/kaze-1.6-in-opencv/#results] for more details.
For instance, on synthetic rotation test, AKAZE features is able to match correctly almost 95% of detected features:
![chart_1](https://cloud.githubusercontent.com/assets/532320/2812145/7c109acc-ce44-11e3-8d91-eacda1aeba65.png)

On synthetic scaling test AKAZE is also very good, especially on very small images
![chart_1 1](https://cloud.githubusercontent.com/assets/532320/2812147/8d84b144-ce44-11e3-823d-a6caf9b562ef.png)

AKAZE uses cv::parallel_for to speed up the feature extraction. 

[kaze]: http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pdf
[akaze]: http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pdf