Implementation of Censure(STAR) Feature Detector

### Issues to address:
- [x] Implement STAR filter
- [x] Speed. Compare with numpy convolve?
- [ ] Check the dependency of `rpc_threshold` with block_size at various scales
- [ ] Decide the size of Octagon filter for scales > 7
- [ ] Wrap lines and other PEP8 changes

### Current API:
```py
>>> import numpy as np
>>> from time import time
>>> start = time()
>>> img = lena()
>>> img = rgb2gray(img)
>>> keypoints_oct = censure_keypoints(img, 7, 'Octagon')
>>> print time() - start
2.17165398598
>>> keypoints_oct.shape
(1206, 3)
>>> keypoints_oct[:10]
array([[  0, 400,   3],
       [  0, 500,   4],
       [  0, 511,   6],
       [  3, 507,   2],
       [  8, 501,   2],
       [  9, 403,   2],
       [ 11, 404,   2],
       [ 11, 494,   5],
       [ 16, 500,   3],
       [ 20, 495,   2]])
>>> start = time()
>>> keypoints_dob = censure_keypoints(img, 7, 'DoB')
>>> print time() - start
0.799248933792
>>> keypoints_dob.shape
(2183, 3)
>>> keypoints_dob[:10]
array([[ 4, 19,  2],
       [ 4, 24,  2],
       [ 4, 39,  2],
       [ 4, 45,  2],
       [ 4, 47,  2],
       [ 4, 51,  2],
       [ 4, 53,  2],
       [ 4, 56,  2],
       [ 4, 71,  2],
       [ 4, 75,  2]])
>>> 
```