Blob Detection ( DoG )

Example Code
------------------
```python
from skimage.feature import get_blobs
from skimage import data
from matplotlib import pyplot as plt
import math

img = data.coins()
blobs = get_blobs(img)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.imshow(img,cmap='gray')


for b in blobs:
    x,y = b[0],b[1]
    r = math.sqrt( b[2]/math.pi )
    c = plt.Circle((y,x),r,color='#ff0000',lw = 2,fill = False)
    ax.add_patch(c)

plt.show()
```
Results
---------

![coins](https://f.cloud.github.com/assets/3823490/2289502/60cd99d6-a009-11e3-8c8a-60ba936543de.png)

![space](https://f.cloud.github.com/assets/3823490/2289504/6c3d05b8-a009-11e3-9983-f94291e27465.png)

This is my first PR to scikit-image. I would be glad if someone could go through it and suggest improvements

I haven't used Cython before.Could re writing `_blob_overlap` in Cython provide speed improvements ? The main bottle neck in this case is computing the Gaussian Filter. 

@cdeil , @adonath Please share your thoughts.

LoG will be just minor additions to this. Once this is accepted I'll move onto that and the remaining ones.