Segmentation photometry and morphological properties

This large PR adds two new functions to perform photometry and calculate morphological properties of sources defined by segmentation images (e.g, from SExtractor or made with `detect_sources`).  When the segmentation image is defined using a thresholded flux level (e.g., `detect_sources`), `segment_photometry` is equivalent to performing isophotal photometry in SExtractor.

I still need to write tests for these new functions, but this is ready for review.  I've done some comparisons to SExtractor, and with the same input segmentation image and background, the photometry and morphology results are identical.

Docs for updated `detect_sources`:
* http://www.stsci.edu/~lbradley/photutils/segmentation/api/photutils.detect_sources.html#photutils.detect_sources

Docs for new `segment_photometry` and `segment_properties` functions:
* http://www.stsci.edu/~lbradley/photutils/segmentation/api/photutils.segment_photometry.html#photutils.segment_photometry

* http://www.stsci.edu/~lbradley/photutils/segmentation/api/photutils.segment_properties.html#photutils.segment_properties

New `SegmentProperties` class:
* http://www.stsci.edu/~lbradley/photutils/segmentation/api/photutils.SegmentProperties.html#photutils.SegmentProperties

Yes, as long as you make it very clear that you're assuming it, I think it's safe to say that Gaussian is the default, but then give the users everything they need to do something else if they want to.

On @cdeil's original point, perhaps you can just say something here like "the gain assumed for determining the final error (), or None to assume `error` holds it all.  See below for more detail" And then in the "Notes" section of the docstring you can go into detail including some examples both with and without gain (and with non-gaussian errors).