Summed-area table vs Integral image? I have been going through few research papers around face recognition and I came across two texture extraction algorithms : 


Summed-area table by Crow et al. ("Summed-area tables for texture mapping", 1984)
Integral image by Viola et al. (e.g. "Robust Real-time Object Detection", 2002)


Apologizes if I'm wrong, I found that generating texture map in both these algorithms are same.


  i.e., Texture map $I(x,y)$ = Sum of all gray level intensity values
  who lies left and above $i(x,y)$ of the original image.


Please clarify me, what is the main difference between these two algorithms?
The Sum-area table (SAT) was introduced in computer graphics, and as far as I know, was popularized in computer vision and image processing under the name Integral image. 

Apart from a larger genericity of the SAT to objects of dimensions different to that of images, the concepts are the same to me.