# Summed area table

A summed area table (also known as an integral image) is a data structure and algorithm for quickly and efficiently generating the sum of values in a rectangular subset of a grid. It was first introduced to computer graphics in 1984 by Frank Crow for use with mipmaps. In computer vision, it was first prominently used within the Viola–Jones object detection framework in 2002. However, historically, this principle is very well known in the study of multi-dimensional probability distribution functions, namely in computing 2D (or ND) probabilities (area under the probability distribution) from the respective cumulative distribution functions.[1]

## The algorithm

As the name suggests, the value at any point (xy) in the summed area table is just the sum of all the pixels above and to the left of (xy), inclusive:[2][3]

$I(x,y) = \sum_{\begin{smallmatrix} x' \le x \\ y' \le y\end{smallmatrix}} i(x',y')$

Moreover, the summed area table can be computed efficiently in a single pass over the image, using the fact that the value in the summed area table at (xy) is just:

$I(x,y) = i(x,y) + I(x-1,y) + I(x,y-1) - I(x-1,y-1)\,$
Finding the sum of a rectangular area

Once the summed area table has been computed, the task of evaluating any rectangle can be accomplished in constant time with just four array references. Specifically, using the notation in the figure at right, having A=(x0, y1), B=(x1, y1), C=(x1, y0) and D=(x0, y0), the sum of $i(x,y)$ over the rectangle spanned by A, B,C and D is just

$\sum_{\begin{smallmatrix} x0 \le x \le x1 \\ y0 \le y \le y1 \end{smallmatrix}} i(x,y) = I(C) + I(A) - I(B) - I(D).$

## Extensions

This method is naturally extended to continuous domains.[4]

The method can be also extended to high dimensional images.[5] If the corners of the rectangle are $x^p$ with $p$ in $\{0,1\}^d$, then the sum of image values contained in the rectangle are computed with the formula

$\sum_{p\in\{0,1\}^d }(-1)^{d-\|p\|_1} I(x^p) \,$

where $I(x)$ is the integral image at $x$ and $d$ the image dimension. The notation $x^p$ correspond in the example to $d=2$, $A=x^{(0,0)}$, $B=x^{(1,0)}$, $C=x^{(1,1)}$ and $D=x^{(0,1)}$. In neuroimaging, for example, the images have dimension $d=3$ or $d=4$, when using voxels or voxels with time-stamp.

## References

1. ^ Finkelstein, Amir (2010). "Double Integrals By Summing Values Of Cumulative Distribution Function". Wolfram Demonstration Project.
2. ^ Crow, Franklin (1984). "Summed-area tables for texture mapping". SIGGRAPH '84: Proceedings of the 11th annual conference on Computer graphics and interactive techniques. pp. 207–212.
3. ^ Viola, Paul; Jones, Michael (2002). "Robust Real-time Object Detection". International Journal of Computer Vision.
4. ^ Finkelstein, Amir; neeratsharma (2010). "Double Integrals By Summing Values Of Cumulative Distribution Function". Wolfram Demonstration Project.
5. ^ Tapia, Ernesto (January 2011). "A note on the computation of high-dimensional integral images". Pattern Recognition Letters 32 (2). doi:10.1016/j.patrec.2010.10.007.