  The integral image, an intermediate image representation, has found extensive
use in multi-scale local feature detection algorithms, such as Speeded-Up
Robust Features (SURF), allowing fast computation of rectangular features at
constant speed, independent of filter size. For resource-constrained real-time
embedded vision systems, computation and storage of integral image presents
several design challenges due to strict timing and hardware limitations.
Although calculation of the integral image only consists of simple addition
operations, the total number of operations is large owing to the generally
large size of image data. Recursive equations allow substantial decrease in the
number of operations but require calculation in a serial fashion. This paper
presents two new hardware algorithms that are based on the decomposition of
these recursive equations, allowing calculation of up to four integral image
values in a row-parallel way without significantly increasing the number of
operations. An efficient design strategy is also proposed for a parallel
integral image computation unit to reduce the size of the required internal
memory (nearly 35% for common HD video). Addressing the storage problem of
integral image in embedded vision systems, the paper presents two algorithms
which allow substantial decrease (at least 44.44%) in the memory requirements.
Finally, the paper provides a case study that highlights the utility of the
proposed architectures in embedded vision systems.
