Integral Image Optimizations for Embedded Vision Applications

This paper illustrates the importance of both algorithmic and embedded software techniques for an optimal embedded implementation of an image analysis and computer vision function: the integral image. A naive, straightforward implementation of the integral image on an embedded processor will likely produce an unacceptable execution time. However, by applying recursion and double buffering, one can improve execution time by several orders of magnitude. We compare execution times and memory utilization for each of the optimization techniques applied. These techniques can also be applied to implement other computer vision functions on programmable processor architectures.