Scale- and rotation-invariant feature detectors on Cellular Processor Arrays

This paper assesses the implementation of scale-and rotation-invariant feature detectors on Cellular Processor Arrays (CPA). Scale- and rotation-invariant feature detectors are complex image processing algorithms with a high computational burden in the low-level image processing stage due to large- neighborhood convolution type operations. Such operations are used to generate the so-called scale-space. This paper outlines different options to provide the scale space in the Scale Invariant Feature Transform (SIFT) and the Speeded-Up Robust Features (SURF) algorithms on CPAs with pixel-per-processor assignment. The paper shows that it is feasible to do this even with a reduced set of inter-processor communications within acceptable time limits on existing CPAs.

keywords: Cellular Processor Arrays (CPA), Scale Invariant and Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Hardware Implementation, Computer Vision, Feature Extraction, Parallel Processing, Split and Shift Techniques