Switched-Capacitor Networks for Scale-Space Generation

In scale-space filtering signals are represented at several scales, each conveying different details of the original signal. Every new scale is the result of a smoothing operator on a former scale. In image processing, scale-space filtering is widely used in feature extractors as the Scale-Invariant Feature Transform(SIFT) algorithm. RC networks are posed as valid scale-space generators in focal-plane processing. Switched-capacitor networks are another alternative, as different topologies and switching rate offer a great flexibility. This work examines the parallel and the bilinear implementations as two different switched capacitor network topologies for scale-space filtering. The paper assesses the validity of both topologies as scale-space generators in focal-plane processing through object detection with the SIFT algorithm.

keywords: Switched-Capacitor Networks, Scale-Space Generation, Feature Detectors, Gaussian Filtering