Geometric Sequence (GS) imaging with Bayesian smoothing for optical and capacitive imaging sensors

In this paper, we introduce a novel technique called geometric sequence (GS) imaging, specifically for the purpose of low power and light weight tracking in human computer interface design. The imaging sensor is programmed to capture the scene with a train of packets, where each packet constitutes a few images. The delay or the baseline associated with consecutive image pairs in a packet follows a fixed ratio, as in a geometric sequence. The image pair with shorter baseline or delay captures fast motion, while the image pair with larger baseline or delay captures slow motion. Given an image packet, the motion confidence maps computed from the slow and the fast image pairs are fused into a single map. Next, we use a Bayesian update scheme to compute the motion hypotheses probability map, given the information of prior packets. We estimate the motion from this probability map. The GS imaging system reliably tracks slow movements as well as fast movements, a feature that is important in realizing applications such as a touchpad type system. Compared to continuous imaging with short delay between consecutive pairs, the GS imaging technique enjoys several advantages. The overall power consumption and the CPU load are significantly low. We present results in the domain of optical camera based human computer interface (HCI) applications, as well as for capacitive fingerprint imaging sensor based touch pad systems.