Bi-Level Thresholding

Abstract

In gesture recognition, one challenge that researchers and developers face is the need for recognition strategies that mediate between false positives and false negatives. In this article, we examine bi-level thresholding, a recognition strategy that uses two thresholds: a tighter threshold limits false positives and recognition errors, and a looser threshold prevents repeated errors (false negatives) by analyzing movements in sequence. We first describe early observations that led to the development of the bi-level thresholding algorithm. Next, using a Wizard-of-Oz recognizer, we hold recognition rates constant and adjust for fixed versus bi-level thresholding; we show that systems using bi-level thresholding result in significantly lower workload scores on the NASA-TLX and significantly lower accelerometer variance when performing gesture input. Finally, we examine the effect that bi-level thresholding has on a real-world dataset of wrist and finger gestures, showing an ability to significantly improve measures of precision and recall. Overall, these results argue for the viability of bi-level thresholding as an effective technique for balancing between false positives, recognition errors, and false negatives.

Measuring the effect of public vs private context on the kinematics of smartphone motion gestures

“…Ceci diffère des interfaces gestuelles fixes telles que les écrans publics où l'endroit et le contexte d'interaction sont fixes. Le design d'interfaces gestuelles sur smartphone est déjà freiné par le besoin de distinguer entre les gestes intentionnels d'interaction et les gestes du quotidien [5,8,12]. Ce problème requiert des algorithmes de reconnaissance finement paramétrés afin de limiter les faux positifs.…”

An Otsu thresholding for images based on a nature-inspired optimization algorithm

“…Bi-level thresholding and multi-level thresholding are the two types of thresholding that are used in practice. Over the past ten years, bi-level thresholding has attracted a lot of attention from researchers [1], [2]. The study of multilevel thresholding has also been ongoing [3]- [5].…”