In the kinematic analysis of time-varying imagery, where the goal is to recover object surface structure and space motion from image flow, an appropriate representation for the flow field consists of a set of deformation parameters that describe the rate of change of an image neighborhood. In this paper we develop methods for extracting these deformation param eters from evolving contours in an image sequence, the image contours being manifestations of surface texture seen in perspective projection. Our results follow directly from the analytic structure of the underlying image flow; no heuristics are imposed. The deformation parameters we seek are actu ally linear combinations of the Taylor series coefficients (through second derivatives) of the local image flow field. Thus, a by-product of our approach is a second-order polyno mial approximation to the image flow in the neighborhood of a contour. For curved surfaces this approximation is only locally valid, but for planar surfaces it is globally valid (i.e., it is exact). Our analysis reveals an "aperture problem in the large" in which insufficient contour structure leaves the set of 12 deformation parameters underdetermined. We also assess the sensitivity of our method to the simulated effects of noise in the "normal flow" around contours as well as the angular field of view subtended by contours. The sensitivity analysis is carried out in the context of planar surfaces executing general rigid-body motions in space. Future work will address the additional considerations relevant to curved surface patches.
SUMMARYNavigation systems providing route-guidance and traffic information are one of the most widely used driver-support systems these days. Most navigation systems are based on the map paradigm which plots the driving route in an abstracted version of a two-dimensional electronic map. Recently, a new navigation paradigm was introduced that is based on the augmented reality (AR) paradigm which displays the driving route by superimposing virtual objects on the real scene. These two paradigms have their own innate characteristics from the point of human cognition, and so complement each other rather than compete with each other. Regardless of the paradigm, the role of any navigation system is to support the driver in achieving his driving goals. The objective of this work is to investigate how these map and AR navigation paradigms impact the achievement of the driving goals: productivity and safety. We performed comparative experiments using a driving simulator and computers with 38 subjects. For the effects on productivity, driver's performance on three levels (control level, tactical level, and strategic level) of driving tasks was measured for each map and AR navigation condition. For the effects on safety, driver's situation awareness of safety-related events on the road was measured. To find how these navigation paradigms impose visual cognitive workload on driver, we tracked driver's eye movements. As a special factor of driving performance, route decision making at the complex decision points such as junction, overpass, and underpass was investigated additionally. Participant's subjective workload was assessed using the Driving Activity Load Index (DALI). Results indicated that there was little difference between the two navigation paradigms on driving performance. AR navigation attracted driver's visual attention more frequently than map navigation and then reduces awareness of and proper action for the safety-related events. AR navigation was faster and better to support route decision making at the complex decision points. According to the subjective workload assessment, AR navigation was visually and temporally more demanding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.