View management, a relatively new area of research inAugmented Reality (AR) applications, is about the spatial layout of 2D virtual annotations in the view plane. This paper represents the first study in an actual AR application of a specific view management task: evaluating the placement of 2D virtual labels that identify information about real counterparts. Here, we objectively evaluated four different placement algorithms, including a novel algorithm for placement based on identifying existing clusters. The evaluation included both a statistical analysis of traditional metrics (e.g. counting overlaps) and an empirical user study guided by principles from human cognition.The numerical analysis of the three real-time algorithms revealed that our new cluster-based method recorded the best average placement accuracy while requiring only relatively moderate computation time.Measures of objective readability from the user study demonstrated that in practice, human subjects were able to read labels fastest with the algorithms that most quickly prevented overlap, even if placement wasn't ideal. MotivationOne of the primary goals of Augmented Reality (AR) is to associate virtual information with their counterparts in the real world. While the virtual information is usually rendered in 3D, in some applications the virtual information may be confined to the 2D view plane. For example, virtual annotations rendered in a head-mounted display might be 2D labels identifying the names of nearby buildings. The decision of where and how to draw the annotations, to determine the spatial layout in the view plane, is the problem of view management [5]. This is a relatively new area of research in AR and Mixed Reality applications.This paper focuses on a specific problem in view management: the placement and evaluation of 2D labels that are associated with 3D counterparts. A basic problem with drawing 2D labels is that the labels may obscure important background objects or may overlap, making them impossible to read. Figure 1 shows an example from our test application, with the labels arranged in randomly-chosen positions. This is a poor configuration because many labels overlap and are unreadable. Figure 2 shows a better labeling for exactly the same situation, where the labels were automatically repositioned by a cluster-based method described in Section 5. Figure 1: Initial (randomly chosen) label p o s i t i o n s Figure 2: Repositioned labels through clusterbased method (red dial pattern #2)Label placement for AR applications is not trivial. Even for a static image, the general label placement problem is NP-hard [7]. The number of possible label positions grows exponentially with the number of items to be labeled. For example, if a label can occupy one of 36 positions around an object, then for 20 objects there are 36 20 possible label positions (over 1x10 31 ). At interactive rates, only a few possible combinations can be considered. Furthermore, cognitive and perceptual issues regarding label placement for AR applications ar...

One of the unique applications of Mixed and Augmented Reality (MR / AR) MotivationIn Mixed Reality (MR) and Augmented Reality (AR) systems, virtual objects are combined with real images at interactive rates in 3D. Such displays can enhance the user's perception of the real environment by showing information the user cannot directly sense when unaided. For example, in many AR applications we wish to endow the user with "X-ray vision," enabling the user to see through objects to view a fetus inside a womb, to see pipes and conduits behind walls, or to spot the location of a hidden enemy soldier. Being able to see occluded objects is a useful capability in a variety of medical, architectural, inspection, and military applications. This technology is especially useful in urban environments, where broad, angular surfaces (e.g., walls in hallways, buildings on a street, etc.) limit one's field of view of nearby visually-obscured locations.However, displaying such hidden objects in a manner that a user intuitively understands is not always trivial. Take the example of a soldier in one room of a building using an AR system to spot the known location of a 1). The use of transparent overlays (LEFT) conveys depth by letting the viewer see structure not otherwise visible, but while still perceiving the realworld structure. A similar approach (RIGHT) presents normally unseen structure by over-rendering a virtual "cut-away" of the occluding surfaces. This approach more clearly depicts the inside of the room, but at the cost of occluding real-world surfaces.

Abstract-From a general cognitive perspective, decision making is the process of selecting a choice or course of action from a set of alternatives. A large number of time critical decision making models have been developed over the course of several decades. This paper reviews both the underlying cognitive processes and several decision making models. In the first section, we briefly describe the primary underlying cognitive processes and issues that are common to most, if not all, decision making models, with a focus on attention, working memory, and reasoning. The second section reviews several of the most prominent high-level models of decision making, especially those developed for military contexts.

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