No abstract

Objective: The study examines the factors determining the movement time (MT) of positioning an object in an immersive 3D virtual environment. Background: Positioning an object into a prescribed area is a fundamental operation in a 3D space. Although Fitts’s law models the pointing task very well, it does not apply to a positioning task in an immersive 3D virtual environment since it does not consider the effect of object size in the positioning task. Method: Participants were asked to position a ball-shaped object into a spherical area in a virtual space using a handheld or head-tracking controller in the ray-casting technique. We varied object size (OS), movement amplitude (A), and target tolerance (TT). MT was recorded and analyzed in three phases: acceleration, deceleration, and correction. Results: In the acceleration phase, MT was inversely related to object size and positively proportional to movement amplitude. In the deceleration phase, MT was primarily determined by movement amplitude. In the correction phase, MT was affected by all three factors. We observed similar results whether participants used a handheld controller or head-tracking controller. We thus propose a three-phase model with different formulae at each phase. This model fit participants’ performance very well. Conclusion: A three-phase model can successfully predict MT in the positioning task in an immersive 3D virtual environment in the acceleration, deceleration, and correction phases, separately. Application: Our model provides a quantitative framework for researchers and designers to design and evaluate 3D interfaces for the positioning task in a virtual space.

Attention readily facilitates the detection and discrimination of objects, but it is not known whether it helps to form the vast volume of visual space that contains the objects and where actions are implemented. Conventional wisdom suggests not, given the effortless ease with which we perceive three-dimensional (3D) scenes on opening our eyes. Here, we show evidence to the contrary. In Experiment 1, the observer judged the location of a briefly presented target, placed either on the textured ground or ceiling surface. Judged location was more accurate for a target on the ground, provided that the ground was visible and that the observer directed attention to the lower visual field, not the upper field. This reveals that attention facilitates space perception with reference to the ground. Experiment 2 showed that judged location of a target in mid-air, with both ground and ceiling surfaces present, was more accurate when the observer directed their attention to the lower visual field; this indicates that the attention effect extends to visual space above the ground. These findings underscore the role of attention in anchoring visual orientation in space, which is arguably a primal event that enhances one’s ability to interact with objects and surface layouts within the visual space. The fact that the effect of attention was contingent on the ground being visible suggests that our terrestrial visual system is best served by its ecological niche.

No abstract

Abstract. [18 F]-Fluorodeoxyglucose Positron Emission Tomography (PET) is an essential imaging modality for the detection of the lymphomas. Generally, the lesions are detected by modeling the characteristics of abnormal areas via thresholding the degree of FDG uptake. However, it is difficult to detect lesions precisely because of inconsistent shape, discontinuous localization. Besides, the sites of normal physiological FDG uptake and normal FDG excretion (sFEPU) such as the kidneys are always interference with lymphoma detection. To address these issues, we propose a novel framework for the recognition of sFEPU and detection of lymphoma based on fully convolutional networks (FCN). FCN is used to extract high-level semantic information for lymphoma detection and meanwhile multi-scale integration in many times to refine the edge detection of sFEPU. Experimental results demonstrates the satisfactory detection and recognition accuracy compared to existing methods.