Spatial contextual awareness

Spatial contextual awareness consociates contextual information such as an individual's or sensor's location, activity, the time of day, and proximity to other people or objects and devices. It is also defined as the relationship between and synthesis of information garnered from the spatial environment, a cognitive agent, and a cartographic map. The spatial environment is the physical space in which the orientation or wayfinding task is to be conducted; the cognitive agent is the person or entity charged with completing a task; and the map is the representation of the environment which is used as a tool to complete the task. An incomplete view of spatial contextual awareness would render it as simply a contributor to or an element of contextual awareness – that which specifies a point location on the earth. This narrow definition omits the individual cognitive and computational functions involved in a complex geographic system. Rather than defining the myriad of potential factors contributing to context, spatial contextual awareness defined in terms of cognitive processes permits a unique, user-centered perspective in which "conceptualizations imbue spatial structures with meaning." Context awareness, geographic awareness, and ubiquitous cartography or Ubiquitous Geographic Information (UBGI) all contribute to the understanding of spatial contextual awareness. They are also key elements in a map-based, location-based service, or LBS. In cases in which the user interface for the LBS is a map, cartographic design challenges must be addressed in order to effectively communicate the spatial context to the user. Spatial contextual awareness can describe present context – the environment of the user at the present time and location, or that of a future context – where the user wants to go and what may be of interest to them in the approaching spatial environment. Some location-based services are proactive systems which can anticipate future context.

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Publications associées (2)

Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo

Pascal Fua, Engin Türetken, David Honzátko

The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly difficult for non-convex real-world surfaces. State-of-the-art methods for calibrated photometric stereo address these issues using convolutional neural networks (CNNs) that primarily aim to capture either the spatial context among adjacent pixels or the photometric one formed by illuminating a sample from adjacent directions. In this paper, we bridge these two objectives and introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. In contrast to existing approaches that rely on standard 2D CNNs and regress directly to surface normals, we argue that using separable 4D convolutions and regressing to 2D Gaussian heat-maps severely reduces the size of the network and leads to more stable predictions. Our experimental results on a real-world photometric stereo benchmark show that the proposed approach outperforms the existing published methods in accuracy. The source code for our method is available at https://github.com/DawyD/UNet-PS-4D.

IEEE COMPUTER SOC2021

Context-aware Mobile Assistants for Optimal Interaction: a prototype for supporting the business traveler

2004