Titlebook: Distributed Video Sensor Networks; Bir Bhanu,Chinya V. Ravishankar,Demetri Terzopoulo Book 2011 Springer-Verlag London Limited 2011 Camera

革新

同音

Multiple-View Object Recognition in Smart Camera Networks simultaneously recover the multiple-view object features based on a distributed compressive sensing theory. The system has been implemented on the Berkeley CITRIC smart camera platform. The efficacy of the algorithm is validated through extensive simulation and experiment.

Asseverate

包租車船

Virtual Visionke virtual pedestrians. The easily reconfigurable virtual cameras distributed in this environment generate synthetic video feeds that emulate those acquired by real surveillance cameras monitoring public spaces. The novel multi-camera control strategies that we describe enable the cameras to collabo

憤怒歷史

Virtualization and Programming Support for?Video Sensor Networks with Application to?Wireless and Phwhile high-level languages, compilers and execution environments allow novice SN programmers to compose SN service logic, unaware of the lower-level components on which their services rely. Concrete examples are provided to illustrate the power and potential of Wireless Security Services that span b

產(chǎn)生

Ubiquitous Displays: A Distributed Network of?Active Displaysan deviate considerably from planar, white and Lambertian—with multiple active displays; (b) provide scalability and reconfigurability (in terms of scale, resolution and form factor) of displays; (c) provide a framework for shared viewing and interaction modalities for multiple users.

騙子

Embolic-Stroke

Comedienne

Toward Robust Online Visual Trackingn computer vision. Robust cognitive visual tracking algorithms facilitate answering important questions regarding how objects move and interact in complex environments. They have broad applications including surveillance, navigation, human computer interfaces, object recognition, motion analysis and video indexing, to name a few.

overweight

Use of Context in Video Processing to information obtained and accumulated from the user. Both types of context can include static or dynamic contextual elements. Examples from a smart home environment are presented to illustrate how different types of contextual data can be applied to aid the decision-making process.