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Andrew Lippman
Researcher at Massachusetts Institute of Technology
Publications - 109
Citations - 8159
Andrew Lippman is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Image retrieval & Image segmentation. The author has an hindex of 34, co-authored 105 publications receiving 7571 citations.
Papers
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Journal ArticleDOI
A simple Cooperative diversity method based on network path selection
TL;DR: A novel scheme that first selects the best relay from a set of M available relays and then uses this "best" relay for cooperation between the source and the destination and achieves the same diversity-multiplexing tradeoff as achieved by more complex protocols.
Proceedings ArticleDOI
MedRec: Using Blockchain for Medical Data Access and Permission Management
TL;DR: This paper proposes MedRec: a novel, decentralized record management system to handle EMRs, using blockchain technology, and incentivizes medical stakeholders to participate in the network as blockchain “miners”, enabling the emergence of data economics.
Proceedings ArticleDOI
Movie-maps: An application of the optical videodisc to computer graphics
TL;DR: An interactive, dynamic map has been built using videodisc technology to engage the user in a simulated “drive” through an unfamiliar space, and to incorporate optical and electronic image processing to provide a more responsive, visually complete representation of an environment.
Patent
A receiver-compatible enhanced definition television system
TL;DR: In this paper, a television encoder for generating a receiver-compatible signal from a production video signal containing enhancement information which cannot be displayed on a standard receiver, the receiver compatible signal being compatible with the standard receiver.
Journal ArticleDOI
Statistical models of video structure for content analysis and characterization
Nuno Vasconcelos,Andrew Lippman +1 more
TL;DR: This paper develops a Bayesian formulation for the shot segmentation problem that is shown to extend the standard thresholding model in an adaptive and intuitive way, leading to improved segmentation accuracy.