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Institution

Carnegie Mellon University

EducationPittsburgh, Pennsylvania, United States
About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.


Papers
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Journal ArticleDOI
TL;DR: A provably-correct algorithm is given, called Space Carving, for computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints to capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints.
Abstract: In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.

1,487 citations

Proceedings ArticleDOI
01 Sep 1987
TL;DR: A variation to Guttman’s Rtrees (R+-trees) that avoids overlapping rectangles in intermediate nodes of the tree is introduced and analytical results indicate that R+-Trees achieve up to 50% savings in disk accesses compared to an R-tree when searching files of thousands of rectangles.
Abstract: The problem of indexing multidimensional objects is considered. First, a classification of existing methods is given along with a discussion of the major issues involved in multidimensional data indexing. Second, a variation to Guttman’s Rtrees (R+-trees) that avoids overlapping rectangles in intermediate nodes of the tree is introduced. Algorithms for searching, updating, initial packing and reorganization of the structure are discussed in detail. Finally, we provide analytical results indicating that R+-trees achieve up to 50% savings in disk accesses compared to an R-tree when searching files of thousands of rectangles.

1,481 citations

Proceedings ArticleDOI
01 Jan 1998
TL;DR: The MaximalMarginal Relevance (MMR) criterion as mentioned in this paper aims to reduce redundancy while maintaining query relevance in retrieving retrieved documents and selecting appropriate passages for text summarization.
Abstract: This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection.

1,479 citations

Book
01 Sep 2002
TL;DR: This lecture maps the concepts and templates explored in this tutorial with well-known architectural prescriptions, including the 4+1 approach of the Rational Unified Process, the Siemens Four Views approach, and the ANSI/IEEE-1471-2000 recommended best practice for documenting architectures for software-intensive systems.
Abstract: This lecture maps the concepts and templates explored in this tutorial with well-known architectural prescriptions, including the 4+1 approach of the Rational Unified Process, the Siemens Four Views approach, and the ANSI/IEEE-1471-2000 recommended best practice for documenting architectures for software-intensive systems. The lecture concludes by re-capping the highlights of the tutorial, and asking for feedback.

1,476 citations

Proceedings ArticleDOI
01 Nov 1996
TL;DR: This document specifies Mobile IPv6, a protocol which allows nodes to remain reachable while moving around in the IPv6 Internet, and defines a new IPv6 protocol and a new destination option.
Abstract: This document specifies a protocol which allows nodes to remain reachable while moving around in the IPv6 Internet. Each mobile node is always identified by its home address, regardless of its current point of attachment to the Internet. While situated away from its home, a mobile node is also associated with a care-of address, which provides information about the mobile node's current location. IPv6 packets addressed to a mobile node's home address are transparently routed to its care-of address. The protocol enables IPv6 nodes to cache the binding of a mobile node's home address with its care-of address, and to then send any packets destined for the mobile node directly to it at this care-of address. To support this operation, Mobile IPv6 defines a new IPv6 protocol and a new destination option. All IPv6 nodes, whether mobile or stationary can communicate with mobile nodes.

1,470 citations


Authors

Showing all 36645 results

NameH-indexPapersCitations
Yi Chen2174342293080
Rakesh K. Jain2001467177727
Robert C. Nichol187851162994
Michael I. Jordan1761016216204
Jasvinder A. Singh1762382223370
J. N. Butler1722525175561
P. Chang1702154151783
Krzysztof Matyjaszewski1691431128585
Yang Yang1642704144071
Geoffrey E. Hinton157414409047
Herbert A. Simon157745194597
Yongsun Kim1562588145619
Terrence J. Sejnowski155845117382
John B. Goodenough1511064113741
Scott Shenker150454118017
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023120
2022499
20214,980
20205,375
20195,420
20184,972