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Institution

SRI International

NonprofitMenlo Park, California, United States
About: SRI International is a(n) nonprofit organization based out in Menlo Park, California, United States. It is known for research contribution in the topic(s): Ionosphere & Incoherent scatter. The organization has 7222 authors who have published 13102 publication(s) receiving 660724 citation(s). The organization is also known as: Stanford Research Institute & SRI.
Papers
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Journal ArticleDOI
Martin A. Fischler1, Robert C. Bolles1Institutions (1)
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Abstract: A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

20,503 citations


Journal ArticleDOI
Thomas M. Cover1, Peter E. Hart2Institutions (2)
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Abstract: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.

10,453 citations


Journal ArticleDOI
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
Abstract: Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.

8,780 citations


Journal ArticleDOI
Richard O. Duda1, Peter E. Hart1Institutions (1)
TL;DR: It is pointed out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further, and how the method can be used for more general curve fitting.
Abstract: Hough has proposed an interesting and computationally efficient procedure for detecting lines in pictures. This paper points out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further. It also shows how the method can be used for more general curve fitting, and gives alternative interpretations that explain the source of its efficiency.

6,225 citations


Book ChapterDOI
Abstract: Reliable computer systems must handle malfunctioning components that give conflicting information to different parts of the system. This situation can be expressed abstractly in terms of a group of generals of the Byzantine army camped with their troops around an enemy city. Communicating only by messenger, the generals must agree upon a common battle plan. However, one or more of them may be traitors who will try to confuse the others. The problem is to find an algorithm to ensure that the loyal generals will reach agreement. It is shown that, using only oral messages, this problem is solvable if and only if more than two-thirds of the generals are loyal; so a single traitor can confound two loyal generals. With unforgeable written messages, the problem is solvable for any number of generals and possible traitors. Applications of the solutions to reliable computer systems are then discussed.

4,834 citations


Authors

Showing all 7222 results

NameH-indexPapersCitations
Rodney S. Ruoff164666194902
Alex Pentland13180998390
Robert L. Byer130103696272
Howard I. Maibach116182160765
Alexander G. G. M. Tielens11572251058
Adolf Pfefferbaum10953040358
Amato J. Giaccia10841949876
Bernard Wood10863038272
Paul Workman10254738095
Thomas Kailath10266158069
Pascal Fua10261449751
Edith V. Sullivan10145534502
Margaret A. Chesney10132633509
Thomas C. Merigan9851433941
Carlos A. Zarate9741732921
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20224
2021178
2020223
2019256
2018218
2017262