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Testing Geometric Properties of Two-...
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ProQuest Information and Learning Co.
Testing Geometric Properties of Two-Dimensional Figures and Images.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Testing Geometric Properties of Two-Dimensional Figures and Images./
作者:
Murzabulatov, Meiram.
面頁冊數:
1 online resource (111 pages)
附註:
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:
Dissertation Abstracts International79-04B(E).
標題:
Computer science. -
電子資源:
click for full text (PQDT)
ISBN:
9780355331240
Testing Geometric Properties of Two-Dimensional Figures and Images.
Murzabulatov, Meiram.
Testing Geometric Properties of Two-Dimensional Figures and Images.
- 1 online resource (111 pages)
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Thesis (Ph.D.)--The Pennsylvania State University, 2017.
Includes bibliographical references
We conduct a systematic study of sublinear-time algorithms for geometric properties of images. We investigate three fundamental properties: being a half-plane, convexity, and connectedness. For all three properties, we study the query and time complexity of property testing and tolerant property testing in different variants of the models: with different types of access to the input and restrictions on the algorithms.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355331240Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
Testing Geometric Properties of Two-Dimensional Figures and Images.
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520
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A property tester is adaptive if its queries depend on the answers to its previous queries. Otherwise, the tester is nonadaptive. For the half-plane property, we give the first nonadaptive property tester whose running time and query complexity is optimal. For convexity, we design the first adaptive tester with optimal query and time complexity. We also improve the previously known nonadaptive tester for this property. For connectedness, we design the first nonadaptive tester and improve the previously known adaptive tester.
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We design algorithms that approximate the distance to the three properties within a small additive error or, equivalently, tolerant testers for being a half-plane, convexity, and connectedness. Previously, no tolerant testing algorithms were known for these properties. Designing tolerant testers for image properties is important, since images are often noisy and tolerant testers are robust to noise.
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