Search results for "matching"

showing 10 items of 509 documents

SIFT Matching by Context Exposed

2023

This paper investigates how to step up local image descriptor matching by exploiting matching context information. Two main contexts are identified, originated respectively from the descriptor space and from the keypoint space. The former is generally used to design the actual matching strategy while the latter to filter matches according to the local spatial consistency. On this basis, a new matching strategy and a novel local spatial filter, named respectively blob matching and Delaunay Triangulation Matching (DTM) are devised. Blob matching provides a general matching framework by merging together several strategies, including rank-based pre-filtering as well as many-to-many and symmetri…

FOS: Computer and information sciencesArtificial neural networkSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniBenchmark testingRANSAClocal image descriptorSettore INF/01 - InformaticaApplied MathematicsComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionTransformDetectorDelaunay triangulationMerginglocal spatial filterimage contextComputational Theory and MathematicsArtificial IntelligenceKeypoint matchingSIFTPipelineTrainingComputer Vision and Pattern RecognitionSoftware
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Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks

2020

Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites. Matching the landmark accurately is of paramount relevance, and the process can be strongly impacted by the cloud contamination of a given landmark. This paper introduces a complete pattern recognition methodology able to detect the presence of clouds over landmarks using Meteosat Second Generation (MSG) data. The methodology is based on the ensemble combination of dedicated support vector machines (SVMs) dependent…

FOS: Computer and information sciencesAtmospheric ScienceMatching (statistics)Computer Science - Machine LearningSource code010504 meteorology & atmospheric sciencesComputer scienceComputer Vision and Pattern Recognition (cs.CV)media_common.quotation_subjectMultispectral image0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputer Science - Computer Vision and Pattern RecognitionCloud computing02 engineering and technology01 natural sciencesMachine Learning (cs.LG)Computers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesmedia_commonLandmarkbusiness.industryPattern recognitionSupport vector machinePattern recognition (psychology)Geostationary orbitArtificial intelligencebusiness
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REDUCTION OF CONSTRAINT SYSTEMS

1993

Geometric modeling by constraints leads to large systems of algebraic equations. This paper studies bipartite graphs underlaid by systems of equations. It shows how these graphs make possible to polynomially decompose these systems into well constrained, over-, and underconstrained subsystems. This paper also gives an efficient method to decompose well constrained systems into irreducible ones. These decompositions greatly speed up the resolution in case of reducible systems. They also allow debugging systems of constraints.

FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)bipartite graphsmatchingperfect matching[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]maximum matching[INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG]geometric modelingComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONFOS: Mathematics[ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG]Mathematics - CombinatoricsCombinatorics (math.CO)constraintsComputer Science - Discrete Mathematics
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Generating a Gray code for prefix normal words in amortized polylogarithmic time per word

2020

A prefix normal word is a binary word with the property that no substring has more $1$s than the prefix of the same length. By proving that the set of prefix normal words is a bubble language, we can exhaustively list all prefix normal words of length $n$ as a combinatorial Gray code, where successive strings differ by at most two swaps or bit flips. This Gray code can be generated in $\Oh(\log^2 n)$ amortized time per word, while the best generation algorithm hitherto has $\Oh(n)$ running time per word. We also present a membership tester for prefix normal words, as well as a novel characterization of bubble languages.

FOS: Computer and information sciencesGeneral Computer ScienceFormal Languages and Automata Theory (cs.FL)Property (programming)combinatorial Gray codeComputer Science - Formal Languages and Automata TheoryData_CODINGANDINFORMATIONTHEORY0102 computer and information sciences02 engineering and technologyCharacterization (mathematics)01 natural sciencesTheoretical Computer ScienceCombinatoricsSet (abstract data type)Gray codeComputer Science - Data Structures and Algorithms0202 electrical engineering electronic engineering information engineeringData Structures and Algorithms (cs.DS)MathematicsAmortized analysisSettore INF/01 - Informaticaprefix normal wordsSubstringcombinatorial generationPrefixjumbled pattern matching010201 computation theory & mathematics020201 artificial intelligence & image processingbinary languagesprefix normal words binary languages combinatorial Gray code combinatorial generation jumbled pattern matchingWord (computer architecture)Theoretical Computer Science
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ALGORITHMS FOR JUMBLED PATTERN MATCHING IN STRINGS

2011

The Parikh vector p(s) of a string s is defined as the vector of multiplicities of the characters. Parikh vector q occurs in s if s has a substring t with p(t)=q. We present two novel algorithms for searching for a query q in a text s. One solves the decision problem over a binary text in constant time, using a linear size index of the text. The second algorithm, for a general finite alphabet, finds all occurrences of a given Parikh vector q and has sub-linear expected time complexity; we present two variants, which both use a linear size index of the text.

FOS: Computer and information sciencesJ.3average case analysis.Binary numberaverage case analysispermuted stringpermuted stringsComputer Science - Data Structures and AlgorithmsComputer Science (miscellaneous)Parikh vectorData Structures and Algorithms (cs.DS)Pattern matchingTime complexityMathematicsString (computer science)Parikh vectorsstring algorithmDecision problemstring algorithmsSubstringParikh vectors; permuted strings; pattern matching; string algorithms; average case analysisF.2.2; J.3Index (publishing)pattern matchingF.2.2Constant (mathematics)AlgorithmComputer Science::Formal Languages and Automata Theory
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On prefix normal words and prefix normal forms

2016

A $1$-prefix normal word is a binary word with the property that no factor has more $1$s than the prefix of the same length; a $0$-prefix normal word is defined analogously. These words arise in the context of indexed binary jumbled pattern matching, where the aim is to decide whether a word has a factor with a given number of $1$s and $0$s (a given Parikh vector). Each binary word has an associated set of Parikh vectors of the factors of the word. Using prefix normal words, we provide a characterization of the equivalence class of binary words having the same set of Parikh vectors of their factors. We prove that the language of prefix normal words is not context-free and is strictly contai…

FOS: Computer and information sciencesPrefix codePrefix normal wordPre-necklaceDiscrete Mathematics (cs.DM)General Computer ScienceFormal Languages and Automata Theory (cs.FL)Binary numberComputer Science - Formal Languages and Automata TheoryContext (language use)Binary languageLyndon words0102 computer and information sciences02 engineering and technologyPrefix grammarprefix normal formsKraft's inequalityCharacterization (mathematics)Lyndon word01 natural sciencesPrefix normal formenumerationTheoretical Computer ScienceFOS: Mathematics0202 electrical engineering electronic engineering information engineeringMathematics - CombinatoricsMathematicsDiscrete mathematicsprefix normal words prefix normal forms binary languages binary jumbled pattern matching pre-necklaces Lyndon words enumerationbinary jumbled pattern matchingSettore INF/01 - InformaticaComputer Science (all)pre-necklacesComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)prefix normal wordsPrefix010201 computation theory & mathematics020201 artificial intelligence & image processingCombinatorics (math.CO)binary languagesComputer Science::Formal Languages and Automata TheoryWord (group theory)Computer Science - Discrete MathematicsTheoretical Computer Science
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Binary jumbled string matching for highly run-length compressible texts

2012

The Binary Jumbled String Matching problem is defined as: Given a string $s$ over $\{a,b\}$ of length $n$ and a query $(x,y)$, with $x,y$ non-negative integers, decide whether $s$ has a substring $t$ with exactly $x$ $a$'s and $y$ $b$'s. Previous solutions created an index of size O(n) in a pre-processing step, which was then used to answer queries in constant time. The fastest algorithms for construction of this index have running time $O(n^2/\log n)$ [Burcsi et al., FUN 2010; Moosa and Rahman, IPL 2010], or $O(n^2/\log^2 n)$ in the word-RAM model [Moosa and Rahman, JDA 2012]. We propose an index constructed directly from the run-length encoding of $s$. The construction time of our index i…

FOS: Computer and information sciencesString algorithmsStructure (category theory)Binary numberG.2.1Data_CODINGANDINFORMATIONTHEORY0102 computer and information sciences02 engineering and technologyString searching algorithm01 natural sciencesComputer Science - Information RetrievalTheoretical Computer ScienceCombinatoricsdata structuresSimple (abstract algebra)Computer Science - Data Structures and AlgorithmsString algorithms; jumbled pattern matching; prefix normal form; data structures0202 electrical engineering electronic engineering information engineeringParikh vectorData Structures and Algorithms (cs.DS)Run-length encodingMathematics68W32 68P05 68P20String (computer science)prefix normal formSubstringComputer Science Applicationsjumbled pattern matching010201 computation theory & mathematicsData structureSignal ProcessingRun-length encoding020201 artificial intelligence & image processingConstant (mathematics)Information Retrieval (cs.IR)Information SystemsInformation Processing Letters
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Languages with mismatches and an application to approximate indexing

2005

In this paper we describe a factorial language, denoted by L(S, k,r), that contains all words that occur in a string 5 up to k mismatches every r symbols. Then we give some combinatorial properties of a parameter, called repetition index and denoted by R(S,k,r), defined as the smallest integer h ? 1 such that all strings of this length occur at most in a unique position of the text S up to k mismatches every r symbols. We prove that R(S, k, r) is a non-increasing function of r and a non-decreasing function of k and that the equation r = R(S, k, r) admits a unique solution. The repetition index plays an important role in the construction of an indexing data structure based on a trie that rep…

FactorialCombinatorics on wordsString (computer science)Function (mathematics)formal languagesmatching indexingCombinatoricsCombinatorics on wordsIntegerapproximate stringPosition (vector)TrieAlgorithmWord (group theory)Mathematics
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Generazione di Seconda Armonica per Quasi-Phase-Matching Random in Tantalato di Litio con Poling Periodico

2009

We experimentally observed and explained bulk Second Harmonic Generation via Random Quasi-Phase-Matching, derived from a periodically poled Lithium Tantalate sample with a randomly patterned mark-to-space-ratio.

Ferroelectric MaterialsRandom Quasi-Phase-MatchingSecond Harmonic Generation
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Financing Unemployment Benefits: Dismissal versus Employment Taxes

2006

This paper investigates the effects of using dismissal taxes to finance unemployment benefits. We compare dismissal and employment taxes in a model with search frictions. Employment taxes give rise to externalities because firms do not take into account the effects their dismissal decisions have on others. By introducing dismissal taxes to finance unemployment insurance, these externalities can partly be internalized. Taking into account the budget of the unemployment insurance, employment taxes can be reduced by more than necessary to offset the adverse effect of dismissal taxes on the firm value. The introduction of dismissal taxes leads to higher job creation and lower unemployment, in c…

FinanceJob creationLabour economicsFull employmentbusiness.industrymedia_common.quotation_subjectGeography Planning and Developmentemployment protection search and matching models unemployment unemployment insurancejel:J64jel:J65jel:J41jel:J68DismissalUnemploymentValue (economics)EconomicsbusinessExternalityDemographymedia_commonLabour
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