An easy way for graph recognition algorithms is to use a two-step process: first, compute a characteristic feature as if the graph belongs to that class;second, check whether the computed feature really defines the in...
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An easy way for graph recognition algorithms is to use a two-step process: first, compute a characteristic feature as if the graph belongs to that class;second, check whether the computed feature really defines the input graph. Although in some cases the two steps can be merged, separating them may yield new and much more easily understood algorithms. In this paper we apply that paradigm to the cograph and distance hereditary graph recognition problems. (C) 2001 Elsevier Science BN. All rights reserved.
Molecular biology which aims to study DNA and protein structure and functions, has stimulated research in different scientific disciplines, discrete mathematics being one of them. One of the problems considered is tha...
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Molecular biology which aims to study DNA and protein structure and functions, has stimulated research in different scientific disciplines, discrete mathematics being one of them. One of the problems considered is that of recognition of DNA primary structure. It is known that some methods for solving this problem may be reduced (in their computational part) to graph-theoretic problems involving labeled graphs. Each vertex in such graphs has a label of length k written with an alphabet of size alpha, where k and alpha are two parameters. This paper is concerned with studying propel ties of these graphs (referred to as DNA graphs). More precisely, we give recognition algorithms and compare graphs labeled with different values of k and alpha. (C) 1999 Elsevier Science B.V. All rights reserved.
In this paper we show an O(m) time recognition algorithm for a class of graphs named Strict 2-Threshold that have threshold number 2. Our algorithm improves the previously known O(m(2)) algorithm and generates the two...
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In this paper we show an O(m) time recognition algorithm for a class of graphs named Strict 2-Threshold that have threshold number 2. Our algorithm improves the previously known O(m(2)) algorithm and generates the two threshold components. The algorithm can be easily adapted to recognize 2-threshold graphs with exactly three cutpoints.
In this paper we present four algorithms developed independently by members of our research team specialized in recognition of unconstrained handwritten numerals. All these methods have high recognition rates and are ...
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In this paper we present four algorithms developed independently by members of our research team specialized in recognition of unconstrained handwritten numerals. All these methods have high recognition rates and are considered experts by our research group. We also present the different ways experimented on for incorporation of these recognition methods into a more powerful system. By combining them we realize that they complement each other in many ways. The resulting multiple-expert system proves that the consensus of these methods tends to compensate for individual weaknesses, while preserving individual strengths. This paper shows that it is possible to reduce the substitution rate to a desired level while maintaining a fairly high recognition rate in the classification of totally unconstrained handwritten ZIP code numerals. Furthermore, if reliability is of the utmost importance, we can avoid substitutions completely (reliability = 100%) and still retain a recognition rate above 90%. In the last part of this paper, we try to compare results given by some of the most effective numeral recognition systems found in the literature.
Characters are recognized from the features extracted. Usually the input chacter is smoothed and cleaned by the preprocessor before it reaches the featuree extractor. Good preprocessors and feature extractors are the ...
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Characters are recognized from the features extracted. Usually the input chacter is smoothed and cleaned by the preprocessor before it reaches the featuree extractor. Good preprocessors and feature extractors are the prerequisites to a successful character recognition system. Following a description of preprocessing techniques, the various features found in the vast accumulation of literature on handprint recognition are divided into two main categories: (1) global analysis, and (2) structural analysis. Further subdivision of these categories gives rise to six families of feature type, viz. (a) distribution of points, (b) transformation, (c) physical measurements, (d) line segments and edges, (e) outline of character, and (f) centre-line of character. Each family is described with illustrative examples. The performance and recognition rates of systems employing these features are discussed. Zeichen werden aufgrund von Merkmalen erkannt. Üblicherweise wird einzu erkennedes Zeichen in der Vorverarbeitungsstufe geglättet und ‘gesäubert’, bevor es den Merkmalsextraktor erreicht. Gute Vorverarbeitungsstufen und Merkmalsextraktoren sind hierbei die Voraussetzung für erfolgreiches Arbeiten eines Zeichenerkennungssystems. In diesem Beitrag werden zunächst einige Prinzipien der Vorverarbeitung beschrieben; hierauf werden die verschiedenen Verarbeitungsmethoden, die sich in dem umfangreichen Schrifttum über die automatische Erkennung handschriftlicher Zeichen finden, in zwei Hauptkategorien eingeteilt: (1) globale Analyse sowie (2) strukturelle Analyse. Eine weitere Unterteilung dieser Kategorien ergibt je nach Verarbeitungsmethode sechs Merkmalsfamilien: (a) Verteilung der Zeichenpunkte, (b) Transformation, (c) physikalische Messungen, (d) Liniensegmente und Ränder, (e) Umriβ sowie (f) Skelett des Zeichens. Jede dieser Merkmalsfamilien wird beschrieben und anhand von Beispielen erklärt. Abschlieβend wird die Wirkungsweise und die Erkennungsrate von Erkennungssystemen diskuti
This paper presents a canonical form for context-sensitive derivations and a parsing algorithm which finds each context-sensitive analysis once and only once. The amount of memory required by the algorithm is essentia...
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This paper presents a canonical form for context-sensitive derivations and a parsing algorithm which finds each context-sensitive analysis once and only once. The amount of memory required by the algorithm is essentially no more than that required to store a single complete derivation. In addition, a modified version of the basic algorithm is presented which blocks infinite analyses for grammars which contain loops. The algorithm is also compared with several previous parsers for context-sensitive grammars and general rewriting systems, and the difference between the two types of analyses is discussed. The algorithm appears to be complementary to an algorithm by S. Kuno in several respects, including the space-time trade-off and the degree of context dependence involved. [ABSTRACT FROM AUTHOR]
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