Grammatical inference of graph grammars for syntactic pattern recognition

2005 ◽  
pp. 1-7 ◽  
Author(s):  
B. Bartsch-Spörl
Keyword(s):  
Pattern Recognition ◽  
Grammatical Inference ◽  
Graph Grammars ◽  
Syntactic Pattern Recognition ◽  
Syntactic Pattern

ON THE USE OF THE MORPHIC GENERATOR GRAMMATICAL INFERENCE (MGGI) METHODOLOGY IN AUTOMATIC SPEECH RECOGNITION

1990 ◽  
Vol 04 (04) ◽  
pp. 667-685 ◽  
Author(s):  
PEDRO GARCÍA ◽  
ENCARNA SEGARRA ◽  
ENRIQUE VIDAL ◽  
ISABEL GALIANO
Keyword(s):  
Pattern Recognition ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Regular Languages ◽  
Grammatical Inference ◽  
Real Problem ◽  
Syntactic Pattern Recognition ◽  
General Methodology ◽  
Syntactic Pattern

Recently, a new methodology, referred to as “Morphic Generator Grammatical Inference” (MGGI), has been introduced as a step towards a general methodology for the inference of regular languages. In this paper we consider the application of this methodology to a real problem of automatic speech recognition, thus allowing (and also requiring) the proposed problem to be properly formulated within the canonical framework of syntactic pattern recognition. The results show both the viability and appropriateness of the application of MGGI to the problem considered.

Syntactic pattern recognition and Hough transformation for reconstruction of seismic patterns

Geophysics ◽  
1987 ◽  
Vol 52 (12) ◽  
pp. 1612-1620 ◽  
Author(s):  
K. Y. Huang ◽  
K. S. Fu ◽  
S. W. Cheng ◽  
Z. S. Lin
Keyword(s):  
Pattern Recognition ◽  
Second Step ◽  
Grammatical Inference ◽  
Transformation Technique ◽  
Syntactic Pattern Recognition ◽  
Syntax Analysis ◽  
Hough Transformation ◽  
Syntactic Pattern ◽  
Straight Line ◽  
Line Patterns

Hierarchical syntactic pattern recognition and the Hough transformation are proposed for automatic recognition and reconstruction of seismic patterns in seismograms. In the first step, the patterns are hierarchically decomposed or recognized into single patterns, straight‐line patterns, or hyperbolic patterns, using syntactic pattern recognition. In the second step, the Hough transformation technique is used for reconstruction, pattern by pattern. The system of syntactic seismic pattern recognition includes envelope generation, a linking process in the seismogram, segmentation, primitive recognition, grammatical inference, and syntax analysis. The seismic patterns are automatically recognized and reconstructed.

Interpreted Graphs and ETPR(k) Graph Grammar Parsing for Syntactic Pattern Recognition

2019 ◽  
Vol 27 (1) ◽  
pp. 3-19
Author(s):  
Mariusz Flasiński
Keyword(s):  
Pattern Recognition ◽  
Model Theory ◽  
Graph Grammar ◽  
Image Process ◽  
Graph Grammars ◽  
Syntactic Pattern Recognition ◽  
Bottom Up ◽  
Recognition Pattern ◽  
Syntactic Pattern ◽  
Parsing Algorithm

Further results of research into graph grammar parsing for syntactic pattern recognition (Pattern Recognit. 21:623-629, 1988; 23:765-774, 1990; 24:1223-1224, 1991; 26:1-16, 1993; 43:249-2264, 2010; Comput. Vision Graph. Image Process. 47:1-21, 1989; Fundam. Inform. 80:379-413, 2007; Theoret. Comp. Sci. 201:189-231, 1998) are presented in the paper. The notion of interpreted graphs based on Tarski's model theory is introduced. The bottom-up parsing algorithm for ETPR(k) graph grammars is defined.

PATH-CONTROLLED GRAPH GRAMMARS FOR SYNTACTIC PATTERN RECOGNITION

1994 ◽  
Vol 08 (02) ◽  
pp. 485-500
Author(s):  
KUNIO AIZAWA ◽  
AKIRA NAKAMURA
Keyword(s):  
Pattern Recognition ◽  
Graph Structure ◽  
Graph Grammars ◽  
Syntactic Pattern Recognition ◽  
Syntactic Pattern ◽  
Parsing Algorithm

The graph structure is a strong formalism for representing pictures in syntactic pattern recognition. Many models for graph grammars have been proposed as a kind of hyper-dimensional generating systems, whereas the use of such grammars for pattern recognition is relatively infrequent. One of the reasons is the difficulty of building a syntax analyzer for such graph grammars. In this paper, we define a subclass of nPCE graph grammars and present a parsing algorithm of O(n) for both sequential and parallel cases.

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