Tree-graph grammars for pattern recognition

2005 ◽  
pp. 349-368 ◽  
Author(s):  
A. Sanfeliu ◽  
K. S. Fu
Keyword(s):  
Pattern Recognition ◽  
Graph Grammars ◽  
Tree Graph

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.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Model-Checking of Infinite Graphs Defined by Graph Grammars

2000 ◽  
Vol 5 ◽  
pp. 1 ◽  
Author(s):  
O BURKART ◽  
Y QUEMENER
Keyword(s):  
Model Checking ◽  
Infinite Graphs ◽  
Graph Grammars

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing
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