Pattern-recognition image processing of 3D atom-probe data

1994 ◽  
Vol 52 ◽  
pp. 832-833
Author(s):  
Patrick P. Camus ◽  
David J. Larson ◽  
Thomas F. Kelly
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Lattice Structure ◽  
A Priori ◽  
Three Dimensional ◽  
Empirical Work ◽  
Atom Probe ◽  
Theoretical Description ◽  
Pattern Recognition Techniques ◽  
Near Future

The ultimate three-dimensional atom probe (3DAP) system would have sufficient spatial resolution so that the crystal structure of a material could be determined directly from the atomic positions. Aberrations in the trajectories of ions evaporated from the specimen are the primary limitation on the lateral resolution of AP analysis. In the near future, it does not seem likely that these aberrationsmay be corrected physically because there is no theoretical description and there has been very little empirical work. If the lattice is known a priori, a suggestion was proposed to force the atoms to their nearest lattice sites. This work reports progress that has been made using Fourier transform (FT) and pattern recognition techniques to reconstruct an original lattice structure from simulated 3DAPdata and subsequently to force atoms to pick their nearest lattice point. Usually FT techniques areused in image processing to reduce the image noise, not actually to shift features in the image.

ANALYSIS OF MORPHOLOGICAL GROUPINGS USING ANOPA, A PATTERN RECOGNITION AND MULTIVARIATE STATISTICAL METHOD: A CASE STUDY INVOLVING CENTRARCHID FISHES

2004 ◽  
Vol 12 (02) ◽  
pp. 137-167 ◽  
Author(s):  
DAVID P. CAVANAUGH ◽  
RICHARD V. STERNBERG
Keyword(s):  
Pattern Recognition ◽  
Statistical Analysis ◽  
A Priori ◽  
Three Dimensional ◽  
Discrete Subgroups ◽  
Multivariate Statistical ◽  
Fish Family ◽  
Cohesive Group ◽  
Statistical Analysis Method ◽  
Taxonomic Groups

Morphological relationships within and among taxonomic groups can be very complicated, with anatomical data often supporting two or more incongruent groupings. One possibility is that incongruent character states are taxonomically informative, although in an N-dimensional taxic space. To test the above, morphological relationships of centrarchid fish species were examined using a new pattern recognition, multivariate correlation, and multivariate statistical analysis method (ANOPA). The objective of ANOPA is to identify N-dimensional pattern space correlations among character states, relations that cannot be detected with standard phenetic or phylogenetic approaches. ANOPA provides a solution to an inherent weakness in statistical analysis which occurs in the face of set classification ambiguity, where there is no a priori reason to assign a membership or class identification within multivariate statistical groups. This approach revealed the percoid fish family Centrarchidae to be a statistically significant, cohesive group with complicated internal relationships. Centrarchid taxa are resolved into three major generic aggregates by two and three-dimensional ANOPA, and discrete subgroups were also detected. The complex interrelationships within the Centrarchidae cannot be readily collapsed to a bifurcating tree-structure, explaining the multitude of conflicting phylogenetic hypotheses that have been presented. This is the first robust study of anatomical disparity in teleostean fishes. Applications of ANOPA to the study of morphological gaps, complex taxonomic patterns, and anatomical disparity are discussed.

Interpretation of Raman Spectra of Nitro-Containing Explosive Materials. Part II: The Implementation of Neural, Fuzzy, and Statistical Models for Unsupervised Pattern Recognition

1997 ◽  
Vol 51 (12) ◽  
pp. 1868-1879 ◽  
Author(s):  
Nelson W. Daniel ◽  
Ian R. Lewis ◽  
Peter R. Griffiths
Keyword(s):  
Pattern Recognition ◽  
Raman Spectra ◽  
Fuzzy Clustering ◽  
Statistical Models ◽  
Structural Information ◽  
A Priori ◽  
Data Set ◽  
Pattern Recognition Techniques ◽  
Explosive Materials ◽  
Neural Fuzzy

The implementation of neural, fuzzy, and statistical models for the unsupervised pattern recognition and clustering of Fourier transform (FT)-Raman spectra of explosive materials is reported. In this work a statistical pattern recognition technique based on the concept of nearest-neighbors classification is described. Also the first application of both fuzzy clustering and a fuzzified Kohonen clustering network for the analysis of vibrational spectra is presented. Fuzzified Kohonen networks were found to perform as well as or better than the traditional fuzzy clustering technique. The unsupervised pattern recognition techniques, without the need for a priori structural information, yielded results which were comparable with those obtained by using a combination of a priori structural information and manual group-frequency analysis. This work demonstrates, via the use of a nitro-containing explosive data set, the utility of unsupervised pattern recognition techniques for the clustering, novelty detection, prototyping, and feature mapping of Raman spectra. The results of this work are directly applicable to the characterization of Raman spectra of explosives recorded with fiber-optic sampling.

scholarly journals Implementasi Algoritma Apriori Terhadap Data Penjualan Oli Mobil Berbasis Sistem Pencarian Aturan Asosiasi

2019 ◽  
Vol 3 (2) ◽  
pp. 316
Author(s):  
Jorza Rulianto ◽  
Wida Prima Mustika
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Software Testing ◽  
A Priori ◽  
Marketing Strategies ◽  
Motor Oil ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Pattern Recognition Techniques ◽  
Mathematical Techniques

Data mining techniques are used to design effective sales or marketing strategies by utilizing sales transaction data that is already available in the company. The problem in the company is that there are many data transactions that occur unknown, causing an accumulation of data unknown sales most in each month & year, unknown brands of car oil are often sold or demanded by customers. So this association search uses a priori algorithm as a place to store data using pattern recognition techniques such as static and mathematical techniques from a set of relationships (associations) between items obtained, it is expected that can help developers in designing marketing strategies for goods in the company. Software testing results that have been made have found the most sold oil brand products if you buy Shell Hx7, it will buy Toyota Motor Oil with 50% support and 66.7% confidence. If you buy Toyota Motor Oil, you will buy Shell Hx 7 with 50% support and 85.7% confidence.

Inspection of tire tread defects using image processing and pattern recognition techniques

1993 ◽  
Author(s):  
Penny Chen ◽  
Gary D. Shubinsky ◽  
Kwan-Hwa Jan ◽  
Chien-An Chen ◽  
Oliver Sidla ◽  
...  
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Pattern Recognition Techniques

Mapping molecular landscapes inside cells

2004 ◽  
Vol 385 (10) ◽  
pp. 865-872 ◽  
Author(s):  
Wolfgang Baumeister
Keyword(s):  
Pattern Recognition ◽  
Biological Samples ◽  
Three Dimensional ◽  
Three Dimensional Imaging ◽  
Pattern Recognition Techniques ◽  
Molecular Landscape

AbstractCryoelectron tomography opens a window into the inner space of cells. It combines the potential of three-dimensional imaging with a close-to-life preservation of biological samples. Tomograms with molecular resolution are essentially images of the cellular proteome and, in conjunction with advanced pattern recognition techniques, they can be used to map the molecular landscape inside organelles and cells.

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