Granular Computing Techniques for Bioinformatics Pattern Recognition Problems in Non-metric Spaces

2018 ◽  
pp. 53-81 ◽  
Author(s):  
Alessio Martino ◽  
Alessandro Giuliani ◽  
Antonello Rizzi
Keyword(s):  
Pattern Recognition ◽  
Granular Computing ◽  
Metric Spaces

scholarly journals Topological pattern recognition for point cloud data

Acta Numerica ◽  
2014 ◽  
Vol 23 ◽  
pp. 289-368 ◽  
Author(s):  
Gunnar Carlsson
Keyword(s):  
Pattern Recognition ◽  
Algebraic Topology ◽  
Point Cloud ◽  
Metric Spaces ◽  
Persistent Homology ◽  
Topological Spaces ◽  
Data Sets ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Finite Metric Spaces

In this paper we discuss the adaptation of the methods of homology from algebraic topology to the problem of pattern recognition in point cloud data sets. The method is referred to aspersistent homology, and has numerous applications to scientific problems. We discuss the definition and computation of homology in the standard setting of simplicial complexes and topological spaces, then show how one can obtain useful signatures, called barcodes, from finite metric spaces, thought of as sampled from a continuous object. We present several different cases where persistent homology is used, to illustrate the different ways in which the method can be applied.

An Approach to Pattern Recognition Based on Hierarchical Granular Computing

2013 ◽  
Vol 127 (1-4) ◽  
pp. 369-384 ◽  
Author(s):  
Sinh Hoa Nguyen ◽  
Tuan Trung Nguyen ◽  
Marcin Szczuka ◽  
Hung Son Nguyen
Keyword(s):  
Pattern Recognition ◽  
Granular Computing

Pattern Recognition Based Dynamics Description of Production Processes in Metric Spaces

2017 ◽  
Vol 19 (4) ◽  
pp. 1595-1607 ◽  
Author(s):  
Mushu Wang ◽  
Zhengguang Xu ◽  
Lingli Guo
Keyword(s):  
Pattern Recognition ◽  
Metric Spaces ◽  
Production Processes

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.

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