A Benchmark Dataset for Repetitive Pattern Recognition on Textured 3D Surfaces

2021 ◽  
Vol 40 (5) ◽  
pp. 1-8
Author(s):  
Stefan Lengauer ◽  
Ivan Sipiran ◽  
Reinhold Preiner ◽  
Tobias Schreck ◽  
Benjamin Bustos
Keyword(s):  
Pattern Recognition ◽  
Benchmark Dataset ◽  
Repetitive Pattern ◽  
3D Surfaces

The Design of a Universal Infrared Remote Repeater

2012 ◽  
Vol 466-467 ◽  
pp. 1100-1103
Author(s):  
Hong Shan Nie ◽  
Qiang Liu ◽  
Miao Li ◽  
Qing Jiang Li ◽  
Hai Jun Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Remote Control ◽  
Vector Quantization ◽  
Control Signal ◽  
Good Prospect ◽  
Clock Signal ◽  
Storage Efficiency ◽  
Pattern Clustering ◽  
Decoding Efficiency ◽  
Repetitive Pattern

In this paper, a common infrared remote control transmitter is designed, using which, a variety of infrared remote control signal can be decoded, stored and transmitted when users need. And with this design, the problem that remote controllers can not be universally operated is solved . In the design, the algorithm of pattern clustering is used to reduce the errors caused by the instability of clock signal. Moreover, the method of vector Quantization coding and repetitive pattern recognition is used to compress he remote control signal data which achieve a good decoding efficiency and a storage efficiency. Experiments show that the design is feasible and has a good prospect in application.

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.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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