Identification of cloud fields by the nonparametric algorithm of pattern recognition from normalized video data recorded with the AVHRR instrument

2002 ◽  
Author(s):  
Konstantin T. Protasov ◽  
Tatyana Y. Pushkareva ◽  
Evgeny S. Artamonov
Keyword(s):  
Pattern Recognition ◽  
Video Data ◽  
Nonparametric Algorithm

Research of Classification Retrieval Technology in Remote Video Communication Network

2013 ◽  
Vol 859 ◽  
pp. 477-481
Author(s):  
Yun Wang ◽  
Cui Huan Yang
Keyword(s):  
Communication Network ◽  
Pattern Recognition ◽  
Traditional Method ◽  
Reference Value ◽  
Video Communication ◽  
Video Data ◽  
Transmission Mode ◽  
Adaboost Algorithm ◽  
Remote Verification ◽  
Network Transmission

This paper studies a new method of the classification retrieval technology in the remote video communication network. In order to make the huge video data can be transmitted in the communication network, this paper puts forward a new idea, a method of increased middleware based on the traditional method of C/S network transmission mode, and combined with advanced AdaBoost algorithm in the field of pattern recognition, to launched in-depth research for the classification retrieval technology in the remote video. This research has a certain reference value to the remote verification system.

scholarly journals Nonparametric algorithm of electronic components test data pattern recognition

2019 ◽  
Vol 537 ◽  
pp. 042021
Author(s):  
N V Koplyarova ◽  
E A Chzhan ◽  
A V Medvedev ◽  
A A Korneeva ◽  
A V Raskina ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Test Data ◽  
Electronic Components ◽  
Nonparametric Algorithm

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.

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