A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities

Multiple Classifier Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-642-12127-2_17 ◽

2010 ◽

pp. 165-174 ◽

Cited By ~ 9

Author(s):

Alexander Tatarchuk ◽

Eugene Urlov ◽

Vadim Mottl ◽

David Windridge

Keyword(s):

Pattern Recognition ◽

Kernel Machine ◽

Diverse Pattern

Download Full-text

Receptor-Like Cytoplasmic Kinases Directly Link Diverse Pattern Recognition Receptors to the Activation of Mitogen-Activated Protein Kinase Cascades in Arabidopsis

The Plant Cell ◽

10.1105/tpc.17.00981 ◽

2018 ◽

Vol 30 (7) ◽

pp. 1543-1561 ◽

Cited By ~ 57

Author(s):

Guozhi Bi ◽

Zhaoyang Zhou ◽

Weibing Wang ◽

Lin Li ◽

Shaofei Rao ◽

...

Keyword(s):

Pattern Recognition ◽

Protein Kinase ◽

Pattern Recognition Receptors ◽

Mitogen Activated Protein Kinase ◽

Mitogen Activated Protein ◽

Diverse Pattern

Download Full-text

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014484x ◽

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.

Download Full-text

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

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100150800 ◽

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.

Download Full-text