Stocks of Images

Stephan Feuchtwang

doi:10.3167/sa.2021.650207

Stocks of Images

Social Analysis ◽

10.3167/sa.2021.650207 ◽

2021 ◽

Vol 65 (2) ◽

pp. 124-138

Author(s):

Stephan Feuchtwang

Keyword(s):

Pattern Recognition ◽

Recent History ◽

Object Of Knowledge

What kind of knowledge is created through systems of divination? I will contend that the form of such knowledge is a type of pattern recognition—patterns that emerge in reference to a cosmology and by means of a stock of images. Divination creates knowledge of a moment and its circumstances. Reference to a sense of the encompassing world raises the issue of how any one means of divination and its outcomes is bound historically to a civilization. That will be my secondary topic of reflection. I will conclude with a discussion of worlds, recent history, speculation, and the ontology of divination in relation to the experience of uncertainty in which the object of knowledge is the momentary and its circumstances.

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GRAPH MATCHING AND LEARNING IN PATTERN RECOGNITION IN THE LAST 10 YEARS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001414500013 ◽

2014 ◽

Vol 28 (01) ◽

pp. 1450001 ◽

Cited By ~ 172

Author(s):

PASQUALE FOGGIA ◽

GENNARO PERCANNELLA ◽

MARIO VENTO

Keyword(s):

Pattern Recognition ◽

Graph Matching ◽

Recent History ◽

Research Results ◽

Systematic Framework

In this paper, we examine the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers; the aim is to provide a systematic framework presenting the recent history and the current developments. This is made by introducing a categorization of graph-based techniques and reporting, for each class, the main contributions and the most outstanding research results.

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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.

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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.

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