Models Fitting to Pattern Recognition in Hyperspectral Images

Image Processing

Handbook of Research on Science Education and University Outreach as a Tool for Regional Development - Advances in Educational Marketing, Administration, and Leadership ◽

10.4018/978-1-5225-1880-8.ch017 ◽

2017 ◽

pp. 223-247

Author(s):

Singaraju Jyothi ◽

Dadala Mary Mamatha

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Programming Language ◽

Computer Experiments ◽

Intermediate Level ◽

Hyperspectral Images ◽

Key Concepts ◽

Matlab Programming ◽

Modern Image

This chapter gives an introductory to intermediate level text on the science of image processing. It employs the Matlab programming language to illustrate some of the elementary key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the chapter offers a framework within which the concepts can be understood by a series of well-chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering. Particularly, the modern applications such as agriculture, aquaculture, food industrial analysis and Hyperspectral images analysis are discussed in this chapter with illustrations using Matlab.

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Pattern recognition in hyperspectral images using feedback

10.1117/12.463518 ◽

2003 ◽

Author(s):

Shawn D. Hunt ◽

Diego Rivera

Keyword(s):

Pattern Recognition ◽

Hyperspectral Images

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Image Processing

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch039 ◽

2018 ◽

pp. 946-971

Author(s):

Singaraju Jyothi ◽

Dadala Mary Mamatha

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Programming Language ◽

Computer Experiments ◽

Intermediate Level ◽

Hyperspectral Images ◽

Key Concepts ◽

Matlab Programming ◽

Modern Image

This chapter gives an introductory to intermediate level text on the science of image processing. It employs the Matlab programming language to illustrate some of the elementary key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the chapter offers a framework within which the concepts can be understood by a series of well-chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering. Particularly, the modern applications such as agriculture, aquaculture, food industrial analysis and Hyperspectral images analysis are discussed in this chapter with illustrations using Matlab.

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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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