High-precision recognition of wheat mildew degree based on colorimetric sensor technique combined with multivariate analysis

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Automatic measurement of SAED patterns from asbestos minerals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100106296 ◽

1988 ◽

Vol 46 ◽

pp. 846-847

Author(s):

J. C. Russ ◽

T. Taguchi ◽

P. M. Peters ◽

E. Chatfield ◽

J. C. Russ ◽

...

Keyword(s):

Diffraction Pattern ◽

High Precision ◽

Diffraction Data ◽

Automatic Measurement ◽

Automatic Method ◽

Important Method ◽

X Ray ◽

Integrated Intensity ◽

Asbestiform Minerals ◽

True Center

Conventional SAD patterns as obtained in the TEM present difficulties for identification of materials such as asbestiform minerals, although diffraction data is considered to be an important method for making this purpose. The preferred orientation of the fibers and the spotty patterns that are obtained do not readily lend themselves to measurement of the integrated intensity values for each d-spacing, and even the d-spacings may be hard to determine precisely because the true center location for the broken rings requires estimation. We have implemented an automatic method for diffraction pattern measurement to overcome these problems. It automatically locates the center of patterns with high precision, measures the radius of each ring of spots in the pattern, and integrates the density of spots in that ring. The resulting spectrum of intensity vs. radius is then used just as a conventional X-ray diffractometer scan would be, to locate peaks and produce a list of d,I values suitable for search/match comparison to known or expected phases.

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On effects of non-systematic reflections on weak-beam images of partial dislocations

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100087756 ◽

1991 ◽

Vol 49 ◽

pp. 688-689

Author(s):

K. Z. Botros ◽

S. S. Sheinin

Keyword(s):

High Precision ◽

Stacking Fault ◽

Important Application ◽

Stacking Fault Energy ◽

Sharp Peak ◽

Weak Beam ◽

Partial Dislocations ◽

Deviation Parameter ◽

Beam Diffraction

The main features of weak beam images of dislocations were first described by Cockayne et al. using calculations of intensity profiles based on the kinematical and two beam dynamical theories. The feature of weak beam images which is of particular interest in this investigation is that intensity profiles exhibit a sharp peak located at a position very close to the position of the dislocation in the crystal. This property of weak beam images of dislocations has an important application in the determination of stacking fault energy of crystals. This can easily be done since the separation of the partial dislocations bounding a stacking fault ribbon can be measured with high precision, assuming of course that the weak beam relationship between the positions of the image and the dislocation is valid. In order to carry out measurements such as these in practice the specimen must be tilted to "good" weak beam diffraction conditions, which implies utilizing high values of the deviation parameter Sg.

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Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100139585 ◽

1995 ◽

Vol 53 ◽

pp. 642-643

Author(s):

Klaus-Ruediger Peters

Keyword(s):

Image Processing ◽

Visual System ◽

High Precision ◽

Image Data ◽

Processing Technology ◽

Output Data ◽

Contrast Resolution ◽

Pixel Intensity ◽

Data Reading ◽

Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

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