Optical processing and neural nets for scene analysis

1993 ◽  
Author(s):  
David P. Casasent
Keyword(s):  
Neural Nets ◽  
Scene Analysis ◽  
Optical Processing

The Theoretical Contrast in Scanning and Conventional High Resolution Microscopes

1969 ◽  
Vol 27 ◽  
pp. 170-171
Author(s):  
E. Zeitler ◽  
M. G. R. Thomson
Keyword(s):  
Small Volume ◽  
Volume Element ◽  
Optical Processing ◽  
Image Construction ◽  
Object Interaction ◽  
Transmission Electron ◽  
Scanning Transmission ◽  
Optical Treatment ◽  
Electron Microscopes ◽  
Intensity Contrast

In the formation of an image each small volume element of the object is correlated to an areal element in the image. The structure or detail of the object is represented by changes in intensity from element to element, and this variation of intensity (contrast) is determined by the interaction of the electrons with the specimen, and by the optical processing of the information-carrying electrons. Both conventional and scanning transmission electron microscopes form images which may be considered in this way, but the mechanism of image construction is very different in the two cases. Although the electron-object interaction is the same, the optical treatment differs.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

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