During the past decade digital imaging has significantly progressed in
all imaging areas ranging from medicine, pharmacy, chemistry, biology to
astrophysics, meteorology and geophysics. The avalanche of digitized
images produced a need for special techniques of processing and
knowledge extraction from many digital images with minimal or even
without human interaction. This has resulted in a new area in the digital
processing called pattern recognition that becomes increasingly necessary
owing to a growing number of images to be processed. The first
applications of pattern recognition techniques were for the analysis of
medical X-rays and MMR images that enabled the extraction of quantified
information in terms of texture, intensity and shape and allowed to
significantly improve a diagnosis of human organs. These techniques were
significantly developed over the last few years and combined feature
detection and classification by using region based and artificial intelligence
methods. By using growing databases of medical images processed with
pattern recognition and classification techniques, one can produce fast and
consistent diagnosis of diseases based on the accumulated knowledge
obtained from many other similar cases from the stored databases.
The use of CCD cameras for astrophysical instruments on the ground
and space produce digitized images in various fields of astrophysics. In the
past decade, many space and ground-based instruments provide large
numbers of digitized images of the night skies and of the Sun, our closest
star. These images provide more and more valuable knowledge about the
evolution of celestial bodies and the physical processes occurring in them.
This ample information can be processed with relatively new methods of
feature recognition and classification developed in other imaging fields.
With every new instrument and space mission, the archives of digital
ments for the development of automated pattern recognition methods in
applications to these archives.
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