Computer Science An Overview _J. Glenn Brookshear _11th Edition
The task of understanding general images is usually approached as a two-
step process: (1)
image processing,
which refers to identifying characteristics of
the image, and (2)
image analysis,
which refers to the process of understanding
what these characteristics mean. We have already observed this dichotomy in
the context of recognizing symbols by means of their geometric features. In that
situation, we found image processing represented by the process of identifying
the geometric features found in the image and image analysis represented by the
process of identifying the meaning of those features.
Image processing entails numerous topics. One is edge enhancement, which is
the process of applying mathematical techniques to clarify the boundaries between
regions in an image. In a sense, edge enhancement is an attempt to convert a
photograph into a line drawing. Another activity in image analysis is known as
region finding. This is the process of identifying those areas in an image that have
common properties such as brightness, color, or texture. Such a region probably
represents a section of the image that belongs to a single object. (It is the ability to
recognize regions that allows computers to add color to old-fashioned black and
white motion pictures.) Still another activity within the scope of image processing
is smoothing, which is the process of removing flaws in the image. Smoothing keeps
errors in the image from confusing the other image-processing steps, but too much
smoothing can cause the loss of important information as well.
Smoothing, edge enhancement, and region finding are all steps toward iden-
tifying the various components in an image. Image analysis is the process of
determining what these components represent and ultimately what the image
means. Here one faces such problems as recognizing partially obstructed objects
from different perspectives. One approach to image analysis is to start with an
assumption about what the image might be and then try to associate the compo-
nents in the image with the objects whose presence is conjectured. This appears
to be an approach applied by humans. For instance, we sometimes find it hard to
recognize an unexpected object in a setting in which our vision is blurred, but
once we have a clue to what the object might be, we can easily identify it.