| Abstract: | Automatic target recognition is a challenging task with a
wide variety of potential applications in the industrial,
military and medical fields. Region segmentation is a
crucial step towards automatic segmentation of images.
Under some severe conditions of improper illumination
and unexpected disturbances, the blurring images make it
more difficult for target recognition, which results in the
necessity of segmentation. For instance, the underwater
images are generally captured under water dispersing and
atmospheric fluctuation. Segmentation is thus needed to
clarify feature ambiguity against stochastic disturbances.
Region segmentation splits images into regions based on
similarity measures, such as pixel intensities, locations
and textures or combinations. It categorizes an image into
separate parts, which correlates with objects involved.
Both K-means segmentation and watershed segmentation
can be applied. Segmentation by K-means clustering
refers to grouping similar data points into the clusters. It
requires that the number of clusters be specified whose
distance metrics should be defined to quantify orientation
closeness of objects. The winner-take-all algorithm can
thus be selected to update the cluster centers. It has the
capability of simplifying computation and accelerating
convergence. Another typical methodology is watershed
segmentation. It is based on the gradient magnitude of
images, which can classify diverse objects automatically,
where watershed lines separate catchment basins. The
erosion and dilation operations are essential procedures
involved in watershed segmentation. Also to avoid over
segmentation, the foreground and background markers
should be selected accordingly. To evaluate the actual
role of nonlinear image region segmentation, quantitative
statistical measures have been proposed, such as the gray
level energy, discrete entropy, relative entropy, mutual
information and information redundancy. The assessment
measures will further quantify the impact from image
segmentation. The objective assessment approach has the
potential to solve other image processing issues.
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