Abstract:
In this paper we study how shape information encoded in contour
energy components values can be used for detection of microscopic organisms
in population images. We proposed features based on shape and geometrical
statistical data obtained from samples of optimized contour lines integrated in
the framework of Bayesian inference for recognition of individual specimens.
Compared with common geometric features the results show that patterns
present in the image allow better detection of a considerable amount of
individuals even in cluttered regions when sufficient shape information is
retained. Therefore providing an alternative to building a specific shape model
or imposing specific constrains on the interaction of overlapping objects.
