Zusammenfassung
Many image segmentation techniques are available in the literature.
Some of these techniques use only the gray level histogram, some
use spatial details while others use fuzzy set theoretic approaches.
Most of these techniques are not suitable for noisy environments.
Some works have been done using the Markov Random Field (MRF) model
which is robust to noise, but is computationally involved. Neural
network architectures which help to get the output in real time because
of their parallel processing ability, have also been used for segmentation
and they work fine even when the noise level is very high. The literature
on color image segmentation is not that rich as it is for gray tone
images. This paper critically reviews and summarizes some of these
techniques. Attempts have been made to cover both fuzzy and non-fuzzy
techniques including color image segmentation and neural network
based approaches. Adequate attention is paid to segmentation of range
images and magnetic resonance images. It also addresses the issue
of quantitative evaluation of segmentation results.
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