The purpose of the research
is to develop a universal algorithm for partial texture segmentation of any
visual images. The main peculiarity of the proposed segmentation procedure is
the extraction of only homogeneous fine-grained texture segments present in the
images. At first, an initial seed point is found for the largest and most
homogeneous segment of the image. This initial seed point of the segment is
expanded using a region growing method. Other texture segments of the image are
extracted analogously in turn. At the second stage, the procedure of merging
the extracted segments belonging to the same texture class is performed. Then,
the detected texture segments are input to a neural network with competitive
layers which accomplishes more accurate delineation of the shapes of the
extracted texture segments. The proposed segmentation procedure is fully
unsupervised, i.e.,
it does not use any a priori knowledge on either the type of textures or the
number of texture segments in the image. The research results in development of
the segmentation algorithm realized as a computer program tested in a series of
experiments that demonstrate its efficiency on grayscale natural scenes.
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