  Digital image segmentation is the process of assigning distinct labels to
different objects in a digital image, and the fuzzy segmentation algorithm has
been successfully used in the segmentation of images from a wide variety of
sources. However, the traditional fuzzy segmentation algorithm fails to segment
objects that are characterized by textures whose patterns cannot be
successfully described by simple statistics computed over a very restricted
area. In this paper, we propose an extension of the fuzzy segmentation
algorithm that uses adaptive textural affinity functions to perform the
segmentation of such objects on bidimensional images. The adaptive affinity
functions compute their appropriate neighborhood size as they compute the
texture descriptors surrounding the seed spels (spatial elements), according to
the characteristics of the texture being processed. The algorithm then segments
the image with an appropriate neighborhood for each object. We performed
experiments on mosaic images that were composed using images from the Brodatz
database, and compared our results with the ones produced by a recently
published texture segmentation algorithm, showing the applicability of our
method.
