This paper propose a new unsupervised image texture segmentation algorithm
based on the design of general optimal FIR filters.
A pyramidal decomposition divide the image into equal dyadic regions/cells.
A new similarity function is developed to compare adjacent cells.
Similar cells are equally labeled through a labeling process yielding
a number of distinct areas. Optimal filters are then computed
with respect to each area. The final segmentation result is
obtained with a local energy function followed by the c-means algorithm.
Recommandations for further improvement of the algorithm are supplied.
Experiments shows that general optimal FIR filters are capable to produce
satisfactory segmentation results in an unsupervised texture segmentation