Abstract
An efficient algorithm based on the simulated annealing for the learning optimization of morphological filters is proposed. The learning stage is divided into two consecutive parts; the initial-learning stage finds and fixes the most important parts of the structuring elements, and the precise-learning stage determines details of the rest. This method significantly reduces the number of trials for the modification of structuring elements. The proposed algorithm is applied to the learning optimization of the bipolar morphological operation, whose optimization problem has not yet been investigated. It is shown experimentally that the algorithm optimizes the operator as efficiently as the conventional one and reduces the amount of calculation.
