Feature Based Defuzzification at Increased Spatial Resolution

Defuzzification of spatial fuzzy sets by feature distance minimization

“…Suggestions how to design a defuzzification method appropriate for a particular application are given, together with a discussion about the behaviour of the algorithm depending on different parameter settings. The proposed method has been further developed and successfully adjusted to particular tasks in [35,36]. In our opinion, the flexibility of the proposed defuzzification method and the essential simplicity of its main idea, presented in this paper, are an appealing starting point for further studies and development.…”

“…7 The presented method can, in combination with a fuzzy segmentation method, be used as an alternative to crisp segmentation, when it is of importance to preserve certain geometric properties of the original object, such as area and perimeter. Moreover, our further studies [35] have shown that the defuzzification can be performed at an increased spatial resolution, compared to the original fuzzy image. This approach utilizes the increased precision of the feature estimates that is achievable when fuzzy representation of an object is used [6,8].…”

“…The visual appearance of the defuzzification result is much more appealing and judgement on the original crisp shape from the defuzzification is no longer difficult. The details on defuzzification by feature distance minimization at increased spatial resolution are given in [35].…”

Defuzzification by Feature Distance Minimization Based on DC Programming

“…We use the SA algorithm as described in [12] and further refined in [4,5], with the following parameter settings: The initial configuration is obtained by the optimal α-cut; The initial temperature, T 0 is 0.1; The number of perturbations tested at each temperature level is 10 000, after which the temperature is reduced one level, T k+1 = 0.995T k ; The temperature is successively lowered until 50 000 successive perturbations does not provide any step that gives a reduction in distance, after which a new re-annealing is restarted from the currently best found solution; After 10 re-annealings the process is stopped and the best found configuration is used. For more details see [5].…”

“…The minimization task of finding an optimal binary configuration requires, due to the large search space, application of some appropriate optimization method. An algorithm which is successfully adapted and mostly used for this task so far is Simulated Annealing (SA) [4,5,12].…”