Abstract
Interval type-2 fuzzy systems can be of great help in image analysis and pattern recognition applications. In particular, edge detection is a process usually applied to image sets before the training phase in recognition systems. This preprocessing step helps to extract the most important shapes in an image, ignoring the homogeneous regions and remarking the real objective to classify or recognize. Many traditional and fuzzy edge detectors can be used, but it is very difficult to demonstrate which one is better before the recognition results are obtained. In this chapter, we show experimental results where several edge detectors were used to preprocess the same image sets. Each resulting image set was used as training data for a modular neural network recognition system, and the recognition rates were compared. The goal of these experiments is to find the better edge detector that can be used to improve the training data of a modular neural network for an image recognition system .
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Abbreviations
- FIS1:
-
type-1 fuzzy inference system
- FIS2:
-
type-2 fuzzy inference system
- MG:
-
morphological gradient
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Melin, P. (2015). Pattern Recognition with Modular Neural Networks and Type-2 Fuzzy Logic. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_79
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DOI: https://doi.org/10.1007/978-3-662-43505-2_79
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