Abstract
Interval type-2 fuzzy systems can be of great help in achieving efficient image processing and pattern recognition applications. In particular, edge detection is an operation 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 have been proposed, but it is very difficult to demonstrate which one is better before the recognition results are obtained. In this chapter, we present experimental results where several edge detectors were used to preprocess the same image sets. Each resultant image set was used as training data for a 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 neural network for image recognition.
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Melin, P. (2015). Image Processing and Pattern Recognition with Interval Type-2 Fuzzy Inference Systems. In: Sadeghian, A., Tahayori, H. (eds) Frontiers of Higher Order Fuzzy Sets. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3442-9_11
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DOI: https://doi.org/10.1007/978-1-4614-3442-9_11
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