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Image Processing and Pattern Recognition with Mamdani Interval Type-2 Fuzzy Inference Systems

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Combining Experimentation and Theory

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 271))

Abstract

Interval type-2 fuzzy systems can be of great help in image processing 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’s very difficult to demonstrate which one is better before the recognition results. In this work we present an experiment where several edge detectors were used to preprocess the same image sets. Each resultant image set was used as training data for neural network recognition system, and the recognition rates were compared. The goal of this experiment is to find the better edge detector that can be used as training data on a neural network for image recognition.

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Authors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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  1. Patricia Melin
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Correspondence to Patricia Melin .

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Editors and Affiliations

  1. European Center for Soft Computing, Mieres, Asturias, 33600, Spain

    Enric Trillas

  2. , Computing and Decision Sciences, Coolidge Fellow GE Global Research, NY, 12309, USA

    Piero P. Bonissone

  3. , Edificio Científico-Tecnológico, European Centre of Soft Computing, Calle Gonzalo Gutiérrez Quirós S/N Mieres, Asturias, 33600, Spain

    Luis Magdalena

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, Warsaw, 01--447, Poland

    Janusz Kacprzyk

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Melin, P. (2012). Image Processing and Pattern Recognition with Mamdani Interval Type-2 Fuzzy Inference Systems. In: Trillas, E., Bonissone, P., Magdalena, L., Kacprzyk, J. (eds) Combining Experimentation and Theory. Studies in Fuzziness and Soft Computing, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24666-1_13

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  • DOI: https://doi.org/10.1007/978-3-642-24666-1_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24665-4

  • Online ISBN: 978-3-642-24666-1

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