Type-2 Fuzzy Logic in Image Analysis and Pattern Recognition

Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 301)

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 difficult to demonstrate which ones are better before the recognition results are obtained. In this work 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 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 an image recognition system.

Keywords

Interval type-2 fuzzy systems Image processing Pattern recognition Edge detection Type-2 fuzzy logic Face recognition Neural networks Fuzzy edge detectors Fuzzy sobel edge detector Rules for edge detection Benchmark image data bases Monolithic neural network Recognition rates Comparison of results Mean recognition rates Type-2 fuzzy edge detector 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Division of Graduate Studies and ResearchTijuana Institute of TechnologyTijuanaMexico

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