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Variable Precision Fuzzy Hit-or-Miss Transformation Models to Object Identification in Grey-Scale Images

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Abstract

Morphological image operators are important geometry-based image transformations, whereas fuzzy set theory and fuzzy logic, favoured for their ability to describe and cope with imprecise and uncertain natures of objects, are successfully applied to image processing and pattern recognition. In this paper, generalizations of hit-or-miss transformation (HMT) for grey-scale images are investigated in the framework of fuzzy set theory and fuzzy logic. To address the problem of robust object identification, a notion of fuzzy vertical translation is proposed and two variable precision fuzzy HMT models are developed for grey-scale images to cater for flexible selection of structuring elements in the fuzzy setting. Their extensions to the rank case are designed to adapt the HMT to noisy images and images with inhomogeneous intensities. Superior performance of the proposed models over the state-of-the-art HMT models in objection extraction is verified by a series of experiments on synthetic images and real images.

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Acknowledgments

The authors would like to thank the editor-in-chief and anonymous reviewers for constructive comments and suggestions for improving the quality and readability of this paper. This paper is partly supported by the National Natural Science Foundation of China under Grants 10771043 and 11471001.

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Authors and Affiliations

  1. Harbin Engineering University, Harbin, People’s Republic of China

    Wei Xie, Tingquan Deng, Qiang Li & Minghua Ma

  2. Harbin University of Science and Technology, Harbin, People’s Republic of China

    Wei Xie

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  1. Wei Xie
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Correspondence to Tingquan Deng.

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Xie, W., Deng, T., Li, Q. et al. Variable Precision Fuzzy Hit-or-Miss Transformation Models to Object Identification in Grey-Scale Images. J Math Imaging Vis 53, 112–129 (2015). https://doi.org/10.1007/s10851-014-0552-x

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