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A Decennary Survey on Artificial Intelligence Methods for Image Segmentation

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Advanced Engineering Optimization Through Intelligent Techniques

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 949))

Abstract

The technique of breaking down an image into categorial regions containing each pixel with similar attributes is termed as image segmentation (IS). It is the preliminary step of image processing. This technique can be used for both grey-scale and colour images. This technique is applied everywhere, even in our personal Smartphone’s camera while capturing pictures. And image segmentation is the most innovative problem under the computer vision domain. This paper provides various techniques that are available in the field of image segmentation and their pros and cons. A lot of research is being done by applying artificial intelligence techniques for the image segmentation problems. In this paper, an overview of artificial intelligence algorithm techniques such as machine learning, deep learning, meta-heuristics approaches that was used in the past decade has been discussed, and a comparative study about the same is carried out and the problems and recommendations for selection of appropriate method for image segmentation have been dealt with.

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Authors and Affiliations

  1. Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India

    B. Vinoth Kumar, S. Sabareeswaran & G. Madumitha

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  1. B. Vinoth Kumar
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  2. S. Sabareeswaran
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  3. G. Madumitha
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Correspondence to B. Vinoth Kumar .

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Editors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, Surat, Gujarat, India

    R. Venkata Rao

  2. Cracow University of Technology, Kraków, Poland

    Jan Taler

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Vinoth Kumar, B., Sabareeswaran, S., Madumitha, G. (2020). A Decennary Survey on Artificial Intelligence Methods for Image Segmentation. In: Venkata Rao, R., Taler, J. (eds) Advanced Engineering Optimization Through Intelligent Techniques. Advances in Intelligent Systems and Computing, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-13-8196-6_27

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