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Context-Sensitive Thresholding Technique Using ABC for Aerial Images

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Book cover Soft Computing and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 898))

Abstract

Image anatomization is a remarkable notch in image processing which entails the scrutinization of the number of non-overlapping and homogeneous regions that exist in the input image. Thresholding is the most popular algorithm of image segmentation. In this article, the authors have utilized energy curve to incorporate spatial contextual information to inspect the regions where most favourable threshold(s) exist. The thresholding technique automatically computes the count of objects present in input image. To determine the optimal thresholds present in the image, artificial bee colony algorithm has been deployed. The results achieved have been compared with GA-based technique to ensure the efficacy of the proposed technique.

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Authors and Affiliations

  1. Department of CSE, CUIET, Chitkara University, Chandigarh, India

    Kirti & Anshu Singla

Authors
  1. Kirti
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  2. Anshu Singla
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Corresponding author

Correspondence to Kirti .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  2. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangaluru, Karnataka, India

    G. Ram Mohana Reddy

  3. Department of Computer Science and Engineering, JNTUH College of Engineering Hyderabad, Hyderabad, Telangana, India

    V. Kamakshi Prasad

  4. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering & Technology, Secunderabad, Telangana, India

    V. Sivakumar Reddy

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Kirti, Singla, A. (2019). Context-Sensitive Thresholding Technique Using ABC for Aerial Images. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-13-3393-4_10

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