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Binary Object Extraction by Bidirectional Self-Organizing Neural Network Architecture

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Soft Computing for Image and Multimedia Data Processing

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Abstract

The conventional multilayer self-organizing neural network (MLSONN) architecture [89] suffers from several limitations as far as the extraction and segmentation of multilevel and color images are concerned. A multilevel version of the standard sigmoidal activation function was introduced in [33, 34, 232, 233] for inducing multiscaling capability in the functionality of the architecture, without increasing the network complexity in the process.

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Authors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science & Engineering, Jadavpur University, Kolkata, India

    Ujjwal Maulik

Authors
  1. Siddhartha Bhattacharyya
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  2. Ujjwal Maulik
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Bhattacharyya, S., Maulik, U. (2013). Binary Object Extraction by Bidirectional Self-Organizing Neural Network Architecture. In: Soft Computing for Image and Multimedia Data Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40255-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-40255-5_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40254-8

  • Online ISBN: 978-3-642-40255-5

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