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Novel fitness function for 3D image reconstruction using bat algorithm based autoencoder

Published: 20 June 2018 Publication History

Abstract

With a view to investigate, examine and analyze the performance of 3D medical image compression based on Autoencoder neural networks, a novel algorithm named autoimage reconstruct algorithm is developed, which is based on recent BAT bioinspired algorithm with a novel fitness function as Mean Square Error. It is observed that proposed algorithm outperforms existing algorithms like wavelet-based encoding and decoding for image compression and reconstruction in terms of Mean Square Error, Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

References

[1]
Rashmita Khilar, S Chitrakala, and SurenderNath SelvamParvathy. 2013. 3D image reconstruction: Techniques, applications and challenges. In Optical Imaging Sensor and Security (ICOSS), 2013 International Conference on. IEEE, 1--6.
[2]
Zhao-Ming Liu, Yung-Yao Chen, S Hidayati, Shih-Che Chien, Feng-Chia Chang, and Kai-Lung Hua. 2017. 3D model retrieval based on deep Autoencoder neural networks. In Signals and Systems (ICSigSys), 2017 International Conference on. IEEE, 290--296.
[3]
Yueqing Wang, Zhige Xie, Kai Xu, Yong Dou, and Yuanwu Lei. 2016. An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning. Neurocomputing 174 (2016), 988--998.
[4]
Xin-She Yang and Amir Hossein Gandomi. 2012. Bat algorithm: a novel approach for global engineering optimization. Engineering Computations 29, 5 (2012), 464--483.

Cited By

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  • (2022)Vehicle Detection From UAV Imagery With Deep Learning: A ReviewIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308027633:11(6047-6067)Online publication date: Nov-2022

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  1. Novel fitness function for 3D image reconstruction using bat algorithm based autoencoder

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      cover image ACM Conferences
      Web3D '18: Proceedings of the 23rd International ACM Conference on 3D Web Technology
      June 2018
      199 pages
      ISBN:9781450358002
      DOI:10.1145/3208806
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 20 June 2018

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      Author Tags

      1. BAT algorithm
      2. autoencoder
      3. image
      4. reconstruction

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      Overall Acceptance Rate 27 of 71 submissions, 38%

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      • (2022)Vehicle Detection From UAV Imagery With Deep Learning: A ReviewIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308027633:11(6047-6067)Online publication date: Nov-2022

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