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Deep Self-Organizing Map Neural Networks for Plantar Pressure Image Segmentation Employing Marr-Hildreth Features

Author

Listed:
  • Jianlin Han

    (Glorious Sun Guangdong School of Fashion, Huizhou University, Huizhou, China & Huidong Shoes Science and Technology Innovation Center, Huizhou, China)

  • Dan Wang

    (Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, China)

  • *Zairan Li

    (Wenzhou Polytechnic, Wenzhou, China & Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, China & Glorious Sun Guangdong School of Fashion, Huizhou University, Huizhou, China)

  • Fuqian Shi

    (Rutgers Cancer Institute of New Jersey, New Brunswick, USA)

Abstract

Using the plantar pressure imaging analysis method to realize the optimization design of shoe last is still relatively preliminary. The analysis and utilization of imaging data still have problems such as single processing, incomplete information acquisition, and poor processing model robustness. A deep self-organizing map neural network based on Marr-Hildreth filter (dSOM-wh) is developed in this research. The structure and learning algorithms were optimized by learning vector quantization (LVQ) and count propagation (CP). As a kind of Marr-Hildreth filter, Laplacian of Gaussian (LoG) was developed for the preprocessing. The proposed method performed high effectiveness in accuracy (AC) (92.88%), sensitive (SE) (0.8941), and f-measurement (F1) (0.8720) by comparing with ANN, CNN, SegNet, ResNet, and pre-trained inception-v neural networks. The classification-based plantar pressure biomedical functional zoning technologies have potential applications in the comfort shoe production industry.

Suggested Citation

  • Jianlin Han & Dan Wang & *Zairan Li & Fuqian Shi, 2021. "Deep Self-Organizing Map Neural Networks for Plantar Pressure Image Segmentation Employing Marr-Hildreth Features," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 12(4), pages 1-21, October.
    • Handle: RePEc:igg:jaci00:v:12:y:2021:i:4:p:1-21
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