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

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

Jianlin Han, Dan Wang, *Zairan Li, Fuqian Shi
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 21
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799860297|DOI: 10.4018/IJACI.2021100101
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MLA

Han, Jianlin, et al. "Deep Self-Organizing Map Neural Networks for Plantar Pressure Image Segmentation Employing Marr-Hildreth Features." IJACI vol.12, no.4 2021: pp.1-21. http://doi.org/10.4018/IJACI.2021100101

APA

Han, J., Wang, D., Li, *., & Shi, F. (2021). Deep Self-Organizing Map Neural Networks for Plantar Pressure Image Segmentation Employing Marr-Hildreth Features. International Journal of Ambient Computing and Intelligence (IJACI), 12(4), 1-21. http://doi.org/10.4018/IJACI.2021100101

Chicago

Han, Jianlin, et al. "Deep Self-Organizing Map Neural Networks for Plantar Pressure Image Segmentation Employing Marr-Hildreth Features," International Journal of Ambient Computing and Intelligence (IJACI) 12, no.4: 1-21. http://doi.org/10.4018/IJACI.2021100101

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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.

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