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

Jianlin Han, Dan Wang, *Zairan Li and Fuqian Shi
Additional contact information
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

International Journal of Ambient Computing and Intelligence (IJACI), 2021, vol. 12, issue 4, 1-21

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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJACI.2021100101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jaci00:v:12:y:2021:i:4:p:1-21

Access Statistics for this article

International Journal of Ambient Computing and Intelligence (IJACI) is currently edited by Nilanjan Dey

More articles in International Journal of Ambient Computing and Intelligence (IJACI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().