Open AccessImage Feature Analysis and Dynamic Measurement of Plantar Pressure Based on Fusion Feature Extraction
Author(s) -
Ji Zou,
Chao Zhang,
Zhongjing Ma,
Lei Yu,
Kai Sun,
Tengfei Liu
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380627
Subject(s) - plantar pressure , feature extraction , artificial intelligence , pattern recognition (psychology) , computer science , feature (linguistics) , image processing , histogram , image fusion , computer vision , footprint , wavelet transform , image (mathematics) , wavelet , engineering , pressure sensor , geology , mechanical engineering , paleontology , linguistics , philosophy
Footprint recognition and parameter measurement are widely used in fields like medicine, sports, and criminal investigation. Some results have been achieved in the analysis of plantar pressure image features based on image processing. But the common algorithms of image feature extraction often depend on computer processing power and massive datasets. Focusing on the auxiliary diagnosis and treatment of foot rehabilitation of foot laceration patients, this paper explores the image feature analysis and dynamic measurement of plantar pressure based on fusion feature extraction. Firstly, the authors detailed the idea of extracting image features with a fusion algorithm, which integrates wavelet transform and histogram of oriented gradients (HOG) descriptor. Next, the plantar parameters were calculated based on plantar pressure images, and the measurement steps of plantar parameters were given. Finally, the feature extraction effect of the proposed algorithm was verified, and the measured results on plantar parameters were obtained through experiments.