Biomechaniclal Analysis concerning Steepness Application on Ground Training Posture for Snowboarding

Lee, Chong-hoon; Jeon, Youn-Girl

doi:10.35159/kjss.2019.08.28.4.1111

Cited by 1 publication

(1 citation statement)

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“…Before extracting the image feature points, we must first ensure that the extraction process is not disturbed by other factors, so as to maintain the stability of the sensing image. erefore, the middle point of the image is taken as the feature point, the interference factors are filtered by laser [20,21], and several parameters are obtained by Hough method. Suppose there are two critical points in the graph, i.e., b1 and b2, the image's center coordinates are substituted into the line equation.…”

Section: Image Filtering Processingmentioning

confidence: 99%

Feature Extraction Method of Snowboard Starting Action Using Vision Sensor Image Processing

Zhang

Li²,

Shin

et al. 2022

Mobile Information Systems

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There is a lot of noise in the snowboard starting action image, which leads to the low accuracy of snowboard starting action feature extraction. We propose a snowboard starting action feature extraction using visual sensor image processing. Firstly, the overlapping images are separated by laser fringe technology. After separation, the middle point of the image is taken as the feature point, and the interference factors are filtered by laser. Secondly, the three-dimensional model is established by using visual sensing image technology, the action feature images are input in the order of recognition, and all actions are reconstructed and assembled to complete the action feature extraction of snowboard. The interference factors are filtered by laser, the middle part of the action image is extracted according to the common features of multiple images, and its definition is described. The movement change and moving distance are used to count the most features and clarity. Finally, the edge recognition effect of snowboard starting action image and the action recognition effect under multiple complex images are taken as experimental indexes. The results show that the method has a good effect on image edge extraction, the extraction effect is as high as 95%, and the accuracy is as high as 2.1%. In addition, under multiple complex images, the action feature recognition rate is also high, which can prove that the method studied has better accuracy in snowboard starting action feature extraction.

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Section: Image Filtering Processingmentioning

confidence: 99%