@article{4640,
  author = {Qianwei Zhang, Lirong Yu},
  title = {Image Processing for Game Stability Assessment Using a Hybrid Fuzzy Neural Network Approach},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2026},
  volume = {17},
  number = {1},
  doi = {https://doi.org/10.6025/jmpt/2026/17/1/1-10},
  url = {https://www.dline.info/jmpt/fulltext/v17n1/jmptv17n1_1.pdf},
  abstract = {The work proposes an improved method for assessing the motion stability of badminton athletes using
advanced image segmentation techniques. It highlights the growing importance of sports stability in performance
enhancement and scientific training. Traditional video segmentation methods are noted as insufficient
for accurate athlete segmentation, prompting the integration of deep learning with fuzzy neural
networks. The authors present a hybrid algorithm combining the computational efficiency of neural networks
with the precision of fuzzy logic to better handle illumination changes and motion dynamics in video frames.
Experimental results demonstrate that their genetic fuzzy neural network algorithm achieves significantly
lower error rates 5.01% and 2.52% compared to traditional and standard fuzzy neural approaches, which
often exceed 15% error. The method shows high accuracy in segmenting moving targets, such as badminton
players, enabling clear foreground background differentiation and reliable feature extraction. Performance
tests confirm improvements in both processing speed and segmentation quality, especially for complex
motion scenarios. Despite these advances, the study acknowledges a limitation: it does not address the
methodology for movement step analysis. Overall, the research contributes a feasible, accurate, and efficient
image segmentation solution tailored for sports stability analysis, leveraging adaptive deep learning to
support real world athletic performance evaluation and decision making.},
}