生物灵感摄影受体与神经网络用于识别和分类手势语言手势
Claudio Urrea1, John Kern1, Ricardo Navarrete1
1Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, Chile.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
一个新的光生物过器分类器 (PhBFC) 提高了使用生物启发的光感受器模型的手语翻译准确度. 该系统可以改善听力障碍者的沟通,而不会牺牲处理速度.
科学领域:
- 计算机视觉 计算机视觉
- 生物启发的计算 生物启发的计算
- 人与计算机的交互
背景情况:
- 静态手语翻译系统经常面临准确性限制.
- 有效的沟通工具对于弥合与听力受损社区的差距至关重要.
- 生物启发的算法为图像处理挑战提供了新的方法.
研究的目的:
- 为静态手语翻译设计和实施光生物波器分类器 (PhBFC).
- 为了增强图像预处理使用生物灵感的光感受器模型.
- 提高手语识别系统的准确性和效率.
主要方法:
- 开发了一种新的光生物过器分类器 (PhBFC).
- 采用一种由哺乳动物视网膜光受体细胞启发的对比度增强算法.
- 在三个系统版本 (V1,V2,V3) 中对生物灵感光感受器模型进行了评估,每个字母进行了100次测试.
主要成果:
- 在V3模型上实现了91.1%的平均精度,比V1的63.4%显著改善.
- 在所有模型中保持了每秒 55.5 (FPS) 的平均处理速度.
- 证明生物启发方法可以提高准确性,而不会影响处理时间.
结论:
- 光生物过器分类器 (PhBFC) 有效地提高了静态手语翻译的准确性.
- 生物启发的光感受器模型提供了一种可行的方法来增强翻译系统中的图像处理.
- 这项技术具有很大的潜力,可以集成到各种设备和应用程序中,包括深度学习.
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