This paper presents a hand shape classification approach employing multiscale template matching. The integration of background subtraction is utilized to derive a binary image of the hand object, enabling the extraction of key features such as centroid and bounding box. The methodology, while simple, demonstrates effectiveness in basic hand shape classification tasks, laying the foundation for potential applications in straightforward human-computer interaction scenarios. Experimental results highlight the system's capability in controlled environments.

翻译：本文提出了一种采用多尺度模板匹配的手部形状分类方法。通过集成背景减除技术获取手部物体的二值图像，从而能够提取质心与边界框等关键特征。该方法虽较为简洁，但在基本手部形状分类任务中展现出有效性，为简单人机交互场景下的潜在应用奠定了基础。实验结果表明，该系统在受控环境中具备良好的识别能力。