运动图像中基于手势识别的计算机指针控制
Control of Computer Pointer Using Hand Gesture Recognition in Motion Pictures
AI总结：
本研究构建含6720样本的四类手势数据集，训练CNN实现手势识别以控制电脑光标，达到91.88%的准确率，验证了多背景下的应用潜力。
摘要：
本文提出了一种用户界面，旨在通过手部检测与手势分类实现计算机光标控制。研究收集了包含6720个图像样本的综合手部数据集，涵盖四个不同类别：拳头、手掌、左指、右指。图像由15名受试者在多种场景下采集，包括不同视角与光照条件的简单背景。研究在该数据集上训练了卷积神经网络（CNN），用于准确预测每张采集图像的标签并衡量其相似度。该系统整合了光标移动、左键点击、右键点击动作的预设命令。实验结果表明，所提算法达到了91.88%的优异准确率，证明其在不同背景下具备潜在应用价值。
英文摘要：
This paper presents a user interface designed to enable computer cursor control through hand detection and gesture classification. A comprehensive hand dataset comprising 6720 image samples was collected, encompassing four distinct classes: fist, palm, pointing to the left, and pointing to the right. The images were captured from 15 individuals in various settings, including simple backgrounds with different perspectives and lighting conditions. A convolutional neural network (CNN) was trained on this dataset to accurately predict labels for each captured image and measure their similarity. The system incorporates defined commands for cursor movement, left-click, and right-click actions. Experimental results indicate that the proposed algorithm achieves a remarkable accuracy of 91.88% and demonstrates its potential applicability across diverse backgrounds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
