Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the ha...Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogrambased threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network(CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest(RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy(88.34% mean intersection over union, mIoU) and a shorter processing time(≤8 ms).展开更多
针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语...针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语义信息的语义分割线程,一个语义建图线程。为优化系统在处理动态物体时的准确性和鲁棒性,YSG-SLAM引入快速动态特征剔除算法,并耦合漏检补偿模块来应对基于实时实例分割(You Only Look At Coefficients,YOLACT)算法可能出现的漏检情况,有效地提升了特征点剔除的精确度和系统的整体稳定性。为减少由特征点聚集引起的定位误差从而优化特征点的空间分布,设计自适应角点提取阈值计算方法,使特征分布更加均匀。语义建图线程充分利用二维语义信息与三维点云数据,可选择性构建语义地图和八叉树地图,提高了系统的环境感知能力及机器人在复杂环境下的相关任务执行能力。YSG-SLAM在德国慕尼黑工业大学数据集、Bonn数据集上进行了评估,相较于原ORB-SLAM2,各项定位误差下降达93%。实验结果表明,YSG-SLAM有效提升了系统实时性,定位精度高,且可构建两种地图,具有一定的实用价值。展开更多
文摘Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogrambased threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network(CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest(RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy(88.34% mean intersection over union, mIoU) and a shorter processing time(≤8 ms).
文摘为解决焊接缺陷图像分割的结果出现失真、分割效果差的问题,以轮辋生产过程中的裂纹和气孔焊接缺陷图像为研究对象,提出了一种基于模拟退火(simulated annealing,SA)策略改进粒子群算法(improved particle swarm optimization,IPSO)的焊接缺陷三阈值图像分割方法。首先通过灰度值、平均灰度值和中值灰度值建立图像的三维最大类间方差(Otsu)模型;其次引入自适应惯性权重和非对称学习因子并融入SA策略增强算法求解效率和跳出局部最优的能力;最后利用SA-IPSO算法优化三维Otsu模型求解得到最佳阈值对应的缺陷分割图像。采用不同算法和模型对焊接缺陷图像进行分割,结果表明:对于裂纹和气孔焊接缺陷图像,本文算法在峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)评价指标上均优于对比算法,在加快算法收敛的同时避免分割结果失真,提高了分割精度。
文摘针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语义信息的语义分割线程,一个语义建图线程。为优化系统在处理动态物体时的准确性和鲁棒性,YSG-SLAM引入快速动态特征剔除算法,并耦合漏检补偿模块来应对基于实时实例分割(You Only Look At Coefficients,YOLACT)算法可能出现的漏检情况,有效地提升了特征点剔除的精确度和系统的整体稳定性。为减少由特征点聚集引起的定位误差从而优化特征点的空间分布,设计自适应角点提取阈值计算方法,使特征分布更加均匀。语义建图线程充分利用二维语义信息与三维点云数据,可选择性构建语义地图和八叉树地图,提高了系统的环境感知能力及机器人在复杂环境下的相关任务执行能力。YSG-SLAM在德国慕尼黑工业大学数据集、Bonn数据集上进行了评估,相较于原ORB-SLAM2,各项定位误差下降达93%。实验结果表明,YSG-SLAM有效提升了系统实时性,定位精度高,且可构建两种地图,具有一定的实用价值。
