Hand gestures are powerful means of communication among humans and sign language is the most natural and expressive way of communication for dump and deaf people. In this work, real-time hand gesture system is propose...Hand gestures are powerful means of communication among humans and sign language is the most natural and expressive way of communication for dump and deaf people. In this work, real-time hand gesture system is proposed. Experimental setup of the system uses fixed position low-cost web camera with 10 mega pixel resolution mounted on the top of monitor of computer which captures snapshot using Red Green Blue [RGB] color space from fixed distance. This work is divided into four stages such as image preprocessing, region extraction, feature extraction, feature matching. First stage converts captured RGB image into binary image using gray threshold method with noise removed using median filter [medfilt2] and Guassian filter, followed by morphological operations. Second stage extracts hand region using blob and crop is applied for getting region of interest and then “Sobel” edge detection is applied on extracted region. Third stage produces feature vector as centroid and area of edge, which will be compared with feature vectors of a training dataset of gestures using Euclidian distance in the fourth stage. Least Euclidian distance gives recognition of perfect matching gesture for display of ASL alphabet, meaningful words using file handling. This paper includes experiments for 26 static hand gestures related to A-Z alphabets. Training dataset consists of 100 samples of each ASL symbol in different lightning conditions, different sizes and shapes of hand. This gesture recognition system can reliably recognize single-hand gestures in real time and can achieve a 90.19% recognition rate in complex background with a “minimum-possible constraints” approach.展开更多
地面激光扫描技术是获取建筑物三维数据的重要手段之一,但其处理技术在自动化程度、密度适应性以及算法计算量等方面还存在较多问题。为此,本文提出了建筑区域点云的快速自动提取方法。首先,引入SLAM6D（simultaneous localization and ...地面激光扫描技术是获取建筑物三维数据的重要手段之一,但其处理技术在自动化程度、密度适应性以及算法计算量等方面还存在较多问题。为此,本文提出了建筑区域点云的快速自动提取方法。首先,引入SLAM6D（simultaneous localization and mapping with 6 Dof）算法实现点云的自动配准;接着,使用体素重采样解决数据的远近密度差异与重叠区域冗余,并设计了空中管线滤除算子防止建筑分割中的粘连现象;然后,引入车载点云中的特征图法实现地面激光点云的快速分割;最后,使用先验知识从分割单元中识别建筑区域。实验证明,本方法可以从地面点云中提取建筑区域点云。展开更多
文摘Hand gestures are powerful means of communication among humans and sign language is the most natural and expressive way of communication for dump and deaf people. In this work, real-time hand gesture system is proposed. Experimental setup of the system uses fixed position low-cost web camera with 10 mega pixel resolution mounted on the top of monitor of computer which captures snapshot using Red Green Blue [RGB] color space from fixed distance. This work is divided into four stages such as image preprocessing, region extraction, feature extraction, feature matching. First stage converts captured RGB image into binary image using gray threshold method with noise removed using median filter [medfilt2] and Guassian filter, followed by morphological operations. Second stage extracts hand region using blob and crop is applied for getting region of interest and then “Sobel” edge detection is applied on extracted region. Third stage produces feature vector as centroid and area of edge, which will be compared with feature vectors of a training dataset of gestures using Euclidian distance in the fourth stage. Least Euclidian distance gives recognition of perfect matching gesture for display of ASL alphabet, meaningful words using file handling. This paper includes experiments for 26 static hand gestures related to A-Z alphabets. Training dataset consists of 100 samples of each ASL symbol in different lightning conditions, different sizes and shapes of hand. This gesture recognition system can reliably recognize single-hand gestures in real time and can achieve a 90.19% recognition rate in complex background with a “minimum-possible constraints” approach.
文摘地面激光扫描技术是获取建筑物三维数据的重要手段之一,但其处理技术在自动化程度、密度适应性以及算法计算量等方面还存在较多问题。为此,本文提出了建筑区域点云的快速自动提取方法。首先,引入SLAM6D（simultaneous localization and mapping with 6 Dof）算法实现点云的自动配准;接着,使用体素重采样解决数据的远近密度差异与重叠区域冗余,并设计了空中管线滤除算子防止建筑分割中的粘连现象;然后,引入车载点云中的特征图法实现地面激光点云的快速分割;最后,使用先验知识从分割单元中识别建筑区域。实验证明,本方法可以从地面点云中提取建筑区域点云。
