摘要
为实现感兴趣区手语视频编码,提高通话效率,提出一种基于细胞神经网络(CNN)的快速手语视频分割方法。该方法首先利用肤色信息特征进行基于CNN的肤色检测,检测出手语视频中的肤色区域;然后对肤色检测结果,利用帧差法进行基于CNN的运动检测,获得初始的手势区域;最后采用形态学处理方法进行空洞填充和边界平滑,实现了手语视频图像序列中的面部和手部区域的分割。研究结果表明,该方法能够快速准确地进行手语视频分割。
To achieve sign language video coding of region of interest, and improve call efficiency, a fast segmentation methodology of sign language video based on Cellular Neural Network （CNN） was proposed. Firstly, the skin regions of sign language video were detected through corresponding CNN templates by using the skin color information characteristics. Secondly, CNN based motion detection was carried out on the skin detection results by using inter-frame difference algorithm, and then the initial gesture region could be obtained. Finally, morphological processing methods were employed to fill small holes and smooth the boundaries of regions, and eventually the segmentation of the face and hands regions of sign language video image sequence was realized. The results show that the method can rapidly and accurately segment sign language video.
出处
《计算机应用》
CSCD
北大核心
2013年第2期503-506,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(11072099)
甘肃省自然科学基金资助项目(112RJZA033)
关键词
细胞神经网络
手语
视频分割
肤色检测
运动检测
Cellular Neural Network （CNN）
sign language
video segmentation
skin color detection
motion detection
