摘要
深度图像直接反映景物表面的三维几何信息,且不受光照、阴影等因素的影响,对深度图像处理、识别、理解是目前计算机视觉领域研究的热点和重点之一。针对深度图像信息单一且噪声较大的特点,提出一种基于组合特征的阈值分割算法,实现对深度图像数据的有效分割。算法首先通过梯度特征对图像进行Otsu阈值分割;在此基础上,分别在不同分割区域内利用深度特征进行Otsu多阈值分割,得到候选目标;然后,在空域上利用像素的位置特征对候选目标进行分割、合并与去噪,最终得到图像分割的结果。实验结果表明,该方法能有效克服深度图像中噪声的影响,得到的分割区域边界准确,分割质量较高,为以后的室内对象识别和场景理解工作奠定了较好的基础。
Depth image directly reflects the three-dimensional geometric information of the scene surface and is not affected by factors such as light and shadow.Processing,recognizing,and understanding depth images are currently one of the hot topics and focuses in the field of three-dimensional computer vision.Aiming at the problem that the depth image information is single and the noise is large,a threshold segmentation algorithm based on combined features is proposed to realize effective segmentation of depth image data.The algorithm first performs Otsu threshold segmentation on the image by using gradient features.On this basis,Otsu multi-threshold segmentation is performed using depth features in different segmented regions to obtain candidate targets.Then,in the spatial domain,the depth feature is used to segment,merge,and denoise the candidate targets,thus finally obtaining the segmentation results.Experimental results show that this method can effectively overcome the influence of noise in depth images,the obtained boundary of the segmentation area is accurate,and the segmentation quality is high,which lays a good foundation for future indoor object recognition and scene understanding.
作者
谭志国
欧建平
张军
沈先耿
TAN Zhi guo;OU Jian ping;ZHANG Jun;SHEN Xian geng(College of Electronic Science,National University of Defense Technology,Changsha 410073;Department of Information;Communication,Armed Police College of PAP,Chengdu 610213,China)
出处
《计算机工程与科学》
CSCD
北大核心
2018年第8期1429-1434,共6页
Computer Engineering & Science
基金
国家自然科学基金(61471370
61471371)
博士后科学基金(2012M512168)
关键词
深度场景理解
深度图像分割
Otsu阈值
梯度特征
深度特征
depth scene understanding
depth image segmentation
Otsu threshold
gradient featureldepth feature
