激光与光电子学进展, 2016, 53 (6): 060003, 网络出版: 2016-06-06
RGB-D图像分类方法研究综述 
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Review on RGB-D Image Classification
图像处理 目标识别 场景分类 特征提取 RGB-D图像 image processing object recognition scene classification feature extraction Kinect Kinect RGB-D image
摘要
采用新型3D传感器能够便捷地同时获取多场景、多视觉和多目标彩色和深度信息的RGB-D图像，利用其在物体重叠和遮挡下深度信息对颜色和亮度的不变特点，有效提高RGB-D图像分类的精度。对微软Kinect设备的发展及原理做详细介绍；介绍了现有的RGB-D数据集；对现有RGB-D图像特征提取与分类方法进行了归纳、分析和比较；阐述RGB-D图像分类的发展趋势。
Abstract
The color and depth information of multi-scenario, multi-vision and multiple target in the RGB-D images are conveniently obtained using a new 3D sensor at the same time. The RGB-D image classification accuracy is effectively improved using the depth information invariant characteristics of color and brightness, when the objects overlap and occlusion occurs. The development and theory of Microsoft Kinect are introduced in detail, and the existing RGB-D datasets are described. Then the feature extraction and classification methods are summarized, analyzed and compared. The development trend of RGB-D image classification is discussed.
涂淑琴, 薛月菊, 梁云, 黄宁, 张晓. RGB-D图像分类方法研究综述[J]. 激光与光电子学进展, 2016, 53(6): 060003. Tu Shuqin, Xue Yueju, Liang Yun, Huang Ning, Zhang Xiao. Review on RGB-D Image Classification[J]. Laser & Optoelectronics Progress, 2016, 53(6): 060003.
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