%0 Journal Article
%T 基于向量场的深度计算方法<br>Vector field based depth acquisition algorithm
%A 路海明
%A 王一娇
%A 谢朝霞
%J 清华大学学报（自然科学版）
%D 2015
%X 基于深度图像可以方便地区分前景和背景, 有效提高自然人机交互的性能。其中基于面结构光技术的深度摄像头实用性强, 得到了迅速发展, 其深度信息获取基于图像块匹配算法, 在计算每个像素点的深度时, 需要在测量范围内进行逐点搜索、图像块匹配和寻优等大量运算, 这些运算要通过高性能计算机或专用并行运算芯片才能达到实时性, 导致了深度摄像头成本的增加。本文研究基于向量场模式识别的深度图算法DepthVH, 通过生成具有深度相关特征的向量场, 将深度信息变换为特征信息, 通过直接识别各个深度点周围的向量场特征, 将该特征信息逆变换为深度信息, 实现类似Hash映射的搜索, 避免了线性搜索匹配算法的巨大运算量。采用DepthVH, 智能电视只要增加一个红外发射元件, 就可以具有自然人机交互功能。<br>Abstract：Scene foregrounds and backgrounds can be separated according to their depth to greatly improve human-computer interaction (HCI). Depth sensors using surface structured light are practical and widely used. The depth information is obtained using an image block matching algorithm, which searches for the optimal matching block in the image. However, the complex computations require high-performance computers or special chips to achieve real-time performance which increase the depth sensor cost. This paper describes a depth acquisition method based on vector field pattern recognition. The scene depth information is obtained by feature recognition of the vector field for every depth point. The vector field is first generated based on the depth features, then the depth information hidden in the vector field is converted to feature information by the pattern recognition algorithm. The depth information is then obtained by feature matching after an inverse transformation. The inverse transformation uses a searching strategy similar to Hash mapping, which avoids the complexity of a linear search. A smart TV with an infrared transmitter can easily realize natural HCI using this method.
%K 深度图
%K 结构光
%K 向量场
%K &lt
%K br&gt
%K depth image
%K structured light
%K vector field
%U http://jst.tsinghuajournals.com/CN/Y2015/V55/I8/916#MetricsTab