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Disparity Map Generation Based on Trapezoidal Camera Architecture for Multi-View Video

机译:基于梯形相机架构的视差图生成多视点视频

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Visual content acquisition is a strategic functional block of any visual system. Despite its wide possibilities, the arrangement of cameras for the acquisition of good quality visual content for use in multi-view video remains a huge challenge. This paper presents the mathematical description of trapezoidal camera architecture and relationships which facilitate the determination of camera position for visual content acquisition in multi-view video, and depth map generation. The strong point of Trapezoidal Camera Architecture is that it allows for adaptive camera topology by which points within the scene, especially the occluded ones can be optically and geometrically viewed from several different viewpoints either on the edge of the trapezoid or inside it. The concept of maximum independent set, trapezoid characteristics, and the fact that the positions of cameras (with the exception of few) differ in their vertical coordinate description could very well be used to address the issue of occlusion which continues to be a major problem in computer vision with regards to the generation of depth map.
机译:视觉内容获取是任何视觉系统的战略功能块。尽管具有广泛的可能性,但要获得用于多视图视频的高质量视觉内容的摄像头布置仍然是巨大的挑战。本文介绍了梯形摄像机架构及其关系的数学描述,这些关系有助于确定摄像机位置,以便在多视图视频中获取视觉内容并生成深度图。梯形相机架构的优点是它允许自适应相机拓扑结构,通过该拓扑结构,可以从梯形边缘或其内部的多个不同视点以光学和几何方式查看场景中的点,尤其是遮挡的点。最大独立集的概念,梯形特征以及摄像机的位置(少数摄像机除外)在其垂直坐标描述中有所不同这一事实很容易用于解决遮挡问题,而遮挡问题仍然是一个主要问题。关于深度图生成的计算机视觉。

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