Detecting Correspondences between Video Image Frames and Upgrading Scene Analysis Using Them

检测视频图像帧之间的对应关系并使用它们升级场景分析

• 批准号: 15500113
• 负责人: KANATANI Kenichi
• 金额: $ 2.43万
• 依托单位: OKAYAMA UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15500113/
• 关键词: video image processing; image matching; mulitibody motion segmentation; 3-D reconstruction; geometric fitting; geometric model selection; 幾何学的当てはめ; 幾何学的モデル選択; ロバスト推定; video image processing; image matching; mulitibody motion segmentation; 3-D reconstruction; geometric fitting; geometric model selection; 幾何学的当てはめ; 幾何学的モデル選択; ロバスト推定

1.Correspondence detection between imagesWe proposed new methods for automatically extracting feature points in two images and automatically detecting correspondences between them, combining voting schemes based on geometric constraints and global consistency conditions. We supplemented them by devising a technique for generating denser feature correspondences using template matching and a technique for detecting mismatches using tentative 3-D reconstruction.2.Separation of moving objects in video imagesWe created a new method for extending the feature point trajectories partially tracked through a video stream by estimating the missing parts. We also created an effective method for separating the tracked trajectories into a background part and an independently moving object part. We also proposed a technique for detecting moving object regions by estimating the motion of the background and subtracting it from the individual video frames.3.3-D reconstruction from imagesWe devised robust techniques for 3-D reconstruction from a video stream and from two separate images. We also derived a robust technique for 3-D reconstruction from a single image using knowledge about parallelism and orthogonality relations in the scene. Furthermore, we created a new technique for displaying the reconstructed 3-D shape in such a way that its shape characteristics are preserved.4.Optimal estimation and model selection for geometric inferencesA considerable progress is made in mathematical analysis of optimal parameter estimation and model selection, and the meaning of the model selection criteria, called the geometric AIC and the geometric MDL, and the theoretical accuracy bound, called the KCR lower bound, that the principal investigator proposed is made clear.

1.映像之间的对应检测，我们提出的新方法是在两个图像中自动提取特征点并自动检测它们之间的对应关系，并根据几何约束和全球一致性条件组合投票方案。我们通过设计一种使用模板匹配和使用暂定3-D重建的技术来生成较密集的功能对应的技术来补充它们。2。视频图像中移动对象的分离我们创建了一种新方法，创建了一种通过视频流进行部分跟踪的特征点轨迹的新方法，通过估计缺失的零件来进行部分跟踪。我们还创建了一种将轨迹轨迹分开为背景部分和独立移动对象部分的有效方法。我们还提出了一种通过估计背景运动并从单个视频框架中减去动作的技术来检测移动对象区域的技术。3.3-d从images We Desight We为从视频流和两个单独的图像中设计的3-D重建的强大技术。我们还使用有关场景中的并行性和正交关系的知识从单个图像中得出了一种可靠的3D重建技术。 Furthermore, we created a new technique for displaying the reconstructed 3-D shape in such a way that its shape characteristics are preserved.4.Optimal estimation and model selection for geometric inferencesA considerable progress is made in mathematical analysis of optimal parameter estimation and model selection, and the meaning of the model selection criteria, called the geometric AIC and the geometric MDL, and the theoretical accuracy bound, called the KCR lower bound, that the提议的首席研究员已清楚。