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Acquisition of Depth Information form Motion Images Taken in Natural Scene and Their Synthesis.

从自然场景中拍摄的运动图像获取深度信息及其合成。

基本信息

批准号：
09650402
负责人：
NAGAHASHI Hiroshi
金额：
$ 2.11万
依托单位：
TOKYO INSTITUTE OF TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1998
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09650402/
关键词：
depth information epipolar geometry stereo vision affine transformation voxel data object tracking Hopfield neural network image synthesis 多視点画像鏡面画像動画像多視点ステレオ視視差マップ

项目摘要

In this project, several methods for acquiring depth information from images were investigated and an image synthesis method using the information was proposed.Firstly, two approaches, in which epipolar geometry derived from multi-view images was analyzed to extract depth information, were investigated : one is stereo images and the other is a special case of multi-view system, that is, a monocular image taking a scene in which there exists a mirror. In the approach of stereo vision, disparities on significant edges were calculated by a correlation technology, and a point between two edges in a row line is linearly interpolated its disparity from those of the edges, and the interpolated disparity is corrected so as to be consistent with that obtained by vertical scan. Experimental results showed that this method simply approximate a region with almost homogeneous depth and also separate foreground regions from backgrounds ones. The other approach using a mirror is based on affine trans … More formation. In this method, when four points of an object on desk and their corresponding points reflected on the mirror are given, it is possible to determine 2-D points projected on the image plane of another 3-D object on the desk. This means that CG animation of a real video and a 3-D object in motion can be generated from a special monocular image.Secondly, 3-D object model reconstructions from images based on 3-D geometry were investigated for image synthesis. A model based volume matching method to extract motion of human being was proposed. It was proved to be possible to identify motion parameters for each pose by matching models with voxel data constructed by four images from different viewpoints.Thirdly, object tracking by contours to extract depth information were investigated. The object tracking enables to separate foreground regions from background ones in a scene. In this approach, a new region segmentation method based on the Hopfield NN was proposed. Some properties of boundary pixels were embedded in an objective function of the network and the segmentation which minimizes this function was obtained. In order to realize a fast, stable and global region segmentation, pyramid images were used. Experimental results showed that this method is effective for region extraction and motion tracking.A new image synthesis method without motion incompatibility was also proposed. In this method, when the contours of objects to be superimposed are specified once in the first frame, they are semi-automatically tracked in object image sequence by a hierarchical matching algorithm and an active contour method. In order to synthesize two image sequences without motion incompatibility, position and magnification ratio of an object to be superimposed are determined automatically based on its motion parameters extracted in the process of contour tracking. A new soft-key for blending boundary colors was also proposed to cope with motion blurs. Less

在这个项目中，研究了从图像中获取深度信息的几种方法，并提出了一种利用这些信息的图像合成方法。首先，研究了两种方法，其中分析从多视点图像导出的对极几何来提取深度信息：一种是立体图像，另一种是多视点系统的特殊情况，即拍摄存在镜子的场景的单目图像。在立体视觉的方法中，通过相关技术计算重要边缘上的视差，并从该边缘的视差中线性插值出行线中两个边缘之间的点的视差，并对插值后的视差进行校正，以使其与垂直扫描得到的视差一致。实验结果表明，该方法简单地近似了具有几乎均匀深度的区域，并且还将前景区域与背景区域分开。另一种使用镜子的方法是基于仿射反式形成。在该方法中，当给定桌子上的物体的四个点和它们在镜子上反射的对应点时，可以确定投影在桌子上的另一个3D物体的图像平面上的2D点。这意味着可以从特殊的单目图像生成真实视频和运动中的 3D 物体的 CG 动画。其次，研究了基于 3D 几何的图像重建 3D 物体模型以进行图像合成。提出了一种基于模型的体匹配方法来提取人体运动。事实证明，通过将模型与由不同视点的四幅图像构建的体素数据进行匹配来识别每个姿势的运动参数是可能的。第三，研究了通过轮廓进行目标跟踪以提取深度信息。对象跟踪能够将场景中的前景区域与背景区域分开。在该方法中，提出了一种基于Hopfield NN的新的区域分割方法。边界像素的一些属性被嵌入到网络的目标函数中，并获得了最小化该函数的分割。为了实现快速、稳定的全局区域分割，使用了金字塔图像。实验结果表明该方法对于区域提取和运动跟踪是有效的。还提出了一种新的无运动不兼容的图像合成方法。在该方法中，当在第一帧中指定一次要叠加的对象的轮廓时，通过分层匹配算法和活动轮廓方法在对象图像序列中半自动地跟踪它们。为了合成没有运动不兼容的两个图像序列，根据轮廓跟踪过程中提取的运动参数自动确定待叠加对象的位置和放大比。还提出了一个用于混合边界颜色的新软键来应对运动模糊。较少的