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，其中分析从多视图图像导出的对极几何以提取深度信息：一种是立体图像，另一种是多视图系统的特殊情况，即拍摄存在镜子的场景的单眼图像。在立体视觉方法中，通过相关技术计算重要边缘的视差。，以及行线中两条边之间的点对边缘的视差进行线性插值，并对插值的视差进行校正，使其与垂直扫描获得的视差一致。实验结果表明，该方法简单地近似了深度几乎均匀的区域，并且将前景区域与背景分开。另一种使用镜子的方法是基于仿射变换形成，在这种方法中，当给出桌子上物体的四个点和它们在镜子上反射的对应点时，可以确定二维点。投影到桌子上另一个 3-D 物体的图像平面上，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 3-D 物体的 CG 动画。其次，从 3-D 物体模型重建。研究了基于 3-D 几何图像的图像合成。提出了一种基于模型的体积匹配方法来提取人体运动，并证明可以通过将模型与由四个构建的体素数据进行匹配来识别每个姿势的运动参数。来自不同的图像第三，研究了通过轮廓进行目标跟踪以提取深度信息。在该方法中，提出了一种基于Hopfield NN 的新区域分割方法。将边界像素嵌入到网络的目标函数中，并得到最小化该函数的分割，以实现快速、稳定的全局区域分割，实验结果表明该方法对于区域提取是有效的。和运动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，通过分层匹配算法在对象图像序列中半自动地跟踪它们。一种主动轮廓方法，为了合成两个图像序列而没有运动不兼容，根据轮廓跟踪过程中提取的运动参数自动确定要叠加的对象的位置和放大率。一种用于混合边界的新软键。还提出了颜色来应对运动模糊较少。