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

本项目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，研究了两种从多视点图像中提取深度信息的方法：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目图像。在立体视觉方法中，利用相关技术计算重要边缘的视差，对行线上两个边缘之间的点进行视差线性插值，并对插值后的视差进行校正，使其与垂直扫描得到的视差一致。实验结果表明，该方法可以简单地逼近深度几乎均匀的区域，并且可以将前景区域与背景区域分离开来。另一种方法是基于仿射变换 关于我们 阵在该方法中，当给定桌面上的物体的四个点及其在镜子上反射的对应点时，可以确定投影在桌面上的另一个三维物体的像平面上的二维点。这意味着可以从一幅特殊的单目图像中生成真实的视频和运动中的三维物体的CG动画。其次，研究了基于三维几何的图像三维物体模型重建方法。提出了一种基于模型的体匹配方法来提取人体运动。通过对不同视点的四幅图像构建的体素数据与模型进行匹配，可以识别出每个姿态的运动参数。第三，研究了基于轮廓的深度信息提取方法。对象跟踪使得能够将场景中的前景区域与背景区域分离。提出了一种基于Hopfield神经网络的区域分割方法。将边界像素的一些性质嵌入到网络的目标函数中，得到最小化该函数的分割结果。为了实现快速、稳定、全局的区域分割，采用了金字塔图像。实验结果表明，该方法能有效地提取出运动目标，并能有效地跟踪运动目标，同时提出了一种新的无运动不相容性的图像合成方法。在该方法中，当要叠加的对象的轮廓在第一帧中被指定一次时，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。为了合成两幅图像序列而不产生运动不相容性，根据轮廓跟踪过程中提取的目标运动参数，自动确定待叠加目标的位置和放大倍数。提出了一种新的边界颜色混合软键来科普运动模糊。少

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