Construction of System of 3-D Modeling and Virtual Image Generation for Mixed Reality

本研究旨在为计算机视觉和混合现实提供新的可能性，包括以下主题。主题包括三维建模和虚拟图像生成的神经网络实现、从扫描电子显微镜的阴影图像中获取形状、从图像数据库中识别形状和生成自由视点图像、利用鲁棒背景减相法提取快速照明变化的运动目标、以及在固定视点下基于粒子滤波的运动跟踪改进和高效方法。利用神经网络对彩色物体图像进行扩展，利用形状信息恢复颜色反射率因子，生成任意光源方向和任意视点的混合现实虚拟图像。与以往基于CG模型的方法相比，开发的基于神经网络的方法具有更好的真实感。以Hopfield神经网络为优化方法，开发了从扫描电镜中恢复形状的方法。提出了一种基于PCA的姿态识别从CG数据库生成自由视点图像的新方法。为了更好地对运动目标进行分割和跟踪，提出并发展了快速光照变化下的运动目标鲁棒分割和基于粒子滤波的多固定摄像机分割方法。这些结果为研究课题提供了很好的可能性。

This research aims the new possibility for computer vision and mixed reality that includes the following topics. The topics are Neural network implementation of 3-D modeling and virtual image generation, obtaining shape from shading images from Scanning Electron Microscope, Recognition of shape and generation of free view point image from image database, Extracting moving object with robust background subtraction for quick illumination change, and Improvement and efficient approach with particle filter based motion tracking approach under fixed view point. Neural network is used to the extension for color object image, where the color reflectance factor is recovered with shape information, in addition, virtual image with any light source direction and any view point is generated for the mixed reality. Developed NN based approach gives better reality in comparison with previous CG model based approaches. Recovering shape from SEM was developed with Hopfield neural network as the optimization approach. A new approach to generate free viewpoint image from CG database is also proposed using pose recognition with PCA from CG image database. For the purpose of better segmentation and tracking of moving object, robust segmentation of moving object under the quick illumination changes and particle filter based approach with multiple fixed cameras are also proposed and developed. These results could have good possibility for the research topics.