New Development of Statistical Optimization and Model Selection for Motion Image Analysis

1.图像三维分析的统计理论我们提出了一个数学上一致的理论来模拟图像噪声的统计特性，用于分析图像三维重建的精度，从图像灰度级估计协方差矩阵，渐近地评估这种分析的精度，并导出诸如“几何AIC”和“几何MDL”的模型选择准则。提出了“规范理论”来分析重构形状的归一化对重构形状可靠性评估的影响.图像三维重建的统计优化我们建立了一个由两幅图像重建三维形状和由光流重建三维形状的优化系统。然后我们使用真实的图像评估和比较它们的性能。图像之间的自动对应检测我们开发了一种自动匹配两幅图像中独立检测到的特征点的技术。这是一个多阶段的技术，迭代地升级试验性的比赛，结合各种全球性的约束。将该技术应用于多幅图像的拼接和场景与物体的三维重建.视频序列中运动目标的分离我们提出了一种在视频序列中检测运动目标的技术，其中目标和背景都是独立运动的。我们设计了一个统计测试方法，自动删除离群轨迹和一个计划，自动选择最合适的运动分离的数学条件。使用真实的视频图像，我们证实了该方法是非常有效的。

1. Statistical theory for 3-D analysis of imagesWe presented a mathematically consistent theory for modeling statistical properties of image noise for analyzing the accuracy of 3-D reconstruction from images, estimating the covariance matrix from the image gray levels, asymptotically evaluating the accuracy of such analysis, and deriving model selection criteria such as "geometric AIC" and "geometric MDL". We also proposed a "gauge theory" for analyzing how the normalization of the reconstructed shape affects its reliability evaluation.2. Statistical optimization of 3-D reconstruction from imagesWe built an optimal system for reconstructing 3-D shape from two images and reconstructing 3-D shape from optical flow. We then evaluated and compared their performance, using real images.3. Automatic correspondence detection between imagesWe developed a technique for automatically matching feature points independently detected in two images. This is a ulti-stage technique, iteratively upgrading tentative matches incorporating various global constraints. We applied our technique to image mosaicing of multiple images and 3-D reconstruction of scenes and objects.4. Separation of moving objects in a video sequenceWe developed a technique for detecting moving objects in a video sequence in which objects and, backgrounds are both moving independently. We devised a statistical testing method for automatically removing outlying trajectories and a scheme for automatically selecting most appropriate mathematical conditions fo the motion separation. Using real video images, we confirmed that out method was very effective.