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

运动图像分析统计优化和模型选择的新进展

• 批准号: 13680432
• 负责人: KANATANI Kenichi
• 金额: $ 2.69万
• 依托单位: OKAYAMA UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13680432/
• 关键词: 3-D reconstruction; geometric model selection; statistical optimization; gauge theory; multibody motion segmentation; image matching; optical flow; ellipse fitting; 幾何学モデル選択; ビデオ画像処理; ロバスト推定; 幾何学的信頼性評価; 動画像解析; モデル選択規準

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.

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，并推导出模型选择标准，如“几何AIC”和“几何MDL”。我们还提出了一个“规范理论”来分析重建形状的归一化对其可靠性评估的影响。图像三维重建的统计优化我们建立了一个从两幅图像重建三维形状和从光流重建三维形状的优化系统。然后，我们使用真实图像对它们的性能进行评估和比较。图像之间的自动对应检测我们开发了一种技术，用于自动匹配在两个图像中独立检测到的特征点。这是一种多阶段技术，迭代地升级包含各种全局约束的试探性匹配。将该技术应用于多幅图像的图像拼接和场景和物体的三维重建。视频序列中运动对象的分离我们开发了一种用于检测视频序列中的运动对象的技术，其中对象和背景都是独立运动的。我们设计了一种自动剔除离群轨迹的统计检验方法和一种自动选择最合适的运动分离数学条件的方案。用真实的视频图像验证了OUT方法是非常有效的。

项目成果

K.Kanatani: "Gauges and gauge transformation for uncertainty description of geometric structure with indeterminacy"IEEE Transactions on Information Theory. 47・5. 2017-2028 (2001)

K.Kanatani：“具有不确定性的几何结构的不确定性描述的规范和规范变换”IEEE Transactions on Information Theory 2017-2028（2001）。

K.Kanatani: "Evaluation and selection of models for motion segmentation"Memoirs of the Faculty of Engineering, Okayama University, Vol.36, No.1. 79-90 (2001)

K.Kanatani：“运动分割模型的评估和选择”冈山大学工学部回忆录，第36卷，第1期。

K.Kanatani: "Motion segmentation by subspace separation : Model selection and reliability evaluation"International Journal of Image and Graphics. Vol.2, No.2. 179-197 (2002)

K.Kanatani：“子空间分离的运动分割：模型选择和可靠性评估”国际图像与图形杂志。

Y.Sugaya: "Outlier removal for motion tracking by subspase separation"Memoirs of the Faculty of Engineering, Okayama University. 3・1. 37-44 (2002)

Y.Sugaya：“通过子空间分离进行运动跟踪的异常值去除”冈山大学工学部回忆录3・1（2002）。

Y. Kanazawa et al.: "Do we really have to consider covariance matrices for image feature points?"Electronic and Communications in Japan, Part 3, Vol.86, No. 1. 1-10 (2003)

Y. Kanazawa 等人：“我们真的必须考虑图像特征点的协方差矩阵吗？”Electronic and Communications in Japan，第 3 部分，Vol.86，No. 1. 1-10 (2003)