Implementation of Optical Flow Analysis System Equipped with Reliability Evaluation

1.新的光流检测方法引入了图像噪声的精确数学模型，提出了一种计算光流并定量评价其可靠性的方案。2.三维重建的计算方案如果一个称为“流矩阵”的矩阵可以通过迭代计算，则重建三维结构。结果表明，对于真实的图像数据，这些迭代的收敛情况比含有人工噪声的模拟数据的收敛要差。3.噪声数据几何推理的数学理论研究结果表明，支持我们的光流三维分析的统计理论可以应用于涉及图像和传感器数据的广泛的一般几何估计问题。我们给出了“最优拟合”的一般理论，其中理论精度界是“Cramer-Rao下界”的一种特殊形式。4.几何信息准则扩展了统计学中使用的AIC，定义了“几何信息准则(AIC)”作为一种度量，用于预测从含噪声的图像数据中可以期望稳健的三维重建的程度。我们将其应用于立体图像分析、运动图像分析和图形界面等问题，验证了该形式化方法的有效性。

1.New optical flow detection methodIntroducing a precise mathematical model of image noise, we presented a scheme for computing optical flow and evaluating its reliability in quantitative terms.2.Robust computational scheme for 3-D reconstructionThe 3-D structure is reconstructed if a matrix called the "flow matrix" can be computed by iterations. The convergence of these iterations turned out to be worse for real image data than for simulated date with artificial noise. We presented a new scheme to guarantee the convergence.3.Mathematical theory of geometric inference from noisy dataIt has turned out that the statistical theory that underlines our 3-D analysis of optical flow can be applied to a wide range of general geometric estimation problems involving image and sensor data. We presented a general theory of "optimal fitting", in which a theoretical accuracy bound was derived as a special form of the "Cramer-Rao lower bound".4.Geometric information criterionExtending the AIC used in statistics, we defined the "geometric information criterion (AIC)" as a measure for predicting to what extend we could expect robust 3-D reconstruction from noisy image data. We applied it to a various problems including stereo image analysis, motion image analysis and graphic interface, confirmed the effectiveness of our formalism.