Hadian Jazi Marjan, Bab-Hadiashar Alireza, Hoseinnezhad Reza
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Corner of Plenty Road and McKimmies Road, Bundoora, Victoria 3083, Australia.
ScientificWorldJournal. 2013 Nov 21;2013:878417. doi: 10.1155/2013/878417. eCollection 2013.
Motion segmentation is an important task in computer vision and several practical approaches have already been developed. A common approach to motion segmentation is to use the optical flow and formulate the segmentation problem using a linear approximation of the brightness constancy constraints. Although there are numerous solutions to solve this problem and their accuracies and reliabilities have been studied, the exact definition of the segmentation problem, its theoretical feasibility and the conditions for successful motion segmentation are yet to be derived. This paper presents a simplified theoretical framework for the prediction of feasibility, of segmentation of a two-dimensional linear equation system. A statistical definition of a separable motion (structure) is presented and a relatively straightforward criterion for predicting the separability of two different motions in this framework is derived. The applicability of the proposed criterion for prediction of the existence of multiple motions in practice is examined using both synthetic and real image sequences. The prescribed separability criterion is useful in designing computer vision applications as it is solely based on the amount of relative motion and the scale of measurement noise.
运动分割是计算机视觉中的一项重要任务，并且已经开发出了几种实用方法。运动分割的一种常见方法是使用光流，并利用亮度恒定约束的线性近似来制定分割问题。尽管有许多解决此问题的方法，并且已经研究了它们的准确性和可靠性，但分割问题的确切定义、其理论可行性以及成功进行运动分割的条件仍有待推导。本文提出了一个简化的理论框架，用于预测二维线性方程组分割的可行性。给出了可分离运动（结构）的统计定义，并推导了在此框架中预测两种不同运动可分离性的相对直接的准则。使用合成图像序列和真实图像序列检验了所提出准则在实际中预测多种运动存在的适用性。规定的可分离性准则在设计计算机视觉应用中很有用，因为它仅基于相对运动量和测量噪声的规模。
