Department of Informatics, University of Thessaloniki, Thessaloniki, Greece.
IEEE Trans Image Process. 1998;7(5):693-702. doi: 10.1109/83.668026.
Various approaches have been proposed for simultaneous optical flow estimation and segmentation in image sequences. In this study, the moving scene is decomposed into different regions with respect to their motion, by means of a pattern recognition scheme. The inputs of the proposed scheme are the feature vectors representing still image and motion information. Each class corresponds to a moving object. The classifier employed is the median radial basis function (MRBF) neural network. An error criterion function derived from the probability estimation theory and expressed as a function of the moving scene model is used as the cost function. Each basis function is activated by a certain image region. Marginal median and median of the absolute deviations from the median (MAD) estimators are employed for estimating the basis function parameters. The image regions associated with the basis functions are merged by the output units in order to identify moving objects.
已经提出了各种方法来实现图像序列中同时的光流估计和分割。在这项研究中，通过模式识别方案，将运动场景根据其运动分解为不同的区域。所提出的方案的输入是表示静止图像和运动信息的特征向量。每个类别对应于一个运动物体。所采用的分类器是中值径向基函数（MRBF）神经网络。从概率估计理论导出的误差准则函数，并表示为运动场景模型的函数，用作代价函数。每个基函数由某个图像区域激活。使用边缘中值和中值绝对偏差（MAD）估计器来估计基函数参数。与基函数相关联的图像区域通过输出单元合并，以便识别运动物体。
