Motion segmentation using GPCA techniques and optical flow

In this work, the use of the Generalized Principal Components Analysis (G-PCA) to improve the segmentation of moving objects in image sequences is proposed. In order to obtain this improvement, the noise components in the image derivatives are reduced, and afterwards, a method based on linear algebra is used to make the segmentation. Furthermore this work presents diverse tests to compare the results reached with and without the noise reduction in the image derivatives, and using the nonlinear minimization of an error function. A remarkable improvement in the segmentation quality and the processing time can be observed in every experiment when using the proposed method.