A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Segmentation of Dynamic Scenes from Image Intensities
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
GPCA: an efficient dimension reduction scheme for image compression and retrieval
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
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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.