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R. Vidal, “Generalized Principal Component Analysis (GPCA): An Algebraic Geometric Approach to Subspace Clustering and Motion Segmentation,” Ph.D. Thesis, Electrical Engineering and Computer Sciences, University of California at Berkeley, 2003.

has been cited by the following article:

• TITLE: Apply GPCA to Motion Segmentation

  AUTHORS: Hongchuan Yu, Jian Jun Zhang

  KEYWORDS: Generalized PCA, Motion Segmentation, Layer Ordering

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.3 No.1, February 24, 2011

  ABSTRACT: In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the genera-lized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segmentation is solved under an algebra framework. Our main contribution is to propose a post-processing procedure, which can detect the boundaries of motion layers and further determine the layer ordering. Test results on real imagery have confirmed the validity of our method.