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Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation

IEEE

Autor: Zappella, Luca
Lladó Bardera, Xavier
Salvi Mas, Joaquim
Resum: In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation
Accés al document: http://hdl.handle.net/2072/58684
Llenguatge: eng
Editor: IEEE
Drets: Tots els drets reservats
Matèria: Imatges -- Processament
Imatges -- Transmissió
Visió per ordinador
Computer vision
Image processing
Image transmission
Títol: Enhanced Model Selection for motion segmentation
Tipus: info:eu-repo/semantics/article
Repositori: Recercat

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