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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

Author: Zappella, Luca
Llad贸 Bardera, Xavier
Salvi Mas, Joaquim
Abstract: 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
Document access: http://hdl.handle.net/2072/58684
Language: eng
Publisher: IEEE
Rights: Tots els drets reservats
Subject: Imatges -- Processament
Imatges -- Transmissi贸
Visi贸 per ordinador
Computer vision
Image processing
Image transmission
Title: Enhanced Model Selection for motion segmentation
Type: info:eu-repo/semantics/article
Repository: Recercat

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