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Robust Simultaneous Registration and Segmentation with Sparse Error Reconstruction
Feb. 2013 (vol. 35 no. 2)
pp. 425-436
P. Ghosh, Microsoft Corp., Redmond, WA, USA
B. S. Manjunath, Dept. of Electr. & Comput. Eng., Univ. of California Santa Barbara, Santa Barbara, CA, USA
We introduce a fast and efficient variational framework for Simultaneous Registration and Segmentation (SRS) applicable to a wide variety of image sequences. We demonstrate that a dense correspondence map (between consecutive frames) can be reconstructed correctly even in the presence of partial occlusion, shading, and reflections. The errors are efficiently handled by exploiting their sparse nature. In addition, the segmentation functional is reformulated using a dual Rudin-Osher-Fatemi (ROF) model for fast implementation. Moreover, nonparametric shape prior terms that are suited for this dual-ROF model are proposed. The efficacy of the proposed method is validated with extensive experiments on both indoor, outdoor natural and biological image sequences, demonstrating the higher accuracy and efficiency compared to various state-of-the-art methods.
Index Terms:
image sequences,image reconstruction,image registration,image segmentation,outdoor natural image sequences,sparse error reconstruction,simultaneous registration and segmentation,dense correspondence map,partial occlusion,shading,reflections,sparse nature,segmentation functional,dual Rudin-Osher-Fatemi model,ROF,biological image sequences,Shape,Robustness,Image segmentation,Optical imaging,Image reconstruction,Adaptive optics,Lighting,optimization,Segmentation,registration,tracking
Citation:
P. Ghosh, B. S. Manjunath, "Robust Simultaneous Registration and Segmentation with Sparse Error Reconstruction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2, pp. 425-436, Feb. 2013, doi:10.1109/TPAMI.2012.103
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