Thumbnail
Access Restriction
Open

Author Ghosh, Pratim ♦ Manjunath, B. S.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Sparse Error Reconstruction ♦ Robust Simultaneous Registration ♦ Consecutive Frame ♦ Fast Implementation ♦ Efficient Variational Framework ♦ Biological Image Sequence ♦ Extensive Experiment ♦ Index Term Segmentation ♦ Partial Occlusion ♦ Simultaneous Registration ♦ Various State-of-the-art Method ♦ Wide Variety ♦ Segmentation Functional ♦ Image Sequence ♦ Dual-rof Model ♦ Dual Rudin-osher-fatemi ♦ Dense Correspondence Map ♦ Sparse Nature ♦ Nonparametric Shape
Abstract Abstract—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—Segmentation, registration, tracking, optimization Ç 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study