Browsing by Subject "Model based approach"

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    Data and model driven hybrid approach to activity scoring of cyclic pathways
    (Springer, Dordrecht, 2010) Işık, Z.; Atalay V.; Aykanat, Cevdet; Çetin-Atalay, Rengül
    Analysis of large scale -omics data based on a single tool remains inefficient to reveal molecular basis of cellular events. Therefore, data integration from multiple heterogeneous sources is highly desirable and required. In this study, we developed a data- and model-driven hybrid approach to evaluate biological activity of cellular processes. Biological pathway models were taken as graphs and gene scores were transferred through neighbouring nodes of these graphs. An activity score describes the behaviour of a specific biological process was computed by owing of converged gene scores until reaching a target process. Biological pathway model based approach that we describe in this study is a novel approach in which converged scores are calculated for the cellular processes of a cyclic pathway. The convergence of the activity scores for cyclic graphs were demonstrated on the KEGG pathways. © 2011 Springer Science+Business Media B.V.
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    Motion capture and human pose reconstruction from a single-view video sequence
    (Academic Press, 2013) Güdükbay, Uğur; Demir, I.; Dedeoǧlu, Y.
    We propose a framework to reconstruct the 3D pose of a human for animation from a sequence of single-view video frames. The framework for pose construction starts with background estimation and the performer's silhouette is extracted using image subtraction for each frame. Then the body silhouettes are automatically labeled using a model-based approach. Finally, the 3D pose is constructed from the labeled human silhouette by assuming orthographic projection. The proposed approach does not require camera calibration. It assumes that the input video has a static background, it has no significant perspective effects, and the performer is in an upright position. The proposed approach requires minimal user interaction. © 2013 Elsevier Inc.