Výsledky vyhledávání - "Paul Grégory"

Akademický článek

Beyond co-localization: inferring spatial interactions between sub-cellular structures from microscopy images

Autor: Paul Grégory, Helmuth Jo A, Sbalzarini Ivo F

Publikováno v: BMC Bioinformatics, Vol 11, Iss 1, p 372 (2010)

Abstract Background Sub-cellular structures interact in numerous direct and indirect ways in order to fulfill cellular functions. While direct molecular interactions crucially depend on spatial proximity, other interactions typically result in spatia

Externí odkaz: https://doaj.org/article/4f900365beda4075827b79796b0798b6

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Akademický článek

Numerical and experimental investigation on the melting heat transfer of nanographene-enhanced phase change material composites for thermal energy storage applications

Autor: Kittusamy, Ragul Kumar ^⁎, Rajagopal, Velavan, Felix, Paul Gregory

Publikováno v: In International Journal of Heat and Mass Transfer 1 June 2023 206

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Modelling Point Spread Function in Fluorescence Microscopy with a Sparse Combination of Gaussian Mixture: Trade-off between Accuracy and Efficiency

Autor: Samuylov, Denis K., Purwar, Prateek, Székely, Gábor, Paul, Grégory

Deblurring is a fundamental inverse problem in bioimaging. It requires modelling the point spread function (PSF), which captures the optical distortions entailed by the image formation process. The PSF limits the spatial resolution attainable for a g

Externí odkaz: http://arxiv.org/abs/1809.01579

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A Bayesian framework for the analog reconstruction of kymographs from fluorescence microscopy data

Autor: Samuylov, Denis K., Székely, Gábor, Paul, Grégory

Kymographs are widely used to represent and anal- yse spatio-temporal dynamics of fluorescence markers along curvilinear biological compartments. These objects have a sin- gular geometry, thus kymograph reconstruction is inherently an analog image pr

Externí odkaz: http://arxiv.org/abs/1809.01590

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Towards Closing the Gap in Weakly Supervised Semantic Segmentation with DCNNs: Combining Local and Global Models

Autor: Mayer, Christoph, Timofte, Radu, Paul, Grégory

Generating training sets for deep convolutional neural networks (DCNNs) is a bottleneck for modern real-world applications. This is a demanding task for applications where annotating training data is costly, such as in semantic segmentation. In the l

Externí odkaz: http://arxiv.org/abs/1808.01625

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