A Motion-Aided Ultrasound Image Sequence Segmentation

Casaburi, Daniela; D’Amore, Luisa; Marcellino, Livia; Murli, A.

doi:10.1007/978-3-642-11795-4_22

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…An analysis of the convergenge and accuracy order of those schemes in our setting is provided in [8]. Here, we use a semi-implicit scheme, since it is invariant to many transformations, such as grey level shift, translation, rotation, etc., and enjoys consistency, convergency and stability properties [4,20]. Now, we consider a uniform grid of values of the scale variable θ, with step-size h θ .…”

Section: Numerical Approachmentioning

confidence: 99%

“…Because of the well known high computational complexity required for solving the optical flow problem, several approaches for efficient parallel software implementations have been proposed. Among them, multicore-cluster approaches [9], parallel pipeline procedures [5], domain decomposition strategies [14]. Recently, on the track of current developments of Graphics Processing Units (GPUs) for High Performance Computing (HPC) [6,7,10], motion estimation has been accelerated, with good results, in [15,17].…”

Section: Introductionmentioning

confidence: 99%

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Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

Cuomo

Galletti

Giunta

et al. 2015

Procedia Computer Science

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In this work we present a multi-level parallel framework for the Optical Flow computation on a GPUs cluster, equipped with a scientific computing middleware (the PetSc library). Starting from a flow-driven isotropic method, which models the optical flow problem through a parabolic partial differential equation (PDE), we have designed a parallel algorithm and its software implementation that is suitable for heterogeneous computing environments (multiprocessor, single GPU and cluster of GPUs). The proposed software has been tested on real SAR images sequences. Numerical experiments highlight the performance of the proposed software framework, which can reach a gain of about 95% with respect to the sequential implementation.

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Section: Numerical Approachmentioning

confidence: 99%