GPU-Based Fluid Motion Estimation Using Energy Constraint

2016 ◽  
Vol 26 (02) ◽  
pp. 1750022 ◽  
Author(s):  
Siyuan Xu ◽  
Han Zhuang ◽  
Xin Fu ◽  
Junlong Zhou ◽  
Mingsong Chen
Keyword(s):  
Motion Estimation ◽  
Graphics Processing Unit ◽  
Fluid Motion ◽  
Fluid Flows ◽  
Brightness Distribution ◽  
Processing Unit ◽  
Consistency Constraints ◽  
Distribution Matrix ◽  
Complex Fluid ◽  
Graphics Processing

Although motion estimation (ME) approaches for fluid flows have been widely studied in computer vision domain, most existing ME algorithms cannot accurately deal with regions with both slight and drastic brightness changes. To address this issue, this paper introduces a novel data structure called brightness distribution matrix (BDM) which can be used to accurately model regional brightness. Based on our proposed consistency constraints and energy function, we can obtain motion vectors from image sequences with high accuracy. Since the BDM-based ME approach requires a large number of computations when dealing with complex fluid scenarios, to reduce the overall ME time, a parallelized version of our approach is developed based on graphics processing unit (GPU). Experimental results show that our GPU-based approach not only can be used to improve the ME quality for complex fluid images, but also can reduce the overall ME processing time (up to 7.06 times improvement).

Bi-predictive motion estimation for HEVC on a graphics processing unit (GPU)

2014 ◽  
Vol 60 (4) ◽  
pp. 728-736 ◽  
Author(s):  
Stefan Radicke ◽  
Jens-Uwe Hahn ◽  
Qi Wang ◽  
Christos Grecos
Keyword(s):  
Motion Estimation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Graphics Processing

Fast motion estimation for HEVC on graphics processing unit (GPU)

2015 ◽  
Vol 12 (2) ◽  
pp. 549-562 ◽  
Author(s):  
Dongkyu Lee ◽  
Donggyu Sim ◽  
Keeseong Cho ◽  
Seoung-Jun Oh
Keyword(s):  
Motion Estimation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Fast Motion Estimation ◽  
Fast Motion ◽  
Graphics Processing

Implementing wide baseline matching algorithms on a graphics processing unit.

2007 ◽  
Author(s):  
Fredrick H. Rothganger ◽  
Kurt W. Larson ◽  
Antonio Ignacio Gonzales ◽  
Daniel S. Myers
Keyword(s):  
Graphics Processing Unit ◽  
Processing Unit ◽  
Wide Baseline Matching ◽  
Graphics Processing

scholarly journals Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

2021 ◽  
Vol 22 (10) ◽  
pp. 5212
Author(s):  
Andrzej Bak
Keyword(s):  
Molecular Conformation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Diverse Range ◽  
Current State ◽  
Gpu Clusters ◽  
Pharmacophore Hypothesis ◽  
Rising Power ◽  
Graphics Processing ◽  
Ligand Conformation

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

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