scholarly journals Real-Time Segmentation of Stereo Videos on a Portable System With a Mobile GPU

2012 ◽  
Vol 22 (9) ◽  
pp. 1292-1305 ◽  
Author(s):  
Alexey Abramov ◽  
Karl Pauwels ◽  
Jeremie Papon ◽  
Florentin Worgotter ◽  
Babette Dellen
Keyword(s):  
Real Time ◽  
Mobile Gpu ◽  
Portable System ◽  
Time Segmentation

scholarly journals Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking

2017 ◽  
pp. 84-95 ◽  
Author(s):  
Luis C. García-Peraza-Herrera ◽  
Wenqi Li ◽  
Caspar Gruijthuijsen ◽  
Alain Devreker ◽  
George Attilakos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Surgical Tools ◽  
Time Segmentation

Real-Time Segmentation of Surgical Tools and Needle Using a Mobile-U-Net

2021 ◽  
Author(s):  
Jakob Kristian Holm Andersen ◽  
Kim Lindberg Schwaner ◽  
Thiusius Rajeeth Savarimuthu
Keyword(s):  
Real Time ◽  
Surgical Tools ◽  
Time Segmentation

Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform

Biometrics ◽  
2017 ◽  
pp. 761-777
Author(s):  
Di Zhao
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Gpu Computing ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Computational Results ◽  
Real Time Processing ◽  
Time Processing ◽  
Mobile Gpu

Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352×288, 480×320, 720×480 and 1280×720, while cDWT can only obtain read-time processing with small image sizes of 352×288 and 480×320.

Real-Time Segmentation of Depth Map Frames on Mobile Devices

2019 ◽  
Author(s):  
Florica Moldoveanu ◽  
Andrei Ciobanu ◽  
Anca Morar ◽  
Alin Moldoveanu ◽  
Victor Asavei
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Depth Map ◽  
Time Segmentation

High-density CCD neurocomputer chip for accurate real-time segmentation of noisy images

1991 ◽  
Author(s):  
Michael W. Roth ◽  
K. E. Thompson ◽  
Robert L. Kulp ◽  
E. Brian Alvarez
Keyword(s):  
Real Time ◽  
High Density ◽  
Noisy Images ◽  
Time Segmentation
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