2,909 research outputs found
Deep Feature-based Face Detection on Mobile Devices
We propose a deep feature-based face detector for mobile devices to detect
user's face acquired by the front facing camera. The proposed method is able to
detect faces in images containing extreme pose and illumination variations as
well as partial faces. The main challenge in developing deep feature-based
algorithms for mobile devices is the constrained nature of the mobile platform
and the non-availability of CUDA enabled GPUs on such devices. Our
implementation takes into account the special nature of the images captured by
the front-facing camera of mobile devices and exploits the GPUs present in
mobile devices without CUDA-based frameorks, to meet these challenges.Comment: ISBA 201
GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy
Online adaptive radiation therapy (ART) has great promise to significantly
reduce normal tissue toxicity and/or improve tumor control through real-time
treatment adaptations based on the current patient anatomy. However, the major
technical obstacle for clinical realization of online ART, namely the inability
to achieve real-time efficiency in treatment re-planning, has yet to be solved.
To overcome this challenge, this paper presents our work on the implementation
of an intensity modulated radiation therapy (IMRT) direct aperture optimization
(DAO) algorithm on graphics processing unit (GPU) based on our previous work on
CPU. We formulate the DAO problem as a large-scale convex programming problem,
and use an exact method called column generation approach to deal with its
extremely large dimensionality on GPU. Five 9-field prostate and five 5-field
head-and-neck IMRT clinical cases with 5\times5 mm2 beamlet size and
2.5\times2.5\times2.5 mm3 voxel size were used to evaluate our algorithm on
GPU. It takes only 0.7~2.5 seconds for our implementation to generate optimal
treatment plans using 50 MLC apertures on an NVIDIA Tesla C1060 GPU card. Our
work has therefore solved a major problem in developing ultra-fast
(re-)planning technologies for online ART
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