33,192 research outputs found
Demystifying the Characteristics of 3D-Stacked Memories: A Case Study for Hybrid Memory Cube
Three-dimensional (3D)-stacking technology, which enables the integration of
DRAM and logic dies, offers high bandwidth and low energy consumption. This
technology also empowers new memory designs for executing tasks not
traditionally associated with memories. A practical 3D-stacked memory is Hybrid
Memory Cube (HMC), which provides significant access bandwidth and low power
consumption in a small area. Although several studies have taken advantage of
the novel architecture of HMC, its characteristics in terms of latency and
bandwidth or their correlation with temperature and power consumption have not
been fully explored. This paper is the first, to the best of our knowledge, to
characterize the thermal behavior of HMC in a real environment using the AC-510
accelerator and to identify temperature as a new limitation for this
state-of-the-art design space. Moreover, besides bandwidth studies, we
deconstruct factors that contribute to latency and reveal their sources for
high- and low-load accesses. The results of this paper demonstrates essential
behaviors and performance bottlenecks for future explorations of
packet-switched and 3D-stacked memories.Comment: EEE Catalog Number: CFP17236-USB ISBN 13: 978-1-5386-1232-
No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences
Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives
Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications
Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Challenges in using GPUs for the real-time reconstruction of digital hologram images
This is the pre-print version of the final published paper that is available from the link below.In-line holography has recently made the transition from silver-halide based recording media, with laser reconstruction, to recording with large-area pixel detectors and computer-based reconstruction. This form of holographic imaging is an established technique for the study of fine particulates, such as cloud or fuel droplets, marine plankton and alluvial sediments, and enables a true 3D object field to be recorded at high resolution over a considerable depth.
The move to digital holography promises rapid, if not instantaneous, feedback as it avoids the need for the time-consuming chemical development of plates or film film and a dedicated replay system, but with the growing use of video-rate holographic recording, and the desire to reconstruct fully every frame, the computational challenge becomes considerable. To replay a digital hologram a 2D FFT must be calculated for every depth slice desired in the replayed image volume. A typical hologram of ~100 ÎĽm particles over a depth of a few hundred millimetres will require O(10^3) 2D FFT operations to be performed on a hologram of typically a few million pixels.
In this paper we discuss the technical challenges in converting our existing reconstruction code to make efficient use of NVIDIA CUDA-based GPU cards and show how near real-time video slice reconstruction can be obtained with holograms as large as 4096 by 4096 pixels. Our performance to date for a number of different NVIDIA GPU running under both Linux and Microsoft Windows is presented. The recent availability of GPU on portable computers is discussed and a new code for interactive replay of digital holograms is presented
Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression
In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream
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