3,095 research outputs found
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
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
Miniaturized embedded stereo vision system (MESVS)
Stereo vision is one of the fundamental problems of computer vision. It is also one of the oldest and heavily investigated areas of 3D vision. Recent advances of stereo matching methodologies and availability of high performance and efficient algorithms along with availability of fast and affordable hardware technology, have allowed researchers to develop several stereo vision systems capable of operating at real-time. Although a multitude of such systems exist in the literature, the majority of them concentrates only on raw performance and quality rather than factors such as dimension, and power requirement, which are of significant importance in the embedded settings.
In this thesis a new miniaturized embedded stereo vision system (MESVS) is presented, which is miniaturized to fit within a package of 5x5cm, is power efficient, and cost-effective. Furthermore, through application of embedded programming techniques and careful optimization, MESVS achieves the real-time performance of 20 frames per second. This work discusses the various challenges involved regarding design and implementation of this system and the measures taken to tackle them
Design and implementation of a real-time miniaturized embedded stereo-vision system
The main motivation of the thesis is to develop a fully integrated, modular, small baseline (\u3c=3cm), low cost (\u3c=CAD$600), real-time miniaturized embedded stereo-vision system which fits within 5x5cm and consumes very low power ([email protected]). The system consists of two small profile cameras and a dualcore embedded media processor, running at 600MHz per core. The stereo-matching engine performs sub-sampling, rectification, pre-processing using census transform, correlation-based Sum of Hamming Distance matching using three levels of recursion, LRC check and post-processing. The novel post processing algorithm removes outliers due to low-texture regions and depth-discontinuities. A quantitative performance of the post processing algorithm is presented which shows that for all regions, it has an average percentage improvement of 13.61% (based on 2006 Middlebury dataset). To further enhance the performance of the system, optimization steps are employed to achieve a speed of around 10fps for disparity maps in MESVS-I and 20fps in MESVS-II system
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
- …