18,585 research outputs found
Real-time refocusing using an FPGA-based standard plenoptic camera
Plenoptic cameras are receiving increased attention in scientific and commercial applications because they capture the entire structure of light in a scene, enabling optical transforms (such as focusing) to be applied computationally after the fact, rather than once and for all at the time a picture is taken. In many settings, real-time inter active performance is also desired, which in turn requires significant computational power due to the large amount of data required to represent a plenoptic image. Although GPUs have been shown to provide acceptable performance for real-time plenoptic rendering, their cost and power requirements make them prohibitive for embedded uses (such as in-camera). On the other hand, the computation to accomplish plenoptic rendering is well structured, suggesting the use of specialized hardware. Accordingly, this paper presents an array of switch-driven finite impulse response filters, implemented with FPGA to accomplish high-throughput spatial-domain rendering. The proposed architecture provides a power-efficient rendering hardware design suitable for full-video applications as required in broadcasting or cinematography. A benchmark assessment of the proposed hardware implementation shows that real-time performance can readily be achieved, with a one order of magnitude performance improvement over a GPU implementation and three orders ofmagnitude performance improvement over a general-purpose CPU implementation
A middleware for a large array of cameras
Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents
A middleware for a large array of cameras
Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents
Software Defined Media: Virtualization of Audio-Visual Services
Internet-native audio-visual services are witnessing rapid development. Among
these services, object-based audio-visual services are gaining importance. In
2014, we established the Software Defined Media (SDM) consortium to target new
research areas and markets involving object-based digital media and
Internet-by-design audio-visual environments. In this paper, we introduce the
SDM architecture that virtualizes networked audio-visual services along with
the development of smart buildings and smart cities using Internet of Things
(IoT) devices and smart building facilities. Moreover, we design the SDM
architecture as a layered architecture to promote the development of innovative
applications on the basis of rapid advancements in software-defined networking
(SDN). Then, we implement a prototype system based on the architecture, present
the system at an exhibition, and provide it as an SDM API to application
developers at hackathons. Various types of applications are developed using the
API at these events. An evaluation of SDM API access shows that the prototype
SDM platform effectively provides 3D audio reproducibility and interactiveness
for SDM applications.Comment: IEEE International Conference on Communications (ICC2017), Paris,
France, 21-25 May 201
Evaluation of optimisation techniques for multiscopic rendering
A thesis submitted to the University of Bedfordshire in fulfilment of the requirements for the degree of Master of Science by ResearchThis project evaluates different performance optimisation techniques applied to stereoscopic and multiscopic rendering for interactive applications. The artefact
features a robust plug-in package for the Unity game engine. The thesis provides background information for the performance optimisations, outlines all the findings, evaluates the optimisations and provides suggestions for future work.
Scrum development methodology is used to develop the artefact and quantitative research methodology is used to evaluate the findings by measuring performance.
This project concludes that the use of each performance optimisation has specific use case scenarios in which performance benefits. Foveated rendering provides
greatest performance increase for both stereoscopic and multiscopic rendering but is also more computationally intensive as it requires an eye tracking solution.
Dynamic resolution is very beneficial when overall frame rate smoothness is needed and frame drops are present. Depth optimisation is beneficial for vast open environments but can lead to decreased performance if used inappropriately
GPU-based Image Analysis on Mobile Devices
With the rapid advances in mobile technology many mobile devices are capable
of capturing high quality images and video with their embedded camera. This
paper investigates techniques for real-time processing of the resulting images,
particularly on-device utilizing a graphical processing unit. Issues and
limitations of image processing on mobile devices are discussed, and the
performance of graphical processing units on a range of devices measured
through a programmable shader implementation of Canny edge detection.Comment: Proceedings of Image and Vision Computing New Zealand 201
Holographic and 3D teleconferencing and visualization: implications for terabit networked applications
Abstract not available
Neural View-Interpolation for Sparse Light Field Video
We suggest representing light field (LF) videos as "one-off" neural networks (NN), i.e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views. Initially, this sounds like a bad idea for three main reasons: First, a NN LF will likely have less quality than a same-sized pixel basis representation. Second, only few training data, e.g., 9 exemplars per frame are available for sparse LF videos. Third, there is no generalization across LFs, but across view and time instead. Consequently, a network needs to be trained for each LF video. Surprisingly, these problems can turn into substantial advantages: Other than the linear pixel basis, a NN has to come up with a compact, non-linear i.e., more intelligent, explanation of color, conditioned on the sparse view and time coordinates. As observed for many NN however, this representation now is interpolatable: if the image output for sparse view coordinates is plausible, it is for all intermediate, continuous coordinates as well. Our specific network architecture involves a differentiable occlusion-aware warping step, which leads to a compact set of trainable parameters and consequently fast learning and fast execution
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