A Portable Multi-CPU/Multi-GPU Based Vertebra Localization in Sagittal MR Images

Belarbi

Concurrency and Computation

et al. 2017

Self Cite

Summary Nowadays, image and video processing applications have become widely used in many domains related to computer vision. Indeed, they can come from cameras, smartphones, social networks, or from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc) in many situations such as airports, train stations, public areas, sport events, and hospitals. Thus, image and video processing algorithms have got increasing importance, they are required from various computer vision applications such as motion tracking, real time event detection, database (images and videos) indexation, and medical computer‐aided diagnosis methods. The main inconvenient of image and video processing applications is the high intensity of computation and the complex configuration and installation of the related materials and libraries. In this paper, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in a cloud‐based platform, in case of processing one image (or one video in real time) or many images (or videos). This framework enables to affect the local or remote computing units for calculation after analyzing the type of media and the algorithm complexity. The framework disposes of a set of selected CPU and GPU‐based computer vision methods, such as image denoising, histogram computation, features descriptors (SIFT, SURF), points of interest extraction, edges detection, silhouette extraction, and sparse and dense optical flow estimation. These primitive functions are exploited in various applications such as medical image segmentation, videos indexation, real time motion analysis, and left ventricle segmentation and tracking from 2D echocardiography. Experimental results showed a global speedup ranging from 5× to 273×(compared to CPU versions) as result of the application of our framework for the above‐mentioned methods. In addition to these performances, the parallel and heterogeneous implementations offered lower power consumption as result of the fast treatment.

Section: Resultsmentioning

confidence: 99%

“…Our solution for vertebra detection on multi-CPU/multi-GPU platforms is detailed in Mahmoudi et al 3 for X-Ray images, and in Larhmam et al 27 for MR images. Figure 6A presents the results of vertebra detection in X-ray images, while Figure 6B is related to present the detected vertebra in MR images.…”

Section: Multi-cpu/multi-gpu-based Vertebra Segmentationmentioning

confidence: 99%

Towards a smart selection of resources in the cloud for low‐energy multimedia processing

Belarbi

Concurrency and Computation

et al. 2017

Self Cite

“…For more details on the underlying steps of this method refer to (Larhmam, 2014). The three web services communicate with a remote virtual machine, which hosts the container of the core application, as explained in Section 3.1.…”

Section: Spine Segmentation In Mrimentioning

confidence: 99%

Real Time Web-based Toolbox for Computer Vision

et al. 2018

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The last few years have been strongly marked by the presence of multimedia data (images and videos) in our everyday lives. These data are characterized by a fast frequency of creation and sharing since images and videos can come from different devices such as cameras, smartphones or drones. The latter are generally used to illustrate objects in different situations (airports, hospitals, public areas, sport games, etc.). As result, image and video processing algorithms have got increasing importance for several computer vision applications such as motion tracking, event detection and recognition, multimedia indexation and medical computer-aided diagnosis methods. In this paper, we propose a real time cloud-based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can be run in real time and in a secure way. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different algorithms without the need to download, install and configure software or hardware. Moreover, the platform offers the access to the integrated applications from multiple users thanks to the use of Docker (Merkel, 2014) containers and images. Experimentations were conducted within three kinds of algorithms: 1. image processing toolbox. 2. Video processing toolbox. 3. 3D medical methods such as computer-aided diagnosis for scoliosis and osteoporosis. These experimentations demonstrated the interest of our platform for sharing our scientific contributions related to computer vision domain. The scientific researchers could be able to develop and share easily their applications fastly and in a safe way.

Section: Multi-cpu/multi-gpu Based Vertebra Segmentationmentioning

confidence: 99%

Multi-CPU/Multi-GPU Based Framework for Multimedia Processing

IFIP Advances in Information and Communication Technology

Manneback

2015

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Abstract. Image and video processing algorithms present a necessary tool for various domains related to computer vision such as medical applications, pattern recognition and real time video processing methods. The performance of these algorithms have been severely hampered by their high intensive computation since the new video standards, especially those in high definitions require more resources and memory to achieve their computations. In this paper, we propose a new framework for multimedia (single image, multiple images, multiple videos, video in real time) processing that exploits the full computing power of heterogeneous machines. This framework enables to select firstly the computing units (CPU or/and GPU) for processing, and secondly the methods to be applied depending on the type of media to process and the algorithm complexity. The framework exploits efficient scheduling strategies, and allows to reduce significantly data transfer times thanks to an efficient management of GPU memories and to the overlapping of data copies by kernels executions. Otherwise, the framework includes several GPUbased image and video primitive functions, such as silhouette extraction, corners detection, contours extraction, sparse and dense optical flow estimation. These primitives are exploited in different applications such as vertebra segmentation in X-ray and MR images, videos indexation, event detection and localization in multi-user scenarios. Experimental results have been obtained by applying the framework on different computer vision methods showing a global speedup ranging from 5 to 100, by comparison with sequential CPU implementations.