基于云的计算机视觉应用平台

S. Mahmoudi, M. Adoui, Mohammed Amin Belarbi, M. A. Larhmam, Fabian Lecron
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引用次数: 8

摘要

在过去的几年里,图像和视频在许多日常应用中得到了广泛的应用。事实上,它们可以来自相机、智能手机、社交网络或医疗设备。一般来说,这些图像和视频用于说明人或物体(汽车,火车,飞机等)在许多情况下,如机场,火车站,公共区域,体育赛事,医院等。因此,图像和视频处理算法变得越来越重要,在运动跟踪、实时事件检测、数据库(图像和视频)索引以及医疗计算机辅助诊断等各种计算机视觉应用中都需要它们。在本文中,我们提出了一个集成上述方法的云平台,这些方法通常使用流行的开源图像和视频处理库(OpenCV1, OpenGL2, ITK3, VTK4等)进行开发。这些模块在云应用中自动集成和配置。因此,平台用户无需下载、安装和配置相应的软件,就可以使用不同的计算机视觉技术。每个客户机都可以选择所需的应用程序,加载其数据并以安全和简单的方式获得输出结果。云平台可以处理各种操作系统和编程语言(c++、Java、Python等)。在两种应用中进行了实验。第一个代表了医学方法,如磁共振图像的图像分割,二维x线片的三维图像重建,二维超声心动图的左心室分割和跟踪。第二类应用涉及视频处理,如人脸、人车跟踪、人群视频中的异常事件检测等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud-based platform for computer vision applications
During last years, images and videos have become widely used in many daily applications. Indeed, they can come from cameras, smartphones, social networks of 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, hospitals, etc. Thus, image and video processing algorithms have got increasing importance, they are required from various computer visions applications such as motion tracking, real time event detection, database (images and videos) indexation and medical computer aided diagnosis methods. In this paper, we propose a cloud platform that integrates the above-mentioned methods, which are generally developed with popular open source image and video processing libraries (OpenCV1, OpenGL2, ITK3, VTK4, etc.). Theses modules are automatically integrated and configured in the cloud application. Thus, the platform users will have access to different computer vision techniques without the need to download, install and configure the corresponding software. Each guest can select the required application, load its data and get the output results in a safe and simple way. The cloud platform can handle the variety of Operating Systems and programming languages (C++, Java, Python, etc.). Experimentations were conducted within two kinds of applications. The first represents medical methods such as image segmentation in MR images, 3D image reconstruction from 2D radiographs, left ventricle segmentation and tracking from 2D echocardiography. The second kind of applications is related to video processing such as face, people and cars tracking, and abnormal event detection in crowd videos.
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