Real Time Web-based Toolbox for Computer Vision

Sidi Ahmed Mahmoudi, Mohammed Amin Belarbi, Mohammed El Adoui, Mohammed Amine Larhmam, Fabian Lecron



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.

Full Text:



Agrawal, H. M. (2015). Cloudcv: Large-scale distributed computer vision as a cloud service. In Mobile cloud visual media computing, pp. 265-290.

Alshammari, R. &.-H. (2007). A flow based approach for SSH traffic detection. Systems, Man and Cybernetics. ISIC. IEEE International Conference, 296-301.

BENMOUSSA, K. L. (2017). SIMACoop: a Framework Application for the Governance of University Cooperation. Transactions on Machine Learning and Artificial Intelligence, vol. 5, no 4.

Bhimani, A. (1996). Securing the commercial Internet. Communications of the ACM, 29-35.

Calasanz, R. B.-I. (2017). Towards the Scientific Cloud Workflow Architecture. 5th International Workshop on ADVANCEs in ICT Infraestructures and Services (ADVANCE'2017. Doube M, K. M.-C. (2010). BoneJ: free and extensible bone image analysis in ImageJ. Bone 47:1076-9.

Callegati, F. C. (2009). Man-in-the-Middle Attack to the HTTPS Protocol. IEEE Security & Privacy, 78-81.

Gupta, U. (2015). Comparison between security majors in virtual machine and linux containers. Comput. Res. Repos., 4.

Kalpana, P. &. (2012). Data security in cloud computing using RSA algorithm. IJRCCT, 143-146.

Larhmam, M. A. (2014). A portable multi-cpu/multi-gpu based vertebra localization in sagittal MR images. In International Conference Image Analysis and Recognition, Springer, pp. pp. 209-218.

Limare, N. a.-M. (2011). The IPOL initiative: Publishing and testing algorithms on line for reproducible research in image processing. Procedia Computer Science , pp. 4:716-725.

MAASWINKEL, T. (2015). Practical Symfony 2. Apress.

Mahmoudi, S. A. (2014). Taking Advantage of Heterogeneous Platforms in Image and Video Processing. High-Performance Computing on Complex Environments. Wiley.

Mahmoudi, S. A. (2016). Real time GPU-based segmentation and tracking of the left ventricle on 2D echocardiography. 4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016, pp. 602-614.

Mahmoudi, S. A. (2017). Cloud-based platform for computer vision applications. In Proceedings of the 2017 International Conference on Smart Digital Environment, ACM, pp. pp. 195-200.

Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 239.

Netto, M. C. (2017, Oct 24). HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges. arXiv preprint arXiv:1710.08731.

PUPEZESCU, V. e. (2015). Enhanced protection level by database replication in the easy-learning online platform. Advanced Topics in Electrical Engineering (ATEE), 2015 9th In-ternational Symposium on. IEEE, pp. 929-932.

RĂDESCU, R. D. (2010). Security And Confidentiality In The Easy Learning On-line Plat-form. Proceedings of the 5th International Conference on Virtual Learning (ICVL-2010), pp. 449-452.

Samuel, D. (2016). Monitor and Control Manager update. Haute Ecole de Gestion & Tourisme.: Thesis.

Shvachko, K. e. (2010). The hadoop distributed file system. 26th symposium on Mass storage systems and technologies (MSST). IEEE.

STONE, N. L. (2017). Collaborative Data Publication Utilizing the Open Data Reposi-tory's (ODR) Data Publisher. NASA: American Geophysical Union, Fall General Assembly 2016.

TOMLINSON, T. (2015). Anatomy of a Module . Beginning Drupal 8, 141-145.

Xia, L. C.-s. (2010). Design of secure FTP system. International Conference on Communications, Circuits and Systems, 270-273.

Yan, Y. a. (2014). Large-scale image processing research cloud. Cloud Computing, pp. 88-93.

YING, H. C. (2016). EARec: leveraging expertise and authority for pull-request re-viewer recommendation in GitHub. Proceedings of the 3rd International Workshop on CrowdSourcing in Software Engineering. ACM, pp. 29-35.

Zaninotto François, P. F. (2007). The definitive guide to Symfony. Apress.



  • There are currently no refbacks.

Journal of Science and Technology of the Arts
Revista de Ciência e Tecnologia das Artes
ISSN: 1646-9798
e-ISSN: 2183-0088
Portuguese Catholic University | Porto


Esta revista científica é financiada por Fundos Nacionais através da FCT – Fundação para a Ciência e a Tecnologia

 Governo da República Portuguesa