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End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Orhei, Ciprian ; Vert, Silviu ; Mocofan, Muguras ; Vasiu, Radu

Sensors (Basel, Switzerland), 2021-05, Vol.21 (11), p.3691 [Periódico revisado por pares]

Basel: MDPI AG

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  • Título:
    End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education
  • Autor: Orhei, Ciprian ; Vert, Silviu ; Mocofan, Muguras ; Vasiu, Radu
  • Assuntos: Algorithms ; Architecture ; Artificial intelligence ; benchmarking ; C plus plus ; Complex systems ; Computer Vision ; Computer Vision Framework ; Data mining ; Deep learning ; Image processing ; Java ; Learning activities ; Libraries ; Machine learning ; neural networks ; Open source software ; pipeline architecture ; Programming languages ; Public domain ; Semantics ; Sensors ; Teachers ; User interface
  • É parte de: Sensors (Basel, Switzerland), 2021-05, Vol.21 (11), p.3691
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    This paper is an extended version of our paper published in Orhei, C.; Mocofan, M.; Vert, S. and Vasiu, R. “End-to-End Computer Vision Framework,” 2020 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 2020, pp. 1–4, doi:10.1109/ISETC50328.2020.9301078.
  • Descrição: Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.
  • Editor: Basel: MDPI AG
  • Idioma: Inglês
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