Department of Communications, Politehnica University of Timișoara, 2, Piata Victoriei, 300006 Timișoara, Romania.
Sensors (Basel). 2021 May 26;21(11):3691. doi: 10.3390/s21113691.
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.
计算机视觉是一个跨学科的研究领域，其主要目的是尽可能地接近人类感知来理解周围环境。图像处理系统不断发展和扩展为更复杂的系统，通常针对其可能服务的特定需求或应用进行定制。为了更好地实现这一目标，对这些系统的架构和设计的研究也很重要。我们提出了端到端计算机视觉框架，这是一个开源解决方案，旨在为图像处理领域的研究人员和教师提供支持。该框架集成了计算机视觉功能和机器学习模型，研究人员可以使用这些功能和模型。在不断需要为日常研究活动添加新的计算机视觉算法的情况下，我们提出的框架具有可配置和可扩展的架构优势。即使该框架的主要重点是计算机视觉处理管道，该框架也提供了解决方案来纳入更复杂的活动，例如训练机器学习模型。EECVF 旨在成为计算机视觉领域学习活动的有用工具，因为它允许学习者和教师只处理手头的主题，而无需处理视觉处理流程所需的连接。
