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Reinforcement Learning in Computer Vision

机译：计算机视觉中的强化学习

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Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.
机译：如今，机器学习已成为解决各种计算机视觉任务（例如特征检测，图像分割，对象识别和跟踪）的基本技术之一。在许多应用中，各种复杂的系统（例如机器人）都配备了视觉传感器，通过解决相应的计算机视觉任务，视觉传感器可以从中了解周围环境的状态。这些任务的解决方案用于做出有关将来可能采取的行动的决策。不足为奇的是，在解决计算机视觉任务时，我们应考虑其在基于模型的预测控制中的后续应用的特殊方面。强化学习是现代机器学习技术之一，其中通过与环境的交互来进行学习。近年来，强化学习已用于解决诸如视觉信息的处理和分析之类的应用任务，并且用于解决特定的计算机视觉问题，例如过滤，提取图像特征，在场景中定位对象等。本文简要介绍了强化学习技术及其在解决计算机视觉问题中的用途。

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