ADAPTIVE IMAGE ENHANCEMENT MODEL FOR THE ROBOT VISION SYSTEM

K. Smelyakov, A. Chupryna, Denys Sandrkin, Loreta Savulioniene, Paulius Sakalys
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Abstract

Robotics is one of the important trends in the current development of science and technology. Most modern robots and drones have their own vision system, including a video camera, which they use to take digital photos and video streams. These data are used to analyze the situation in the robot's camera field of view, as well as to determine a real-time robot's behavior algorithm. In this regard, the novelty of the paper is special polynomial mathematical model and method for adaptive gradational correction of a digital image. The proposed model and method make it possible to independently adjust to brightness scales and image formats and optimally perform gradational image correction in various lighting conditions. Thus, ensuring the efficiency of the entire subsequent cycle of image analysis in the robot's vision system. In addition, the paper presents the results of numerous experiments of such gradational correction for images of various classes, as well as conditions of reduced and increased levels of illumination of the field of view objects. Conclusions and recommendations are given regarding the practical application of the proposed model and method.
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机器人视觉系统的自适应图像增强模型
机器人技术是当前科学技术发展的重要趋势之一。大多数现代机器人和无人机都有自己的视觉系统,包括一个摄像头,用来拍摄数码照片和视频流。这些数据用于分析机器人相机视野中的情况,以及确定机器人的实时行为算法。在这方面,本文的新颖之处在于对数字图像进行自适应梯度校正的特殊多项式数学模型和方法。所提出的模型和方法可以独立调整亮度尺度和图像格式,并在各种照明条件下最佳地进行渐变图像校正。从而保证了机器人视觉系统后续整个图像分析周期的效率。此外,本文还介绍了对不同类别图像进行这种梯度校正的大量实验结果,以及视场对象光照水平降低和增加的情况。最后对模型和方法的实际应用提出了结论和建议。
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