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On the Research of Automatic Counting Score Method and its Application in Shooting Training Based on CIELAB Color Model and Radial Basis Function

机译:基于CIELAB颜色模型和径向基函数的自动计数法及其在拍摄训练中的应用研究

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Aiming at the key technology of automatic target-reporting system, which are paper image analysis and bullet hole recognition, a fast hole-location and score-counting method is proposed in this paper. It is consisted of two key algorithms: the two-step fast & fine segmentation algorithm based on CIELAB color model and the self-adapt scoring algorithm based on radial basis function. Firstly, the fast Gauss filter is used to smooth the target image and avoid the influence of white loop on threshold settling. Secondly, based on the CIELAB color model, the A-channel layer of the green target paper is extracted for threshold segmentation. When the complete and effective target area is separated out, the 10-ring region and bullet hole location are identified through the calculation of image region roundness. Finally, by establishing the two-dimensional Euclidean space radial basis function, a self-adaptive fast scoring algorithm is realized which does not depend on the size of target paper and improves the generality of automatic target reporting system. In the last part of the paper, a series of experiments were carried out. All the results show that the proposed method is effective and reliable, with good robustness and adaptability. It is suggested that the application of our method will benefit on the improvement of target-reporting system in shooting training.
机译:针对自动目标报告系统的关键技术,这是纸张图像分析和弹孔识别,本文提出了一种快速孔位置和分数计数方法。它由两个关键算法组成:基于Cielab颜色模型的两步快速和精细分割算法和基于径向基函数的自适应评分算法。首先,快速高斯滤镜用于平滑目标图像并避免白环对阈值沉降的影响。其次,基于CIELAB颜色模型,提取绿色目标纸的A沟道层进行阈值分割。当分开完整且有效的目标区域时,通过计算图像区域圆度来识别10环区域和子弹孔位置。最后,通过建立二维欧几里德空间径向基函数,实现了自适应快速评分算法,其不依赖于目标纸张的大小并提高自动目标报告系统的一般性。在本文的最后一部分中,进行了一系列实验。所有结果表明,该方法有效可靠,具有良好的鲁棒性和适应性。建议我们的方法应用将有利于改善射击培训中的目标报告系统。

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