基于大数据技术的非线性计算机图像场景与目标信息提取

IF 2.4 Q2 ENGINEERING, MECHANICAL
Jiaqi Wang
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引用次数: 0

摘要

摘要为了探索计算机图像场景和目标信息的提取,提出了一种基于大数据技术的非线性提取方法。该方法在对SAR计算机图像进行目标提取和计算机图像压缩等处理时,可将计算机图像分解为多个分量,分别表示捕获的不同图像特征。根据不同部件的特点选择最合适的加工方法,可以大大提高性能。采用非线性扩散方法,将计算机图像分解为代表大尺度结构信息的结构分量和代表小尺度细节信息的纹理分量,研究了扩散过程中的自动阈值估计。将LAIDA准则引入到基于非线性扩散的计算机图像分解的自动阈值解中,对各种扩散参数形式的扩散过程进行测试和评价。结果表明,基于自动阈值估计的扩散分解实验结果在各指标上都非常接近,这表明使用自动阈值估计,无论使用何种扩散指标,都可以得到非常接近的结果。具体来说,对于每一种算法,异常值的参数估计阈值l起着明显的作用。第三是评估过程的主动性程度。L越大,离群值越大,将导致扩散过程的程度越大,导致结构相似指数和成分相关性不断下降。实践证明,该算法具有较强的全局搜索能力,能有效避免过早收敛,收敛速度快,长时间稳定性好。它可广泛用于各种多模态函数的优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonlinear computer image scene and target information extraction based on big data technology
Abstract To explore the extraction of computer image scene and target information, a nonlinear method based on big data technology is proposed. The method can decompose the computer image into a plurality of components when the SAR computer image is processed such as target extraction and computer image compression, which represent different captured image features, respectively. Selecting the most suitable processing method according to the characteristics of different components can greatly improve the performance. Using nonlinear diffusion method, the computer image is decomposed into structural components representing large-scale structural information and texture components representing small-scale detailed information, and the automatic threshold estimation in the diffusion process is studied. The LAIDA criterion is introduced into the automatic threshold solution of nonlinear diffusion-based computer image decomposition to test and evaluate the diffusion process of various diffusion parameter forms. The results show that the experimental outcome of the diffusion decomposition based on automatic threshold estimation is very close on each index, which shows that using automatic threshold estimation, no matter what diffusion index is used, very close results can be obtained. Specifically, for each algorithm, the parameter estimation threshold l for outliers plays an obvious role. The third is the degree of initiative of the estimation process. The larger the L, the larger the outlier, which will lead to a greater extent of the diffusion process, resulting in a continuous decrease in the structural similarity index and compositional correlation. It is proved that the algorithm has strong global search ability, can effectively avoid premature convergence, has fast convergence speed, and good long stability. It can be widely used for optimization of various multimodal functions.
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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