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   图象目标识别 的翻译结果: 查询用时:0.288秒
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图象目标识别
相关语句
  image object recognition
     Adaptive and Progressive Detection of Image Object Recognition Figure
     图象目标识别图形的自适应步级检测
短句来源
     A modified B-P (MB-P) algorithm is proposed and applied to noisy image object recognition.
     将这种算法应用于两类含噪飞机图象目标识别系统,并进行了仿真实验。
短句来源
     The image object recognition model, which describe image object using shap, grey level and motion characters, is built.
     建立图象目标识别模型,用形状、灰度和运动特征描述图象目标。
短句来源
     Based on the modelling ohect, a algorithm of the adaptive and progressive detection of the image object recognition figure in the led tracking system is presented by combining organically the object recognition, the progressive detection of the threshold and the object tigure region, the modification of the false alarm and false dismissal with object spatial condition.
     基于目标建模,把目标识别、门限及目标图形区域步级检测、虚漏警调节和目标空域条件有机地结合起来,给出牵引式跟踪系统中图象目标识别图形的自适应步级检测算法。
短句来源
  “图象目标识别”译为未确定词的双语例句
     GABOR Wavelet Neural Networks Algorithms and an Application Study on Gray Image Target Recognition
     GABOR小波神经网络算法及其在灰度图象目标识别中的应用研究
短句来源
     Intelligent Image Recognition and Tracking
     智能图象目标识别与跟踪
短句来源
     Image Recognition and Classification Based on Multi-feature
     基于多特征的图象目标识别分类
短句来源
     Based on the works mentioned above, this paper constructed. a region-based multiple features image retrieval system. All works have been evaluated on Amsterdam library of object images (ALOI).
     在图象分割与多特征图象目标识别工作的基础上,本文构建了一个基于区域的多特征图象检索实验系统,并在一个最新的图象测试库——Amsterdam目标图象库(ALOI)上对本文的相关工作进行了测试,最终结果证明了本文工作的有效性。
短句来源
     Since in the process of target recognition, the demand of real time work was highlighted while the image data were quite large, we proposed to addincrement learning during rules modeling which speeded up the algorithm and thus increased efficiency.
     针对图象处理目标识别过程中,实时性要求较高,图象数据也较大,我们提出在利用粗集理论建立图象目标识别规则模型的过程中引入增量学习,加速了算法处理过程,提高了运算效率。
短句来源
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  相似匹配句对
     Intelligent Image Recognition and Tracking
     智能图象目标识别与跟踪
短句来源
     A Survey of Researches on SAR ATR
     SAR图象自动目标识别研究
短句来源
     The Recognition of Multiple Targets
     多目标识别
短句来源
     3, recognition of the text information.
     字符目标识别
短句来源
     Image Recognition and Classification Based on Multi-feature
     基于多特征的图象目标识别分类
短句来源
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  image object recognition
Statistical Image Object Recognition using Mixture Densities
      
In this paper, we present a mixture density based approach to invariant image object recognition.
      


In this paper, the back-propagation (B-P) learning algorithm is reviewed at first. Some modifications of the B-P algorithm are discussed. A modified B-P (MB-P) algorithm is proposed and applied to noisy image object recognition. The simulation experiments show that the MB-P algorithm offers a much faster convergence speed, compared with the B-P algorithm.

本文首先简要地介绍了人工神经网络(以下简称神经网络)的B—P学习算法,继而分析了B—P算法收敛速度慢的内在原因,讨论一些加速B—P算法收敛的措施,提出了一种改进的B—P学习算法(MB—P)。将这种算法应用于两类含噪飞机图象目标识别系统,并进行了仿真实验。实验结果表明,MB—P学习算法的收敛速度比B—P算法的收敛速度快许多,而且分别用这两种学习算法训练的神经网络对目标具有大致相同的识别率。

In this paper, the CF(Certain Factor) theory which is one of well--known reasoning modes used in research of expert system for dealing with the uncertain information is introduced into the processes of recognition of imperfect image and a new method is proposed which works on the principle of evidence accumulation. This method emphasizes the effect of fall-aspects information to avoid the error decision which may be caused by uncertainty of individual feature.

本文将专家系统中处理不精确信息时所采用的基于确定性理论的推理模型引入到对不完整图象的目标识别中来,提出了利用证据累积的方法对目标进行识别的观点,强调所有信息的综合作用和正反两方面证据对可信度的影响,减少了由于个别特征的不精确性对分类决策的影响

Multi-feature fusion technique is used to recognize and classify the image target in this paper. We extract the fractal feature and gray entropy from the image,then use Dempster-Shafer's Evidential Reasoning to fuse the information at the report level.Some decision strategies are used to recognize and classify the image. In experiment, we compare the results obtained from Multi-feature fusion with those obtained from single feature. The final results indicate that the Multi-feature fusion method is stable, reliable,...

Multi-feature fusion technique is used to recognize and classify the image target in this paper. We extract the fractal feature and gray entropy from the image,then use Dempster-Shafer's Evidential Reasoning to fuse the information at the report level.Some decision strategies are used to recognize and classify the image. In experiment, we compare the results obtained from Multi-feature fusion with those obtained from single feature. The final results indicate that the Multi-feature fusion method is stable, reliable, and can efficiently improve the accuracy and the ability of fault tolerance.

文中研究将多特征信息融合技术用于图象目标识别分类的方法,利用图象灰度表面的分形特征与图象的摘特征(非分形特征)所提供的信息进行融合处理,在决策层中运用Dempster-Shafer证据推理理论,并使用决策规则对目标进行分类。在实验中,将经过信息融合分类的结果与单特征独自分类的结果进行比较。结果表明,多特征信息融合的目标识别方法具有良好的稳定性,准确性和可靠性,能够有效地提高图象分类识别系统的精确度与容错性。

 
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