共查询到20条相似文献，搜索用时 218 毫秒
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《黑龙江科技信息》2016,(24)
根据图像内容的最小化嵌入失真原则,在空域通用小波相对失真方法(Spatial-UNIversal WAvelet Relative Distortion,S-UNIWARD)的基础上,提出一种结合小波边缘检测和校验格编码(Syndrome Trellis Code,STC)的图像自适应空域隐写术。首先利用小波变换检测图像边缘区域,然后根据S-UNIWARD定义图像像素的嵌入失真,并通过设置失真阈值选择图像的纹理区域,最后使用STC在边缘区域和纹理区域对秘密信息进行嵌入。实验表明本算法提高了S-UNIWARD在图像边缘区域和纹理区域的嵌入精度,且能提高算法安全性。 相似文献
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提出了基于特征的三维地形匹配算法,通过提取山地的山谷线作为地形特征,对基准图和实时图中的特征进行匹配来确定飞行器所在的地理位置。匹配过程阐述了树描述符算法,拓扑粗匹配以及几何量精匹配等概念,该算法具有较大的拉入范围,在一定程度上可以克服因实时图和基准图的差异而产生的误匹配。 相似文献
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在纹理合成领域,基于样图的曲面纹理合成是现在研究的热点,如何在多边形表面生成无缝无变形的纹理成为一个焦点。Wei和Levoy提出的纹理合成算法是当前较流行的算法之一,但该方法存在算法实现复杂、使某些纹理的合成质量降低等缺点。基于Wei-Levoy方法,进行了相应改进,对给定的二维图像能够合成无缝的最小变形的任意多边形表面的纹理。 相似文献
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《科技通报》2015,(10)
对私有云计算平台上资源最优路径匹配,实现对资源的有效调度和检测,提高资源共享能力。传统算法采用云平台的资源置换算法实现云资源目标匹配,受限于机器学习技术自身的复杂性,性能不好。提出一种基于委托管理节点角色量化合成的私有云计算平台上资源最优搜索路径匹配算法。构建私有云计算平台终端数据访问和资源调度模型,根据贪心算法的收缩原则,在资源访问管理中引入互斥锁机制以保证资源共享操作的完整性,路径匹配有向图模型,提高资源路径匹配搜索能力。以全局度量为中心节点,计算逆向追踪搜索频度,实现算法优化。仿真实验结果表明,采用该算法进行私有云计算平台上资源最优路径匹配优化,能有效提高私有云平台上的资源路径匹配准确度,从而提高资源搜索成功率,实现资源优化共享,展示优越的适用价值。 相似文献
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随着液晶显示技术的快速发展,3D液晶显示逐渐受到市场和消费者的追捧。在3D液晶电视中,有一类快门式3D显示技术。这种技术通过一定间隔的两台摄像机同时拍摄同一个物体的左右眼两幅画面,然后合成处理后,在显示屏上通过分时显示的方式显示出来,然后搭配与分时频率相同的快门式眼镜,使人的左右眼分别观看到摄像机拍摄到的同一物体画面。由于两幅画面存在视差的不同,人眼即可感受到了立体的影像,实现3D效果显示。为了实现更好的3D显示效果,一般通过扫描背光的方式来对背光系统进行控制。 相似文献
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立体匹配是计算机立体视觉中最重要的步骤之一,由于计算量巨大,使得在各种实时应用中,都必须解决立体匹配的优化问题。立体匹配的实时性研究已经成为现代立体视觉的一个重要研究方向。本文从硬件和软件两个层次分析了立体匹配算法的基本加速方法和技巧,然后详细介绍了利用TiC64xDSP进行Census算法的优化。最后通过实验结果验证了本文提出算法的有效性。 相似文献
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局部匹配方法主要是通过搜索最优点对应或特征对应来判断形状是否匹配.转向角函数也是一种基于局部特征的形状匹配算法,该算法将平面图形用直角坐标系图形表示,更形象地体现各图形间的差异性,并将这些差异用汇编语言转为计算机可以识别的代码进行实验.该算法能有效地处理图形变形和遮掩的问题,与其他的局部匹配算法相比有较高精度、运算速度快等优点,用Matlab实验证明是一种比较优秀的形状匹配算法. 相似文献
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本文研究一种改进的近邻搜索算法的图像匹配技术。本文采用基于特征的图像匹配方法,利用SIFT算法提取特征点。在特征点匹配的过程中,为提高搜索样本特征点的最近邻和次近邻特征点的速度,本文采用一种基于二叉检索树算法改进的近邻搜索算法,该算法用最近邻与次近邻比值来进行特征点的匹配。用MATLAB语言实现该算法并运用到图像特征匹配中,实验证明优于原算法并具有较高实时性。 相似文献
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《Information processing & management》2023,60(3):103322
Using AI technology to automatically match Q&A pairs on online health platforms (OHP) can improve the efficiency of doctor-patient interaction. However, previous methods often neglected to fully exploit rich information contained in OHP, especially the medical expertise that could be leveraged through medical text modeling. Therefore, this paper proposes a model named MKGA-DM-NN, which first uses the named entities of the medical knowledge graph (KG) to identify the intention of the problem, and then uses graph embedding technology to learn the representation of entities and entity relationships in the KG. The proposed model also employs the relationship between entities in KG to optimize the hybrid attention mechanism. In addition, doctors' historical Q&A records on OHP are used to learn modeling doctors’ expertise to improve the accuracy of Q&A matching. This method is helpful to bridge the semantic gap of text and improve the accuracy and interpretability of medical Q&A matching. Through experiments on a real dataset from a Chinese well-known OHP, our model has been verified to be superior to the baseline models. The accuracy of our model is 4.4% higher than the best baseline model. The cost-sensitive error of our model is 13.53% lower than that of the best baseline model. The ablation experiment shows that the accuracy rate can be significantly improved by 8.72% by adding the doctor modeling module, and the cost-sensitive error can be significantly reduced by 17.27% by adding the medical KG module. 相似文献
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《Information processing & management》2001,37(1):119-145
This study attempted to use semantic relations expressed in text, in particular cause-effect relations, to improve information retrieval effectiveness. The study investigated whether the information obtained by matching cause-effect relations expressed in documents with the cause-effect relations expressed in users’ queries can be used to improve document retrieval results, in comparison to using just keyword matching without considering relations.An automatic method for identifying and extracting cause-effect information in Wall Street Journal text was developed. Causal relation matching was found to yield a small but significant improvement in retrieval results when the weights used for combining the scores from different types of matching were customized for each query. Causal relation matching did not perform better than word proximity matching (i.e. matching pairs of causally related words in the query with pairs of words that co-occur within document sentences), but the best results were obtained when causal relation matching was combined with word proximity matching. The best kind of causal relation matching was found to be one in which one member of the causal relation (either the cause or the effect) was represented as a wildcard that could match with any word. 相似文献
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