简化的SIFT算法在双目立体视觉中的应用

图像均匀匹配是双目立体视觉领域研究的重点。本文用SIFT特征匹配算法处理立体匹配,并利用构造圆环形窗口以及12维向量表示一个特征点的方法,既保持SIFT算法的尺度不变性,又有效降低了算法的复杂度,提高了算法实时性。     

基于人工神经网络的智能设计系统研究与实现

针对轴对称的拉深零件的设计,引入了特征编码文件概念,将零件特征以特征编码的形式表示出来。为了实现过程状态模型的智能输出,运用人工神经网络实现自动推理的功能;通过智能推理系统,除去了经验化设计中的不断摸索和改进,将零件的特征造型输入之后,系统能够自动确定零件的工艺流程,进而实现自动化生产。系统能够帮助开发和设计人员迅速的产生设计实例,使开发人员能够更专注于产品的创新活动,对于提高企业的产品设计和研发能力有着重要的实际意义。     

Lapped transform‐based codec for frame‐memory reduction in super‐quality LCD overdrive

Abstract— Overdrive is commonly used to reduce the liquid‐crystal response time and motion blur in liquid‐crystal displays (LCDs). However, overdrive requires a large frame memory in order to store the previous frame for reference. In this paper, a lapped transform‐based codec (LTC) is proposed to reduce the frame memory needed for LCD overdrive. In the latest literature, a directional prediction‐based codec (DPC) employs eight directional predictions, which takes up a large percentage of the computational complexity of the codec and does not consider the de‐correlation of the inter‐blocks. Therefore, the LTC first uses the lapped transform to decompose the correlation of the inter‐blocks in the YUV color space. A hadamard transform is then used for energy compaction. The reordered coefficients are pre‐quantized and encoded using the proposed adaptive bit‐plane coding (ABPC) method for a simple hardware implementation. The simulation results show that the proposed LTC significantly improves the DPC in both subjective and objective performance and outperforms the block truncation coding (BTC) and adaptive multi‐level BTC (AM‐BTC), which have been described in the literature.     

Series feature aggregation for content-based image retrieval

Feature aggregation is a critical technique in content-based image retrieval (CBIR) systems that employs multiple visual features to characterize image content. Most previous feature aggregation schemes apply parallel topology, e.g., the linear combination scheme, which suffer from two problems. First, the function of individual visual feature is limited since the ranks of the retrieved images are determined only by the combined similarity. Second, the irrelevant images seriously affect the retrieval performance of feature aggregation scheme since all images in a collection will be ranked. To address these problems, we propose a new feature aggregation scheme, series feature aggregation (SFA). SFA selects relevant images using visual features one by one in series from the images highly ranked by the previous visual feature. The irrelevant images will be effectively filtered out by individual visual features in each stage, and the remaining images are collectively described by all visual features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that the proposed SFA can outperform conventional parallel feature aggregation schemes.     

基于指纹的数字信息加密算法

根据密钥产生和管理过程的复杂性,为增强数字信息安全性,提出一种基于指纹的数字信息加密算法。该算法利用指纹的唯一性和稳定性,将指纹特征矩阵作为密钥。解码时,根据指纹匹配后得到的特征矩阵和密文,对秘密信息进行恢复。实验结果表明,该算法借助指纹识别的安全性增强了密钥安全性,简化了密钥管理,其二值矩阵运算效率很高,便于硬件实现。     

一种高效的自适应彩色图像加密算法

将自适应的灰度图像加密算法引入彩色图像加密中,针对其安全性不理想和效率低等问题,采用耦合映像格子及AMS变换进行改进,设计一种新的自适应彩色图像加密算法。理论分析和仿真实验表明该加密算法操作简单、运算速度快、安全性高,能够在实际的并行计算平台上使用。     

基于特征选择的网页排名算法

为提高网页排名满意度,基于特征选择技术提出一种新的页面排名算法。该算法利用多特征选择技术对页面的特征子集进行筛选,寻找一组具有最大权值与最小相似性的特征集合。在通用信息检索数据集上进行特征词选择测试,结果表明该算法的性能优于传统排名 算法。     

特征维数对隐写检测的影响分析

通过实验验证并分析图像隐写检测过程中特征维数对隐写检测正确率的影响,对比使用人工选取与机器降维的隐写图像识别率。结果表明,低维特征更有利于简化分类器的设计,降低计算复杂度,提高隐写检测正确率,且机器降维后的特征相比人工选取的特征拥有更好的隐写检测效果。     

基于点云几何与形状特征的曲面重构算法

提出一种新的利用点云进行曲面重构的算法,该算法基于点云的几何与形状特征,根据点云的几何分布进行分类和按照点的局部形状特征进行分类,对每类点进行局部网格重构,并进行后续处理以修补拓扑和几何错误。实验结果表明,该算法强健有效,能生成高质量的网格,并能较好地保持模型的几何与形状特征。     

基于柔性匹配的中文文本特征提取方法

针对含有变形关键词的不良信息过滤问题,提出一种基于柔性匹配的中文文本特征信息提取方法。该方法采用柔性匹配技术识别和提取变形关键词,改进向量空间模型中特征项权重的计算方法,对具有变形形式的关键词赋予较高权重,从而提高特征信息的提取效率。实验结果表明,该方法可在保证过滤准确率的前提下,获得较高的召回率。