Similarity-based image organization and browsing using multi-resolution self-organizing map

An algorithm is presented in this paper to facilitate the exploration of large image collections based on visual similarities. Starting with an unordered and unannotated set of images, the algorithm first extracts the salient details into feature vectors using both color and gradient information. The feature vectors are then used to train a self-organizing map which maps high-dimensional feature vectors onto a 2D canvas so that images with similar feature vectors are grouped together. When users browse the image collection, an image collage is generated that selects and displays the most pertinent set of images based on which portion of the 2D canvas is currently in view. Flowing from an overview to details is a seamless operation controlled simply by pan and zoom, with representative images selected in a consistent and predictable way. To make organizing larger image collections practical in interactive time, the organization algorithm is designed to run in parallel on graphics processing units. Overall this paper presents an end-to-end solution that facilitates the surfing of image collections in a fresh way.     

Central catadioptric image processing with geodesic metric

Because of the distortions produced by the insertion of a mirror, catadioptric images cannot be processed similarly to classical perspective images. Now, although the equivalence between such images and spherical images is well known, the use of spherical harmonic analysis often leads to image processing methods which are more difficult to implement. In this paper, we propose to define catadioptric image processing from the geodesic metric on the unitary sphere. We show that this definition allows to adapt very simply classical image processing methods. We focus more particularly on image gradient estimation, interest point detection, and matching. More generally, the proposed approach extends traditional image processing techniques based on Euclidean metric to central catadioptric images. We show in this paper the efficiency of the approach through different experimental results and quantitative evaluations.     

图像处理技术在玻璃缺陷检测中的应用

利用MATLAB图像处理工具箱强大的图像处理功能,对采集到的玻璃表面缺陷图像进行图像预处理,提取出感兴趣的区域,为后面的缺陷识别作前期的处理。利用玻璃缺陷中的划痕图像为例进行一系列的图像处理来实现目标区域的分割并作初步的分析。     

激光条纹图像处理算法的研究

概述了数字图像处理的基本理论知识以及发展趋势,介绍了三维人体激光条纹图像的特点,并重点讲述了所选取的图像处理算法及其实现过程,对非接触式三维人体尺寸的获取有一定的参考意义。     

基于计算机视觉的高精度图像拼接

图像拼接是大幅面图像测量过程中非常重要的一个环节,其拼接精度的高低直接影响测量结果的精度。介绍了视觉检测系统的基本组成和原理,并重点阐述了基于计算机视觉的高精度大幅度图像拼接。     

一种JPEG图像的DCT域隐写分析方法

提出了一种基于JPEG图像DCT系数差分矩阵统计特征的隐写分析方法。该算法保留了以往算法选用的DCT系数水平和竖直方向的差分矩阵相关特征,通过增加其主副对角线方向上差分矩阵来提取和计算特征向量,进而利用SVM分类器进行分类。实验结果表明,该方法能够有效地对JPEG图像进行检测,并且具有较高的检测正确率。     

基于改进伪中值滤波器的道路图像滤波算法*

针对已有的细胞神经网中值滤波器滤波时,收敛速度慢、稳定性不好以及滤波图像比较模糊的缺点,设计一种差值控制细胞神经网的改进伪中值滤波器。提出了改变取值空间、引入随机扰动、扩大中值滤波窗口尺度和引入Mask掩图的改进方法。实验结果表明：该算法具有去除各种强度脉冲随机噪声能力,又能保护图像细节信息,而且具有良好的实时性。     

基于SIFT特征点匹配的水印图像几何校正算法*

为了有效实现水印图像的水印检测与水印嵌入之间的同步,提出一种应用SIFT(scale invariant feature transform)特征点匹配估计水印图像几何变换参数的校正算法.用两个稳定而又相距最远的图像SIFT特征点的变化估计图像所经历的旋转和缩放参数,校正之后,再用一个稳定的SIFT特征点的变化校正图像...     

面向新型业务的多维快速包分类算法研究*

网络中新兴的新型业务例如P2P,VoIP,Worm等日益增多,在整个网络流量中占有越来越多的比重。对这些新型业务的监测和控制要求路由器等核心设备必须有能力对数据包进行快速和准确的分类。本文从这些新型业务包分类规则库的特点着手,提出在多维多模式匹配情况下的三种包分类决策树,通过实验说明这些决策树应用在新型业务的包分类上与传统的串行决策树性能各自的差异,进而得出适合新型业务包分类的最佳算法。     

t分布下基于核函数的最大后验概率分类方法

针对多元正态分布不能适应样本数据严重拖尾现象的问题,提出t分布下的多分类识别方法。利用核技术将样本数据扩展到高维特征空间中,采用贝叶斯分类器得到最大后验概率,进而得到分类结果。由于可以调整t分布中的自由度参数v,因此更容易满足数据样本的不同拖尾情况,具有较好的稳健性。在5个国际标准UCI数据集和3个人脸数据集上进行了大量实验,实验结果表明,该方法有较好的分类效果,具有可行性。