一种基于多边形柱面全景图的虚拟漫游新方法

提出了一种基于图像绘制的多边形柱面全景图的虚拟漫游方法。利用普通的手持相机在一个多边形区域内沿某一路径拍摄并拼接多幅全景图,通过基于SIFT的特征点检测来计算深度,用狭缝图像插值来实现整个区域内的平滑漫游。该方法具有采样简单、虚拟场景真实感强,支持连续大范围漫游的特点。     

基于未校准照片构造全景图的新方法

提出一种基于未校准的系列照片构造全景图纳新方法，该组照片可采用普通三脚架/数码相机在固定地点拍摄。采用普通三角架通过旋转角度拍摄的照片难以保证轴心一致性，可能出现图像扭曲、交叠和倾斜，照片之间可能有一定色差，因此，在图像的拼接和建立全景图方面难度大。文中采用特征匹配技术实现照片的拼接，通过照片的定位拼接、相邻照片颜色亮度的协调实现全景图的构建。该算法所生成的全景图效果好，计算速度快，巳得到实际应用。它可广泛应用于Web环境下的基于图像的虚拟场景构造等方面。     

仿生复眼测量系统中全景图生成的研究

模仿昆虫复眼结构形状,设计了一种仿生复眼测量系统.该系统由七个CCD摄像机按照复眼最小单元排列组成,利用多个DSP处理板对七路视频信号进行同步处理,模拟实现了复眼的功能.与传统的视觉测量系统相比,该系统具有更宽的视场和更高的运动灵敏度.本文利用改进的相关性方法实现视频图像的拼接,生成全景图,并提出了新的方法,较好地消除了因图像局部不齐和图像灰度不一致带来的人为拼接痕迹.     

3D全景图制作系统研究

本文详细讨论了全景图像生成的基本步骤,根据本文选用的柱面模型,推导出柱面投影公式,然后根据双线性插值算法得到柱面全景图。在基于区域块匹配算法的基础上,提出一种基于像素相关性配准的图像拼接方法,采用渐入渐出的融合方法,实现图像的无缝拼接。最后采用柱面反投影实现柱面全景图的浏览。本文采用Visualc＋＋6．0实现界面设计,通过编程实现了以上算法,能够生成效果理想的柱面全景图。     

基于图像的虚拟环境建模技术

综述了基于图像的虚拟现实环境的建模技术，并给出了各种模型表示的理论依据和建模过程的定义。把环境表示分为四类：全景图，光场数据库，深度图像和多投影中心图。全景图的生成包括基于图像相关性和基于图像整合变换的算法。光场数据库的分类主要依据全光函数。同时论述了在各种表示下的采样和建模技术。最后讨论了当前建模技术研究的热点及所需要解决的问题。     

一种改进的全景图生成算法

介绍了全景图的生成过程，对目前常见的几种图像拼接算法进行了分析和比较，在此基础上，提出了一种改进的全景图像生成算法．该算法通过对初始图像进行预处理、改进特征点选取方法以及优化特征点匹配过程，提高了全景图像生成的效率和准确性．实验结果也表明，该算法可广泛应用于基于图像的虚拟场景构造等方面．     

基于模板匹配的柱面全景图拼接优化算法

在保证拼接质量的前提下,为了有效地提高拼接速度,根据全景图拼接的特点,提出一种模板匹配优化算法。该算法逐一分析了模板的尺寸、匹配时的搜索方式和搜索范围对拼接速度的影响,给出一个优化模板尺寸、确定搜索范围及搜索方式的新方法,并给出一个实例,证实了该算法的可靠性。     

基于相似性积累与光束法平差的机载视频图像拼接

研究了无平台信息的视频图像拼接,提出了基于相似性积累与光束法平差的机载视频图像拼接方法.该方法借鉴字符串匹配的基本思想,用基于相似性积累的帧匹配方法解决无平台信息下的帧间优化匹配问题;采用基于光束法平差的空间变换参数估计方法消除序贯拼接所可能产生的累积误差,并消除序贯拼接所可能产生的累积误差,并给出了相应的优化算法.利...     

基于SIFT变换和RBF神经网络的彩色全景图拼接算法

目的 为了复原存在平移、色彩差异、旋转、形变等问题的全景图,提出一种结合SIFT（尺度不变特征变换）和RBF神经网络的彩色全景图拼接算法。方法 通过SIFT算法匹配出两子图中对应的特征点,利用仿射变换解决图像间的旋转和形变问题,采用RBF神经网络纠正子图的色彩差异,最后利用权值矩阵融合技术实现重叠区域的融合。结果 文中算法在拼接效果上优于其他算法,其拼接效果DoEM值为0.902,图像重叠区域过度平滑,有效地避免了融合区域的亮度块或亮度线。结论 该算法效果好,可解决全景图复原过程中多方面的难题。     

增强联系鉴别分析及在人脸识别中的应用

针对人脸识别中的小样本问题,本文提出了一种名为增强联系鉴别分析的方法并应用人脸识别中.该方法利用将人脸局部流形的结构信息和样本的类别信息进行有效地结合进行维数约简,首先构建人脸数据的近邻图与类别联系图,然后通过解决在一定约束条件下的优化问题来获取低维鉴别流形特征,实现在低维空间中同一类人脸数据聚集,不同类别间的人脸数据间尽可能发散,从而可以更好的应用于分类.在AT&T和Yale人脸图像数据库上的实验结果表明该方法能有效的提高人脸识别的性能.     

A 360-Degree Panoramic Image Inpainting Network Using a Cube Map

Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network. Since the cube map format is used, the correlation of the six sides of the cube map should be considered. Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.     

视频监控中基于在线多核学习的目标再现识别

在非重叠多摄像机或单摄像机视频监控中,识别跟踪目标的再次出现很重要.针对传统支持向量机方法在特征融合方面的缺陷,本文提出了一种新的基于在线多核学习的人体目标再现识别方法.该方法对跟踪目标视频前景图像序列提取具有互补性的视觉单词树直方图和全局颜色直方图二种特征,再采用多核学习方法在线训练人体目标视觉外观,从而得到多核特征融合模型.实验结果表明,该方法能快速训练人体目标外观模型,满足视频监控的实时要求,多核融合模型获得了比单一特征模型和单核支持向量机方法更高的识别性能.     

An adaptive compressed MPEG-2 video watermarking scheme

Digital watermarking is becoming more and more important for protecting the authenticity of multimedia objects as they become easier to copy, exchange, and modify. Several watermarking schemes have been proposed in recent years, but most of them deal with still images, only some being extended over to the temporal domain for video watermarking. But again most of those approaches are applied to uncompressed video processing domain. In the subject paper, a new compressed video watermarking procedure is proposed. The developed method embeds several binary images, decomposed from a single watermark image, into different scenes of a video sequence. The spatial spread spectrum watermark is embedded directly into the compressed bit streams by modifying discrete cosine transform (DCT) coefficients. In order to embed the watermark with minimum loss in image fidelity, a visual mask based on local image characteristics is incorporated. Extensive experimental simulations demonstrate that the proposed watermarking scheme is substantially more effective and robust against spatial attacks such as scaling, rotation, frame averaging, and filtering, besides temporal attacks like frame dropping and temporal shifting.     

基于背景图像的视频图像压缩感知重构关键技术研究

目的针对视频序列相邻帧图像之间的相关性,提出了基于帧间差分的背景图像的视频图像重构算法。方法利用前2帧的重构图像求得背景图像,作为下一帧图像重构的先验知识。结果明显提高了重构图像的质量,同时也减少了重构图像所需时间。结论将帧间差分法与压缩感知重构算法相结合得到的视频重构算法,在视频图像重构的质量和速度上都有明显优势。     

Fine-Grained Features for Image Captioning

Image captioning involves two different major modalities (image and sentence) that convert a given image into a language that adheres to visual semantics. Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences. The Convolutional Neural Network (CNN) is often used to extract image features in image captioning, and the use of object detection networks to extract region features has achieved great success. However, the region features retrieved by this method are object-level and do not pay attention to fine-grained details because of the detection model’s limitation. We offer an approach to address this issue that more properly generates captions by fusing fine-grained features and region features. First, we extract fine-grained features using a panoramic segmentation algorithm. Second, we suggest two fusion methods and contrast their fusion outcomes. An X-linear Attention Network (X-LAN) serves as the foundation for both fusion methods. According to experimental findings on the COCO dataset, the two-branch fusion approach is superior. It is important to note that on the COCO Karpathy test split, CIDEr is increased up to 134.3% in comparison to the baseline, highlighting the potency and viability of our method.     

一种基于压缩视频的红外图像预处理算法

文章讨论了在DCT域中进行红外视频图像预处理的可能性,并提出了一种基于DCT域的压缩视频预处理算法.该算法通过DCT域低通滤波、系数变换和对比度拉伸实现了红外图像的增强处理.仿真结果表明,提出的算法克服了对视频压缩、解压再处理的弊端,提高了系统的整体性能和响应速度,改善了视频图像的视觉效果.     

基于L1范数优化路径的视频稳像算法

目的 为解决外包装行业对产品外观进行检测时,采集视频图像存在抖动失真的问题.方法 提出一种基于L1范数优化路径的视频稳像算法,采用SURF算法和FREAK算法对视频序列帧中的特征点进行检测和描述;然后,使用KNN算法和RANSAC算法对相邻帧间的特征点进行匹配和筛选;最后,通过L1范数优化算法对序列帧进行校正和去黑边处理,得到稳像视频.结果 在处理前景无运动和前景有运动的2类视频时,稳像前后视频的平均PSNR值分别提升了5.094 dB和4.273 dB,并且相对于常用的特征匹配算法,文中算法能显著降低相机路径的主动平滑因子.结论 文中算法能够有效地解决视频抖动失真的问题,提高抖动视频的质量,具有一定的鲁棒性.     

Computer-aided inverse panorama on a conical projection surface

The aim of this study is to develop a practical method of direct mapping of an inverse conical panorama with computer aid. The panoramic image is a central projection onto a conical surface from the centres dispersed on a circular or straight path located outside the projection surface. The proposed method is a nonlinear projection in which the resulting image is treated as a sequence of stitched pictures of zero width taken from a moving view point. Geometric data and relations between the projected points are utilised to formulate algorithms for drawing of inverse panoramic images of straight lines, which further enables drawing of wire frame models of represented forms. Images are drawn as plots of a function in the Mathcad software. However, in order to make drawing more efficient and convenient, as well as for the practical applications of them, the algorithms can be implemented in any graphical package.     

结合复小波域和空间总变分的无参考图像清晰度评价

目的针对频域和空域相结合的清晰度评价方法耗时太长、与主观评价一致性不高等问题,提出结合复小波域和空间总变分的感知清晰度评价方法。方法采用低通滤波的方法将原始图像再模糊,得到其参考图像;在复小波域上采用局部相位一致性计算原始图像和参考图像的清晰度图,在空间域上采用总变分计算原始图像和参考图像的清晰度图;分别将原始图像以及参考图像各自的2幅清晰度图进行几何加权平均,得到2幅整体的感知清晰度图;计算2幅感知清晰度图的相似性,得到原始图像的感知清晰度值。结果在LIVE数据库上的SROCC值达到0.9476,CC值达到0.9483,一幅测试图所耗时间为6.003 s。结论实验证明文中算法的评价性能较优,相较同样结合频域和空域的评价方法计算复杂度降低,耗时较少,更加符合人眼视觉主观感知清晰度。