基于帧间差值的视频镜头渐变检测算法

针对在视频同一镜头中运动因素引起的帧间差值波动与镜头渐变过程的帧间差值波动情况相似可能对镜头渐变产生误检的问题,利用镜头渐变是从一个镜头转换到一个新的镜头的本质特征,通过分析帧间差值曲线,提出了省略帧间差值波动,直接计算帧间差值波动前后两帧差值的视频镜头渐变检测算法.利用该算法对若干视频进行实验,并与双阈值累计帧差算法进行比较,实验结果表明了该算法在视频镜头渐变检测中的有效性.     

基于窗口最大值和自适应阈值的视频镜头分割算法

本文对典型视频镜头分割算法进行分析,提出基于窗口最大值和自适应阈值的镜头分割算法.对该算法的系统结 构、特征参数、检测依据、自适应阈值及算法的实现进行了分析,用十段视频进行实验,结果表明,该算法的分割效果比较明显.     

利用基于色彩直方图的Fuzzy K-Means算法进行视频镜头分割

分析了现有的基于帧间特征差与阈值进行比较的传统视频镜头分割方法在阈值确定上的困难,以及由此对实验结果带来的不准确性,提出了将聚类算法应用于视频镜头分割,并提出了用FuzzyK-Means的聚类算法进行视频镜头分割.在视频特征上,选取的是传统的色彩直方图.实验结果显示这种基于色彩直方图的Fuzzy K-Means算法对于视频镜头的分割具有较好效果.     

压缩视频域镜头边界检测研究现状与发展前景

镜头边界检测是视频检索的首要问题,镜头转换分为突变和渐变,镜头边界的检测结果直接影响视频检索的准确度,针对这个问题,提出了在压缩域视频中进行镜头边界检测常用的两类方法:一类是基于I帧DC系数的方法;另一类是基于聚类的方法.前者先利用I帧的DC图进行镜头的粗略分割,再分别运用基色调、宏块信息和运动矢量进行精确分割;后者聚类法克服了帧的无序性.实验结果表明,第一类压缩域镜头边界检测的方法之于镜头的渐变检测效果普遍不理想,但是计算较第二类算法简便,第二类方法对渐变镜头的检测效果好于第一类,有效克服无序性是一种改进.     

基于聚类的镜头边界检测算法

镜头边界检测是基于内容视频检索的重要组成部分。为从不同类型的视频中有效地检测出视频镜头边界,提出一种视频镜头边界检测算法。通过视频帧图像的颜色特征,得到视频的相似性矩阵,根据突变镜头和渐变镜头在Affinity Propagation聚类结果中的不同特点,运用双阈值法检测镜头边界。实验结果表明,该算法从视频的本身信息分布出发,能自动快速地检测出镜头边界。     

基于聚类的镜头边界检测算法

镜头边界检测是基于内容视频检索的重要组成部分。为从不同类型的视频中有效地检测出视频镜头边界,提出一种视频镜头边界检测算法。通过视频帧图像的颜色特征,得到视频的相似性矩阵,根据突变镜头和渐变镜头在Affinity Propagation聚类结果中的不同特点,运用双阈值法检测镜头边界。实验结果表明,该算法从视频的本身信息分布出发,能自动快速地检测出镜头边界。     

改进的累积帧实现镜头转换检测技术

镜头检测方法是视频内容分析和视频检索中的关键技术之一。对累积帧的检测镜头转换的方法提出了改进,包括：采用图像块颜色均值差作为前景后景判决参数,更好地避免了噪声影响;提出局部全搜索块匹配算法准确区分物体运动与渐变镜头。对镜头转换判决阈值的设定进行了修正。通过实验对算法检测效果进行分析比较,结果表明,改进后的算法能很好地解决累积帧方法对快速运动场景容易出现误判的问题,具有更好的镜头检测效果。     

基于决策树的MPEG视频镜头分割算法

压缩视频镜头的分割是视频内容分析中的一个难点,由于镜头在组织和索引视频中起关键性的作用,提出了一种基于决策树的MPEG视频镜头分割算法。该算法采用决策树这种机器学习方法对样本视频进行训练,通过融合运动信息、颜色、边缘等特征获得镜头分割的最佳阈值,较好地解决了压缩视频处理中检测镜头突变和渐变难题,同时还能够检测出镜头是否产生闪光现象和相机运动的产生。实验证明本算法在压缩视频镜头检测方面取得了较好的检测结果。     

基于互信息量的镜头边界检测算法

为快速稳健地检测镜头边界,提出基于互信息量的镜头边界检测算法。采用基于HSV空间不均匀分块直方图求取的帧间互信息量作为视频帧间差异度量,结合相应的阈值策略与时域窗策略,实现对镜头的切变,以及常见渐变与计算机特效技术制作的镜头变换检测。对包括广告、综艺节目、新闻等多类视频数据进行实验,结果证明,该算法对切变检测的综合效果比经典基于直方图的双阈值法平均提高11.9%,渐变检测的综合效果平均提高7.6%,能有效减弱摄像机运动与光照变化对镜头检测的影响,对不同类型视频镜头检测效果稳定,具有较高的查全率与查准率。     

一种基于模型的扫换检测方法

视频自动分割是实现视频数据库检索必不可少的一个过程,其基础是镜头边界检测.当前已有的算法能够较准确地检测出镜头突变,但对于镜头的渐变则常常会漏检,这是由于镜头渐变时帧间差没有一个明显的峰值,因而其检测比突变检测要困难得多.扫换是一种常用的视频空间编辑手段,用于实现多种镜头变化.通过分析各种类型的扫换,提出了一种新的基于视频空间编辑模型的扫换检测算法,其性能优于Alattar提出的基于统计特征的算法.对用AdobePremiere5.1生成的各种扫换视频进行检测.实验结果表明,这种扫换检测算法能够较好地适应     

基于MPEG-2压缩域的非线性编辑特技算法

在数字电视节目的制作过程中，非线性编辑中视频特技合成是一项很重要的工作。目前，这些特技大都是在宅间域中实现的，但是由于数字视频的压缩编码大都采用MPEG-2标准，因此直接在MPEG-2压缩域实现视频特技的合成，可以减少很多中间步骤，从阿加快电视台基于MPEG-2的数字电视节目的制作播出一体化进程。该文提出了一种在MPEG-2压缩域中不完全解压缩蛮现镜像特技的算法。实验结果表明，采用这种算法后，计算量比传统的空间域实现特技有了很大的减少，而视频图像的质量损失很小。     

Using a preamble to increase presence in digital virtual environments

Immersion in a digital virtual environment (DVE) increases the likelihood that individuals will feel present in the DVE and hence respond as they would in a similar physically grounded environment. Previous research utilizing high-fidelity technology has demonstrated that by starting a virtual experience in a virtual replica of the immediate physical environment, presence is increased. The purpose of this study was to determine whether utilizing such a transitional environment to increase presence could be replicated on a significantly less immersive system—a 2D desktop monitor with mouse and keyboard for navigation. Participants began their DVE experience either in a “preamble” DVE made to look like the surrounding physical laboratory space, or in a novel DVE (i.e., a house). Then, they were given verbal instructions to leave their respective environments and told to go up a set of stairs to explore a museum. Afterward, they reported levels of immersion and presence in the latter DVE. Results demonstrated that entering a target DVE via a familiar “preamble” environment increased perceptions of reality judgment of the virtual experience, perceptions of possibility to act, and levels of presence. These results suggest that incorporating a familiar digital preamble environment as a prelude to the target DVE enables DVE designers and enthusiasts to increase presence without having to invest in more expensive hardware, but it could also augment existing immersive technology. Their efficacy may be because they offer a gradual transition into the virtual world, such that the familiarity eases users into the novel experience.     

An efficient partitioning algorithm for distributed virtualenvironment systems

Distributed virtual environment (DVE) systems model and simulate the activities of thousands of entities interacting in a virtual world over a wide area network. Possible applications for DVE systems are multiplayer video games, military and industrial trainings, and collaborative engineering. In general, a DVE system is composed of many servers and each server is responsible to manage multiple clients who want to participate in the virtual world. Each server receives updates from different clients (such as the current position and orientation of each client) and then delivers this information to other clients in the virtual world. The server also needs to perform other tasks, such as object collision detection and synchronization control. A large scale DVE system needs to support many clients and this imposes a heavy requirement on networking resources and computational resources. Therefore, how to meet the growing requirement of bandwidth and computational resources is one of the major challenges in designing a scalable and cost-effective DVE system. In this paper, we propose an efficient partitioning algorithm that addresses the scalability issue of designing a large scale DVE system. The main idea is to dynamically divide the virtual world into different partitions and then efficiently assign these partitions to different servers. This way, each server will process approximately the same amount of workload. Another objective of the partitioning algorithm is to reduce the server-to-server communication overhead. The theoretical foundation of our dynamic partitioning algorithm is based on the linear optimization principle. We also illustrate how one can parallelize the proposed partitioning algorithm so that it can efficiently partition a very large scale DVE system. Lastly, experiments are carried out to illustrate the effectiveness of the proposed partitioning algorithm under various settings of the virtual world     

Novel automatic video cut detection technique using Gabor filtering

Video shot transition identification constitutes an important computer vision research field, being applied, as an essential step, in many other digital video analysis domains: video scene detection, video compression, video indexing, video content retrieval and video object tracking. This paper approaches the video cut transition detection domain, providing a novel feature-based automatic identification method. We propose a feature extraction technique that uses 2D Gabor filtering, computing tridimensional image feature vectors for the video frames. Most shot cut detection techniques use a thresholding operation to discriminate between the inter-frame difference metric values and thus identify the video break points. Our identification approach is not threshold-based, using an automatic unsupervised distance classification procedure instead of a threshold. Thus, we provide a region-growing based classification approach, that proves to be very efficient in clustering the distances between feature vectors of consecutive frames. The two resulted distance classes determine a satisfactory video shot detection.     

Automatic logo transition detection in digital video contents

The amount of user created contents has been increasing rapidly and is associated with a serious copyright problem. Automatic logo detection and recognition in videos is a natural and efficient way of overcoming the copyright problem. However, logos have varying characteristics, which make logo detection and recognition very difficult. Moreover, logo transitions between two different logos exist in one video comprising several video contents. This disrupts the automatic logo detection and recognition. Therefore, in order to improve logo detection, it is necessary to take into account the logo transitions explicitly. This paper proposes an accurate logo transition detection method for recognizing logos in digital video contents. The proposed method accurately segments a video according to logo and efficiently recognizes various types of logos. The experimental results demonstrate the effectiveness of the proposed method for logo detection and video segmentation according to logo.     

Real time repeated video sequence identification

In this paper, we present a real time system for detecting repeated video clips from a live video source such as news broadcasts. Our system utilizes customized temporal video segmentation techniques to automatically partition the digital video signal into semantically sensible shots and scenes. As each frame of the video source is processed, we extract auxiliary information to facilitate repeated sequence detection. When the video transition marking the end of the shot/scene is detected, we are able to rapidly locate all previous occurrences of the video clip. Our objective is to use repeated sequence information in our multimedia content analysis application to deduce semantic relationships among shots/scenes in the input video. Our real time video processing techniques are independent of source and domain and can be applied to other applications such as commercial detection and improved video compression.     

Production model based digital video segmentation

Effective and efficient tools for segmenting and content-based indexing of digital video are essential to allow easy access to video-based information. Most existing segmentation techniques do not use explicit models of video. The approach proposed here is inspired and influenced by well established video production processes. Computational models of these processes are developed. The video models are used to classify the transition effects used in video and to design automatic edit effect detection algorithms. Video segmentation has been formulated as a production model based classification problem. The video models are also used to define segmentation error measures. Experimental results from applying the proposed technique to commercial cable television programming are presented.     

Sudden scene change detection in compressed video using interpolated macroblocks in B-frames

This paper addresses an important area in video processing, namely compressed domain processing. For video indexing, video scene transition detection is an essential step to segment the video. Current techniques for scene change detection tend to suffer from a major limitation as most of them cannot identify scene transitions in the compressed domain. Since most video is expected to be stored in the compressed domain, scene transition detection in this domain is highly desirable. In this paper an algorithm for video scene change detection is proposed to overcome this limitation. In this scheme, properties of the B-frames are used as it is capable of measuring the correlation between two adjacent reference frames. The results show that this scheme performs better than schemes based on P-frames. Proposed scheme can be directly applied with compressed data with minimum decompression and hence it is computationally efficient and makes real time implementations possible. Results show that video scene transitions can be identified satisfactorily with the proposed scheme.     

基于OpenCV的数字视频缺陷检测快速算法

随着视频应用范围的扩展,在视频中定位出视频缺陷变得越来越重要。对于简单的视频缺陷,比如黑场,红、绿、蓝场以及静帧的缺陷,提出了相应的检测算法,用到的特征包括绝对差等这类简单的特征。对于复杂的视频缺陷,如马赛克缺陷,在分析现有算法的基础上,提出了一个快速有效的检测算法,采用了两种策略以提高检测速度：新的模板匹配及对边缘图像的预处理。所有以上检测算法都基于OpenCV库开发实现,使得整个算法的实现更快捷。进行了仿真实验,结果验证了所提算法的有效性。     

基于镜头关键帧集的视频场景聚类的研究

在数字视频的分析、浏览、检索中,镜头已难以满足现有的需要。场景是一组包含有内容相关的若干镜头的集合,在一定程度上满足了数字视频的分析、浏览、检索的需要。文章首先使用了X2直方图匹配的计算方法,结合直方图的两次判断法,进行突变和渐变镜头边界的检测;然后对镜头内非相邻帧间距离经过阈值判断提取关键帧集;文章提出了基于镜头关键帧计算两个关键帧集之间距离的最小值作为所计算镜头之间的距离的算法;最后运用镜头之间的距离进行镜头的聚类产生场景,给出了典型的实验结果,表明该算法对视频场景的聚类有较好的性能。