从视频中检测人脸

视频中人脸检测的应用领域广泛，近来受到了极大关注。文中提出一种在MPEG流中检测人脸的新方法，它可以从复杂背景中有效地检测方向、大小不同的人脸，还可以处理多个人脸交叠的情况，为适应视频检索的需要，该算法依据帧间冗余性，自适应地调整肤色检测器，利用MPEG流中的运动矢量在一个GOP内跟踪人脸，依据场景的变化更新分割码本等措施，有效地提高了计算速度。用算法测试多个视频序列，实验结果令人满意。     

Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach

We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under various environment settings.     

Modeling and Animating Realistic Faces from Images

We present a new set of techniques for modeling and animating realistic faces from photographs and videos. Given a set of face photographs taken simultaneously, our modeling technique allows the interactive recovery of a textured 3D face model. By repeating this process for several facial expressions, we acquire a set of face models that can be linearly combined to express a wide range of expressions. Given a video sequence, this linear face model can be used to estimate the face position, orientation, and facial expression at each frame. We illustrate these techniques on several datasets and demonstrate robust estimations of detailed face geometry and motion.     

Detecting Irregularities in Images and in Video

We address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images. The term “irregular” depends on the context in which the “regular” or “valid” are defined. Yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context. We pose the problem of determining the validity of visual data as a process of constructing a puzzle: We try to compose a new observed image region or a new video segment (“the query”) using chunks of data (“pieces of puzzle”) extracted from previous visual examples (“the database”). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. We show applications of this approach to identifying saliency in images and video, for detecting suspicious behaviors and for automatic visual inspection for quality assurance. Patent Pending     

视频图像中的车型识别

文章介绍一种在固定单摄像头拍摄交通图像序列中检测车辆的方法。处理过程大致分为以下三步:重建不含运动目标的自然背景及图像分割;摄像机标定;目标区域的跟踪和车型识别。实验证明方法是可行的。     

视频字幕的自动检测与去除

由于视频中固化的字幕影响了不同语种间视频的交流和处理,为此提出了一种基于CEMA算法和纹理修复技术的自动检测与去除视频内字幕的方法。首先,运用CEMA算法检测出视频中的字幕,然后,结合纹理修复技术,将检测出来的字幕从原图中去除,同时,恢复原图中被字幕所遮挡的背景区域。实验结果表明,该方法能较好地检测和去除视频图像内的字幕。     

视频和图像文本提取方法综述

文本提取在视频和图像中具有重要的应用价值。近年来,大数据时代带来了海量信息检索的迫切需求,大量视频和图像中文本的提取方法涌现出来。回顾了视频和图像中文本提取的算法,从文本提取流程出发,将其分为文本区域检测定位和文本分割两大步骤。在每个步骤中,分析并比较了现有算法的使用范围及相对优缺点,讨论了图像公用数据库,列举了近些年来图像中文本提取的重要应用,指出了当前研究中存在的问题,展望了视频和场景图像文本提取方法的发展趋势。     

Learning the Statistics of People in Images and Video

This paper address the problems of modeling the appearance of humans and distinguishing human appearance from the appearance of general scenes. We seek a model of appearance and motion that is generic in that it accounts for the ways in which people's appearance varies and, at the same time, is specific enough to be useful for tracking people in natural scenes. Given a 3D model of the person projected into an image we model the likelihood of observing various image cues conditioned on the predicted locations and orientations of the limbs. These cues are taken to be steered filter responses corresponding to edges, ridges, and motion-compensated temporal differences. Motivated by work on the statistics of natural scenes, the statistics of these filter responses for human limbs are learned from training images containing hand-labeled limb regions. Similarly, the statistics of the filter responses in general scenes are learned to define a background distribution. The likelihood of observing a scene given a predicted pose of a person is computed, for each limb, using the likelihood ratio between the learned foreground (person) and background distributions. Adopting a Bayesian formulation allows cues to be combined in a principled way. Furthermore, the use of learned distributions obviates the need for hand-tuned image noise models and thresholds. The paper provides a detailed analysis of the statistics of how people appear in scenes and provides a connection between work on natural image statistics and the Bayesian tracking of people.     

视频图像中的实时人脸检测方法

给出了一种视频图像中的实时人脸检测方法,该方法综合了彩色视频图像的运动信息和颜色信息,可以快速地对图像中的人脸区域进行定位。算法通过对视频图像序列中每连续3帧图像进行对称差分,提取出运动区域;然后基于肤色聚类模型,再对运动区域进行肤色检测,经过候选人脸验证,最终定位图像中的人脸。实验表明,提出的方法检测速度快,实现简单、高效,满足实时系统的要求。     

Toward Compression of Encrypted Images and Video Sequences

We present a framework for compressing encrypted media, such as images and videos. Encryption masks the source, rendering traditional compression algorithms ineffective. By conceiving of the problem as one of distributed source coding, it has been shown in prior work that encrypted data are as compressible as unencrypted data. However, there are two major challenges to realize these theoretical results. The first is the development of models that capture the underlying statistical structure and are compatible with our framework. The second is that since the source is masked by encryption, the compressor does not know what rate to target. We tackle these issues in this paper. We first develop statistical models for images before extending it to videos, where our techniques really gain traction. As an illustration, we compare our results to a state-of-the-art motion-compensated lossless video encoder that requires unencrypted video input. The latter compresses each unencrypted frame of the “Foreman” test sequence by 59% on average. In comparison, our proof-of-concept implementation, working on encrypted data, compresses the same sequence by 33%. Next, we develop and present an adaptive protocol for universal compression and show that it converges to the entropy rate. Finally, we demonstrate a complete implementation for encrypted video.      

结合深度图像和强度图像的人脸浅浮雕生成算法

针对单纯基于深度图像压缩方法生成的三维浮雕模型容易存在大量噪声,并且易丢失细节的问题,为了生成特征表达准确且具有艺术美感的人脸浅浮雕,提出一种基于三维深度图像和与之对应的二维强度图像的混合人脸浅浮雕的生成算法.首先对三维深度图像的高度场进行压缩生成浮雕基网格;然后提取二维强度图像的灰度信息、梯度信息和显著度信息,并基于显著度信息对基网格的高度场相应地叠加灰度和梯度信息,以保持脸部五官的细节;最后根据显著度信息使用Laplace算子对网格进行光顺处理,使脸部光滑.实验结果表明,与单纯使用基于深度图像压缩方法相比,文中算法生成的浅浮雕模型可以更好地保持五官的细节特征;与基于图像的浮雕生成方法相比,该算法避免了复杂交互操作,可以更好地保持人脸的整体轮廓和形状.     

基于视频图像的交通参数检测

针对交通图像的特点,提出了一种将灰度判断与边缘检测相结合的方法,来获得车流量、车速等相关交通参数。将先锋遗传算法(Vanguard Genetic Algorithms)应用到图像阈值分割中,利用先锋遗传算法寻求全局最优阈值,可以比较准确地将图像中不同灰度的车辆从背景中分离出来。分析了小波变换的特点,应用小波变换对交通图像进行车辆边缘检测。采用帧平均法来处理视频流,可以减小由于摄像头抖动或背景微小变化而产生的误差。实验结果表明,该方法简单、有效,能够获得较满意的检测结果。     

数字虚拟图像与实拍影像的整合

数字虚拟图像与实拍影像的整合是现阶段动态影像创作的一种趋势,它突破了单一的影像形式,融会了两种不同形态的影像语言,不仅为创作者提供了无限的创作空间,并且开拓了新的审美情趣,使影像在创作观念与形式语言表现上发生了许多变化,值得我们思考与研究。本文通过对数字虚拟图像与实拍影像整合的含义、目的与价值三个方面的论述,希望可以引起对新的影像形式语言理论研究的思考,开拓影像形式语言的多元化。     

视频图像中的快速人脸定位方法

本文给出了一种用于视频图像中的快速人脸定位方法。该方法首先利用变化检测法检测出运动目标区域，剔除静止背景的干扰，得到运动目标子图像。然后基于肤色聚类模型对运动目标子图像进行肤色分割，结合椭圆模板检测出人脸区域，投影法定位人脸边界。实验表明，该方法快速准确，能满足实时系统的需要。     

利用视频进行小特征点集的快速人脸建模

文中算法利用视频重建人脸，给出相机定标、特征点提取及跟踪、运动估计、3D重建、纹理映射技术；该算法基于小特征点集进行，具有很强的实时性、自动性、鲁棒性，相关技术有一定特色和学术参考价值。     

一种新型非线性视频图像交叉加密算法

为了改善视频图像加密的安全和实时性能,提出了一种基于非线性的视频图像交叉加密算法。针对混沌系统中存在有限精度效应的影响和线性反馈移位寄存器生成序列的线性复杂度非常有限并且容易破解的问题,结合混沌技术和线性反馈移位寄存器技术,构造了非线性交叉加密算法的理论数学模型,阐述了算法的设计原理,介绍了实现的相关代码。通过与其他传统算法的理论分析和硬件实现的性能比较,结果表明：产生的视频图像密码序列在较低迭代次数来得到周期极大的非线性序列密码,其总体性能比可达到0．68,因此为高性能视频图像的传输提供了一种新的安全实时加密方法。     

基于视频图像的人眼动态追踪

论文基于视频图像对人眼的瞳孔中心进行实时定位,根据该点的位置变化从而判断人眼的注视方向即人眼的动态追踪.首先对视频图像进行灰度化处理,将视频图像从RGB色彩空间转换到灰度空间.然后对灰度图像进行高斯滤波处理,去除图像的噪点.接着对图像进行二值化处理找到人眼区域并检测出瞳孔轮廓,最后进行瞳孔中心定位实现人眼动态追踪.实验...